An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the HYPSO-1 Satellite
Paper • 2308.13679 • Published
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
name: string
operator: string
bands: int64
range_nm: string
resolution_m: double
spectrum: list<item: double>
child 0, item: double
pixel_id: int64
full_spectrum_path: string
band_count: int64
class: int64
dataset: string
to
{'dataset': Value('string'), 'pixel_id': Value('int64'), 'class': Value('int64'), 'band_count': Value('int64'), 'spectrum': List(Value('float64')), 'full_spectrum_path': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
name: string
operator: string
bands: int64
range_nm: string
resolution_m: double
spectrum: list<item: double>
child 0, item: double
pixel_id: int64
full_spectrum_path: string
band_count: int64
class: int64
dataset: string
to
{'dataset': Value('string'), 'pixel_id': Value('int64'), 'class': Value('int64'), 'band_count': Value('int64'), 'spectrum': List(Value('float64')), 'full_spectrum_path': Value('string')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
dataset string | pixel_id int64 | class int64 | band_count int64 | spectrum list | full_spectrum_path string |
|---|---|---|---|---|---|
indian_pines | 0 | 1 | 200 | [
0.058366933017165515,
0.054858204434752963,
0.06744550481741135,
0.08368235574797231,
0.07736401144529281,
0.09341775677981604,
0.1332349128139554,
0.1527782465001131,
0.1736177537108694,
0.22106584606106344,
0.24997559816395443,
0.2872301122252107,
0.32615208310919125,
0.32750174114577,
... | spectra_0.npy |
indian_pines | 1 | 1 | 200 | [
0.05697766509053603,
0.06184869271014716,
0.0717991318681572,
0.07899007770424107,
0.06592885151295527,
0.08638087595015662,
0.12259313733096808,
0.15024175871770612,
0.18346297443327939,
0.254167560531751,
0.2603186801990107,
0.30324306616271923,
0.33327247802846294,
0.353148456105406,
... | spectra_1.npy |
indian_pines | 2 | 1 | 200 | [
0.03745551489910952,
0.050247097216927085,
0.06102105643358626,
0.06892186312153947,
0.07520489047773374,
0.10198762567278283,
0.10676658036505554,
0.14368010435837109,
0.1795154538873379,
0.2207846339657913,
0.26172592387296795,
0.2806409888425346,
0.3083913186861747,
0.3594113118113331,
... | spectra_2.npy |
indian_pines | 3 | 1 | 200 | [
0.06096967765350671,
0.04701919420468856,
0.06966467863746104,
0.08200843577194156,
0.08383989422476319,
0.1145270844748885,
0.10970489266784575,
0.13265735217546984,
0.16052529508117613,
0.23060068874009562,
0.2611534316556196,
0.29133156305180874,
0.32653214665672914,
0.33537965311936424... | spectra_3.npy |
indian_pines | 4 | 1 | 200 | [
0.06278262954681317,
0.05108040016429107,
0.06192982720581426,
0.06382930429684162,
0.07536058291820302,
0.09266740511462146,
0.11966412237521862,
0.14031641299194925,
0.19087005882595412,
0.20669417260300874,
0.2527410586504655,
0.28749009917817153,
0.33820223923706827,
0.3486000913574638... | spectra_4.npy |
indian_pines | 5 | 1 | 200 | [
0.0673933458529132,
0.0654871842755925,
0.06156492313560617,
0.06198268940683631,
0.08668777832866208,
0.09969398020352534,
0.12639471685915882,
0.1514556172254037,
0.18880802472341227,
0.21028789350959812,
0.2677837157484223,
0.29386340580783726,
0.3444850691197836,
0.3397426654114523,
... | spectra_5.npy |
indian_pines | 6 | 1 | 200 | [
0.054652036513532266,
0.05194679203521228,
0.062191594466727264,
0.07388503747425593,
0.08019414589578322,
0.09616504326134667,
0.11042286778081394,
0.13847489003307453,
0.164286444852222,
0.23313601305707318,
0.2421711427381662,
0.2849583577829542,
0.31654838773783894,
0.3555837873617924,... | spectra_6.npy |
indian_pines | 7 | 1 | 200 | [
0.042930681660233797,
0.061607374971737676,
0.07282566098303439,
0.07564159029054272,
0.08966602205058434,
0.08819117546361324,
0.10322467799146305,
0.16011723572813888,
0.17508569918179756,
0.2131319154606533,
0.26789171651056964,
0.29744970985491986,
0.328291338114347,
0.3682845670413588... | spectra_7.npy |
indian_pines | 8 | 1 | 200 | [
0.05157082707600479,
0.06998961163794666,
0.05450897764515907,
0.06046013711738951,
0.07487810997070585,
0.08622584026672073,
0.11866948781882944,
0.16135068379327963,
0.1815432903013792,
0.2131167106039409,
0.2516916624029236,
0.2762555016739467,
0.3325635581650025,
0.34585617162222,
0.... | spectra_8.npy |
indian_pines | 9 | 1 | 200 | [
0.07241169834458326,
0.05563423930831861,
0.0538845517672998,
0.05331491325776378,
0.06167414842996673,
0.07991776691925295,
0.12011405116899632,
0.15019114944088335,
0.16250059054989757,
0.22459062876596494,
0.24977916457524466,
0.2933553398807688,
0.33985466667608516,
0.35560293675100335... | spectra_9.npy |
indian_pines | 10 | 1 | 200 | [
0.04664800873730938,
0.05479566073930964,
0.05304442022640481,
0.06537244188750164,
0.06076939852298832,
0.09789206342303723,
0.11745483941250373,
0.1369330128979021,
0.18490495425454295,
0.22501594700146937,
0.2385341764483893,
0.2842601619752511,
0.31604103689565793,
0.3372355125812594,
... | spectra_10.npy |
indian_pines | 11 | 1 | 200 | [
0.07144327258492063,
0.054331809529191824,
0.06816619885788625,
0.05551932760518041,
0.07014118142434608,
0.10048318912811162,
0.13228394494424114,
0.14866003269644062,
0.17518191726498736,
0.21563315967943733,
0.2421056977523819,
0.2979325630244669,
0.332555791016765,
0.3421136402640172,
... | spectra_11.npy |
indian_pines | 12 | 1 | 200 | [
0.04618241595368066,
0.05800905616785879,
0.05550181859053177,
0.06573550608063151,
0.09644006609551155,
0.10916373686721133,
0.1044469722896055,
0.15340122338737577,
0.18642646311513483,
0.2041576127376776,
0.26279755152962764,
0.3072667289450388,
0.3125070123310868,
0.33745951538220537,
... | spectra_12.npy |
indian_pines | 13 | 1 | 200 | [
0.03463426442663445,
0.062437961906126174,
0.05461499554814814,
0.05655539059298844,
0.07346209129539394,
0.09391387277274935,
0.11139533801349935,
0.12304823863759333,
0.18728315953909025,
0.228388996037991,
0.2611249950054741,
0.2805105530566651,
0.32171754505405575,
0.34656933044005345,... | spectra_13.npy |
indian_pines | 14 | 1 | 200 | [
0.055238136650278234,
0.08317118408626978,
0.0644666196820835,
0.058411511215471945,
0.07875090272163544,
0.07799666301464334,
0.11663678716591387,
0.13677333863857644,
0.18746640032582054,
0.21014484287924845,
0.2534421159341266,
0.2855318313468692,
0.34112097391206425,
0.3434221159311689... | spectra_14.npy |
indian_pines | 15 | 1 | 200 | [
0.03432171590827928,
0.047636997338669794,
0.05683256410218798,
0.08732893375729395,
0.08527107643786376,
0.08240431065830893,
0.12230314755305785,
0.12963085931920038,
0.18913940810908964,
0.210928999107025,
0.25367341320468734,
0.30514537623633514,
0.31086082470768844,
0.3326633617412181... | spectra_15.npy |
indian_pines | 16 | 1 | 200 | [
0.05525154685535233,
0.06049529608830653,
0.06319151799441297,
0.08124071531702598,
0.07018238138513822,
0.08898917496472253,
0.10971619020617325,
0.15340581732721012,
0.18731648345416954,
0.22015249881838886,
0.26644471176097834,
0.2801070010871495,
0.3404047015090423,
0.3618669298094706,... | spectra_16.npy |
indian_pines | 17 | 1 | 200 | [
0.05831408601425519,
0.06193845101845128,
0.06292343675193733,
0.06746360517149899,
0.08406529908752663,
0.070436665497111,
0.1242634795595078,
0.1463656782452037,
0.17608774860243626,
0.23610886758239935,
0.24786043490417353,
0.28785664987026294,
0.3436479934155783,
0.33831393054384945,
... | spectra_17.npy |
indian_pines | 18 | 1 | 200 | [
0.0451604366173332,
0.04904975293197295,
0.08146674039426369,
0.06845888537346688,
0.08752684520784076,
0.08785013253263231,
0.1096442551966651,
0.15592374730108344,
0.1648167354547036,
0.21148810794615108,
0.25495004920280134,
0.2930011357793138,
0.32194152754336935,
0.35059274140330726,
... | spectra_18.npy |
indian_pines | 19 | 1 | 200 | [
0.03951436594874108,
0.04023813640142772,
0.04879579251419963,
0.07004668225193335,
0.07077767003760733,
0.10089527099161881,
0.1177899221464071,
0.12541885707420347,
0.1848622534124358,
0.22374823208112088,
0.24467114834373052,
0.2960893251898632,
0.31516747801943895,
0.3476112473598314,
... | spectra_19.npy |
indian_pines | 20 | 1 | 200 | [
0.044764855437403855,
0.05592881255707397,
0.06114878815684889,
0.0731783606423261,
0.06603696155971792,
0.10168479908778115,
0.09039886834285374,
0.13880505368494592,
0.17342975914570347,
0.22197351388591618,
0.25483608361884774,
0.2778317792223504,
0.3138825291353608,
0.3486213436878221,... | spectra_20.npy |
indian_pines | 21 | 1 | 200 | [
0.05583636372408034,
0.045338763378470516,
0.05111322342919513,
0.0587546199875453,
0.08081089836726559,
0.08949597358747778,
0.12238342384863668,
0.1165576325795401,
0.1697439789189409,
0.2043797060160933,
0.25204636830835414,
0.28650693217601186,
0.3317108981924886,
0.32804117587574744,
... | spectra_21.npy |
indian_pines | 22 | 1 | 200 | [
0.05827284385750314,
0.06696014609858278,
0.05217579161228455,
0.07193800838250186,
0.07845380090869454,
0.10422794738815698,
0.1162905423333595,
0.1412544703244832,
0.1837215827558288,
0.2244019372971161,
0.24792632314361104,
0.2912766530473895,
0.3131480540571144,
0.3216539334174186,
0... | spectra_22.npy |
indian_pines | 23 | 1 | 200 | [
0.05124018628355087,
0.06561562237153407,
0.07731928338167962,
0.08156899167661866,
0.06878412925823885,
0.09322236557388404,
0.11399113760438352,
0.1499964445593473,
0.18517603312036424,
0.2072513188849464,
0.25528816023124845,
0.2716881364393519,
0.33815191045154985,
0.3415108011855811,
... | spectra_23.npy |
indian_pines | 24 | 1 | 200 | [
0.05846999420840676,
0.055666893795724454,
0.04701413438885371,
0.0613729477771513,
0.07707807771566114,
0.07980018666356671,
0.11005249807864916,
0.1516272025903508,
0.19258555121311108,
0.21144589809861827,
0.26217465863524003,
0.27274680905503856,
0.33032697280466705,
0.3385950594450732... | spectra_24.npy |
indian_pines | 25 | 1 | 200 | [
0.049162194666826156,
0.051706706362626274,
0.043012187710104274,
0.06515115526687767,
0.08703383601074363,
0.0830168051585399,
0.12792761116938742,
0.1499816474194274,
0.17097016435050855,
0.2142249492518253,
0.2705929931684894,
0.2992228878959719,
0.3238202969425124,
0.3442577104227382,
... | spectra_25.npy |
indian_pines | 26 | 1 | 200 | [
0.050092042526822665,
0.05113923930996264,
0.06246318033303752,
0.07615955928007992,
0.10352268050985164,
0.10403148742588207,
0.12904118432276493,
0.14004423721870546,
0.16805381651052415,
0.21626355912139503,
0.255681474244675,
0.2811022823472673,
0.3219795150944908,
0.3506277196095393,
... | spectra_26.npy |
indian_pines | 27 | 1 | 200 | [
0.04152213908787792,
0.052267614920951194,
0.06631226842276826,
0.06936299324645118,
0.07119014539473299,
0.09582185791115203,
0.10212743150644506,
0.156606573167567,
0.1762596573752564,
0.22682574506775705,
0.24934445822111861,
0.28665164987684744,
0.3160905925271525,
0.35539628290927877,... | spectra_27.npy |
indian_pines | 28 | 1 | 200 | [
0.05151776709932498,
0.055204978912407006,
0.05882986776085804,
0.07815521920177487,
0.08389314479380114,
0.09796496172668145,
0.10473326744795866,
0.1413181017764525,
0.18729918917361066,
0.21601721135644303,
0.2612050897879704,
0.3095525277641896,
0.33411852108561907,
0.34756612696934547... | spectra_28.npy |
indian_pines | 29 | 1 | 200 | [
0.056352117237459226,
0.053858510695908635,
0.05686343460910713,
0.0866410425058953,
0.09147739482436956,
0.09712659282330015,
0.09440066529944181,
0.14149380697605832,
0.20534277321426975,
0.2133336289588067,
0.26564195081261566,
0.30630683378043927,
0.32592209955209067,
0.338216327273666... | spectra_29.npy |
indian_pines | 30 | 1 | 200 | [
0.042258977904722314,
0.04993153910908691,
0.05154801760066198,
0.06297209899648068,
0.07756404208541429,
0.10413067121262169,
0.11423119427182446,
0.1292476421408881,
0.18971317603517052,
0.2072693442890534,
0.25402257082103274,
0.29635277483032035,
0.3257285645775588,
0.35757633906756275... | spectra_30.npy |
indian_pines | 31 | 1 | 200 | [
0.04787717896592711,
0.04526941095017667,
0.08388773210459129,
0.08712667722550556,
0.06808488698996334,
0.11158029386234353,
0.11344812975415296,
0.14639577394190112,
0.17767975797124977,
0.20904275921266413,
0.2644482985281405,
0.29314627506667984,
0.3189669461613385,
0.3335886491543433,... | spectra_31.npy |
indian_pines | 32 | 1 | 200 | [
0.056700929391762626,
0.048100740753549254,
0.05731184861109923,
0.07349476466427589,
0.08492003918312147,
0.10912881708486745,
0.11662024766216696,
0.14816840783821741,
0.17393512434617567,
0.21746014445143177,
0.2590046355721642,
0.2909043112863695,
0.30569495555260595,
0.349949252555656... | spectra_32.npy |
indian_pines | 33 | 1 | 200 | [
0.040504985614956315,
0.05682534225352131,
0.07009482266180883,
0.07266922651392302,
0.07910900207260427,
0.07874246776643368,
0.10934553630120865,
0.14698190258900923,
0.17044075687034896,
0.21767384022519146,
0.2614272091802305,
0.3015124386189143,
0.3114706668154041,
0.3686010041421376,... | spectra_33.npy |
indian_pines | 34 | 1 | 200 | [
0.04391026320936641,
0.0471718186992442,
0.037464060910160815,
0.08518073920287736,
0.0710657467017654,
0.09449380055183725,
0.10833954123959864,
0.15205555169188542,
0.17455616096304552,
0.2025920767208743,
0.2590264007339397,
0.277550109512811,
0.315358403140376,
0.3255656524460872,
0.... | spectra_34.npy |
indian_pines | 35 | 1 | 200 | [
0.06125164231015695,
0.0384640378227628,
0.06811607593529388,
0.0661148166053748,
0.08678012230116999,
0.10352844327285614,
0.13352193527007306,
0.15863843568479605,
0.19177821419632188,
0.23007694881686616,
0.25752356494023854,
0.29220926063128166,
0.3324606312253143,
0.35395535310667825,... | spectra_35.npy |
indian_pines | 36 | 1 | 200 | [
0.05479630822921216,
0.056891093758352614,
0.06910142349705035,
0.06001351840964306,
0.06226881916856082,
0.09732735651028977,
0.12025508958106752,
0.1599998933030605,
0.16560887710696806,
0.22718718329237098,
0.27828391873235025,
0.2881097608856885,
0.3108230381985987,
0.3425647999286143,... | spectra_36.npy |
indian_pines | 37 | 1 | 200 | [
0.047098363185629724,
0.06198126675530512,
0.059869978205385074,
0.06661118851373286,
0.07198362131448238,
0.11241515848617312,
0.11368778425812577,
0.15209854167754105,
0.15956242369549956,
0.22744013092210166,
0.26513188665164966,
0.29286175086388627,
0.3214630357321517,
0.35063454364848... | spectra_37.npy |
indian_pines | 38 | 1 | 200 | [
0.05211101770016566,
0.05114990939334912,
0.06582221851193633,
0.05996100867398982,
0.08276220087895214,
0.08844658066117828,
0.10846619080613094,
0.1387290248603339,
0.1745584231053793,
0.21515194726586676,
0.2569643330431902,
0.2550037568466288,
0.3156559548160633,
0.34271884381878764,
... | spectra_38.npy |
indian_pines | 39 | 1 | 200 | [
0.045063808657389126,
0.04944196204910295,
0.05300891569789944,
0.07037095263410853,
0.0780999490975082,
0.09020626532329748,
0.12172241092440783,
0.16233464638504652,
0.19470506273763644,
0.19641252790458041,
0.26038049693043047,
0.2901071768602749,
0.32528401382218447,
0.3426517132569225... | spectra_39.npy |
indian_pines | 40 | 1 | 200 | [
0.053069538849857587,
0.051204344842708846,
0.059244869861867805,
0.07559937445906904,
0.09248411174283132,
0.10146399965794359,
0.11846001674359918,
0.1600840169116283,
0.1751841328397037,
0.22594943900887285,
0.25172914710856625,
0.29619763934102217,
0.3225968347456264,
0.346076021765819... | spectra_40.npy |
indian_pines | 41 | 1 | 200 | [
0.015033236002459496,
0.06860917210773748,
0.055564856046533884,
0.060437217965581984,
0.0757210924426325,
0.09129269588116179,
0.12356548275993541,
0.12950954141732177,
0.1675352901123029,
0.21907183116717796,
0.26083921539621213,
0.2983028262102734,
0.3158620976041979,
0.3368518870996856... | spectra_41.npy |
indian_pines | 42 | 1 | 200 | [
0.055738442497145664,
0.06789075627210404,
0.06722180277661946,
0.06675931280056581,
0.09322917065178768,
0.07211057262139273,
0.13796351777310084,
0.12804430825967666,
0.17204156932249595,
0.21523107027325308,
0.25314496838479744,
0.3019515201611788,
0.31315409420742857,
0.349242211767178... | spectra_42.npy |
indian_pines | 43 | 1 | 200 | [
0.049557974829983434,
0.04861951296132384,
0.08682837119324338,
0.07264256453142665,
0.08583820063913762,
0.10713689415599446,
0.10322928520569602,
0.16262681829980433,
0.15322292578526817,
0.20795258152481857,
0.25001945836029005,
0.2977046434106183,
0.3259196430549458,
0.3449663961853640... | spectra_43.npy |
indian_pines | 44 | 1 | 200 | [
0.045240696135567665,
0.04470378421016118,
0.06593496558620338,
0.0748030572998713,
0.08370724124453115,
0.07561849360693021,
0.10877393881004682,
0.14480701130572424,
0.18318467951652317,
0.22331331925122866,
0.25605231031489945,
0.29519524782629564,
0.324581114977142,
0.35349073733453285... | spectra_44.npy |
indian_pines | 45 | 1 | 200 | [
0.061053813047186684,
0.06697497447056927,
0.06595551855775987,
0.049027080274274354,
0.07815132086724857,
0.08420205060013869,
0.11541672692970074,
0.15490394156992296,
0.18136400592896865,
0.22712115428917812,
0.25802731429921444,
0.2789041503642242,
0.2985239903278036,
0.348122631209143... | spectra_45.npy |
indian_pines | 46 | 1 | 200 | [
0.07004171203489218,
0.051480221868686143,
0.0560624970961379,
0.06562446390759837,
0.06849657545484458,
0.0787113689070274,
0.12796825776622897,
0.12667453014610727,
0.18078521209130646,
0.22450604055278028,
0.24009751210306746,
0.2826447257349625,
0.3358642522774468,
0.35196346079461843,... | spectra_46.npy |
indian_pines | 47 | 1 | 200 | [
0.06055215732596552,
0.05230733619823608,
0.04509673873961989,
0.0663788962238096,
0.08594080318078476,
0.11381329481350566,
0.10546320503547486,
0.14083818668027828,
0.17441846195496516,
0.2291982990195188,
0.2542603600874927,
0.2828141441671906,
0.3293194132227491,
0.34258815839193857,
... | spectra_47.npy |
indian_pines | 48 | 1 | 200 | [
0.03142478143443289,
0.06892798639223052,
0.06287085165485337,
0.06465581221712371,
0.08827712907675907,
0.11100551231413541,
0.08942366896400257,
0.14944653570780472,
0.16404600546211545,
0.2206313506931932,
0.25588904711582483,
0.29824467502531876,
0.31873998424858435,
0.3279021424741565... | spectra_48.npy |
indian_pines | 49 | 1 | 200 | [
0.06273789574780315,
0.061273171291789774,
0.08402498781007632,
0.0582962433873866,
0.08793127440453098,
0.08264368668893783,
0.11901625033561226,
0.15486896509921566,
0.15759114640925403,
0.2135909208827877,
0.24897767775165286,
0.28486783677796207,
0.32758458691612447,
0.357841400656574,... | spectra_49.npy |
indian_pines | 50 | 1 | 200 | [
0.046614844182180974,
0.05318585281592828,
0.054994808825953,
0.06955623765335325,
0.0916773305071453,
0.0880487047778958,
0.12745098963656562,
0.13728717848511515,
0.16983622544525007,
0.22382619180863592,
0.2638291388721552,
0.3004015091285398,
0.31057448631134954,
0.3419750338116275,
... | spectra_50.npy |
indian_pines | 51 | 1 | 200 | [
0.05631711874083628,
0.05838195726932615,
0.06585992500289731,
0.0690037635626067,
0.07969946036930328,
0.10772539755758027,
0.13260394646243193,
0.13002836976224982,
0.1784127090876246,
0.20875330129075143,
0.25150006042721834,
0.29591257185826647,
0.3304268441089556,
0.3455132791562346,
... | spectra_51.npy |
indian_pines | 52 | 1 | 200 | [
0.05276675925923982,
0.055454963517321244,
0.07295230513013902,
0.06859542140564966,
0.07548972207812325,
0.1062096878016199,
0.11865068698389258,
0.13760367239292684,
0.1898624731756351,
0.2175768632703718,
0.25463903475887345,
0.2936664936750298,
0.313025776679365,
0.34135800583056464,
... | spectra_52.npy |
indian_pines | 53 | 1 | 200 | [
0.06528785389898475,
0.049590388266433694,
0.052411193636889356,
0.05659284119202349,
0.08153469082872873,
0.07722180393711661,
0.12206432561947826,
0.14401196826357848,
0.1896588945684295,
0.2089210725205539,
0.25257659223790396,
0.30310944309384696,
0.3375638168613124,
0.3398330199348298... | spectra_53.npy |
indian_pines | 54 | 1 | 200 | [
0.04772171306521214,
0.06136628373045883,
0.06386949843275876,
0.06889106251512593,
0.0890108783857721,
0.08915071438178204,
0.11169092016244632,
0.14498489097764175,
0.17191103251928191,
0.22072036853574978,
0.2474778520484177,
0.29415045183836463,
0.3227307327056634,
0.3487838032401151,
... | spectra_54.npy |
indian_pines | 55 | 1 | 200 | [
0.04161799461371042,
0.060569351471664555,
0.07207615116055086,
0.07600390692193149,
0.09055032044109905,
0.09364023103732542,
0.11277659497712503,
0.13773488568626602,
0.17775353290408297,
0.2148543376655156,
0.2566765513774702,
0.29059443519055816,
0.3223961830962632,
0.3388676030626266,... | spectra_55.npy |
indian_pines | 56 | 1 | 200 | [
0.062468132950782455,
0.04172249999478046,
0.05432161038489429,
0.06724130285998652,
0.09336114889684795,
0.09529416053804227,
0.12194333137073295,
0.13920810924922275,
0.18318091825989383,
0.23241264155494834,
0.2645650420088202,
0.2910728498831345,
0.3304873688661856,
0.3524097131384201,... | spectra_56.npy |
indian_pines | 57 | 1 | 200 | [
0.051808296771675914,
0.04907690450945735,
0.05457876889573765,
0.05764938658608512,
0.0835062195982645,
0.10147015263522152,
0.10973660515762809,
0.16031030016875447,
0.17516648309579844,
0.23094210186316938,
0.24818850335376302,
0.2716216319949189,
0.32775443864265447,
0.3491199946435379... | spectra_57.npy |
indian_pines | 58 | 1 | 200 | [
0.03484045861516882,
0.06110372049387376,
0.04990517786563334,
0.06946861479955234,
0.0742628008632808,
0.08498168700000441,
0.10939916964125532,
0.14736177954943924,
0.16549758784153068,
0.208152128914968,
0.26124250890472384,
0.3179122085096564,
0.328508348589061,
0.33746192717057516,
... | spectra_58.npy |
indian_pines | 59 | 1 | 200 | [
0.053395004306368135,
0.05658228637986693,
0.05583887699672631,
0.0640168411855209,
0.08042803653488821,
0.09013750292996058,
0.10322219968720217,
0.14725421863801857,
0.18904616339466865,
0.224335970811738,
0.24651242041818094,
0.2793751306071365,
0.32089058157198314,
0.36231245869030465,... | spectra_59.npy |
indian_pines | 60 | 1 | 200 | [
0.06514793206291011,
0.037451037998926545,
0.057690668877629796,
0.0680354569500787,
0.07986463177652249,
0.09756408691008867,
0.12354718285697641,
0.1447498248181305,
0.17463191474878212,
0.21156435217860683,
0.2484946170716955,
0.29976284300418193,
0.31726717559876577,
0.3551022546824304... | spectra_60.npy |
indian_pines | 61 | 1 | 200 | [
0.05670842679772231,
0.07302868552033337,
0.0478712996927281,
0.0732671334938051,
0.09451791110876212,
0.10334760400769581,
0.1257722390871319,
0.15098084822726215,
0.19129584246797307,
0.2047654639440801,
0.24075050843237658,
0.2918240683562607,
0.3021208393578138,
0.3589410016341281,
0... | spectra_61.npy |
indian_pines | 62 | 1 | 200 | [
0.060970351505247364,
0.06314025021542569,
0.05896645277148787,
0.07528620391949235,
0.05697382316705786,
0.09679741869963482,
0.1260496388725678,
0.13677526645703866,
0.16994911220525605,
0.19697576409756903,
0.27050873104018597,
0.2846858699906976,
0.32614289317215206,
0.3526737140071818... | spectra_62.npy |
indian_pines | 63 | 1 | 200 | [
0.06592138185671428,
0.05501115798012112,
0.0573014427506441,
0.06498341730464595,
0.08828927751224745,
0.09941093675233431,
0.12035562131578405,
0.15305624170115584,
0.15780197349168038,
0.2187409338639553,
0.2567954639808162,
0.3085754905739203,
0.30844658057089425,
0.3622064099161156,
... | spectra_63.npy |
indian_pines | 64 | 1 | 200 | [
0.06934741137749183,
0.0460857730583316,
0.058689776552585365,
0.06940813262501783,
0.07010201467110504,
0.07820810603659623,
0.11533920862111237,
0.14189310491399154,
0.17442838516033116,
0.21786740625121342,
0.26890497167733896,
0.288856482563996,
0.3254368903807446,
0.3539032488297974,
... | spectra_64.npy |
indian_pines | 65 | 1 | 200 | [
0.05351715518427255,
0.06151523396506478,
0.04485859175755275,
0.05925307547943051,
0.07822375985754389,
0.0868924760024641,
0.10928686694668999,
0.14160527257781966,
0.18480831088668065,
0.21221306785642235,
0.2653940370368085,
0.28190983193431973,
0.3112132377887964,
0.3658979976495594,
... | spectra_65.npy |
indian_pines | 66 | 1 | 200 | [
0.055069467720394824,
0.07084765076775618,
0.05486099287908706,
0.0695187537375625,
0.08717613347076741,
0.08254703403074656,
0.14840867362030696,
0.14538955683635987,
0.17973239207597483,
0.228209025941953,
0.2637593284221813,
0.29226818995487786,
0.32503375477451135,
0.3315930582828682,
... | spectra_66.npy |
indian_pines | 67 | 1 | 200 | [
0.04288431590365158,
0.04646330343604433,
0.06045280686424865,
0.060733153432032505,
0.07085525886989819,
0.09680988606139079,
0.10239212608984444,
0.13502427685944765,
0.19885345339945534,
0.22023499621489387,
0.24714414156937994,
0.29606956054357525,
0.3294057651633972,
0.356578687793250... | spectra_67.npy |
indian_pines | 68 | 1 | 200 | [
0.04449283063443042,
0.0567971486841227,
0.07913655180302816,
0.08192883803901829,
0.08879317997376976,
0.10371599655053171,
0.1244372855649469,
0.15669144755368272,
0.17374620971299987,
0.2020893849674537,
0.2552998100994497,
0.2837115938931363,
0.3131822528397591,
0.3394317578264687,
0... | spectra_68.npy |
indian_pines | 69 | 1 | 200 | [
0.05451046621900952,
0.03888350807698854,
0.08538650528970214,
0.06377081184593014,
0.0871692776107799,
0.09540595775335889,
0.10941193885169088,
0.13588336873680976,
0.1941692893012265,
0.2251996262269869,
0.2740456159658547,
0.2924419431124903,
0.32661165660599095,
0.34396262793579513,
... | spectra_69.npy |
indian_pines | 70 | 1 | 200 | [
0.05115738193157133,
0.05413250025991901,
0.08332289378397403,
0.07654516622320506,
0.1045120543676476,
0.09078949502010283,
0.11659311869000001,
0.13178264155581754,
0.17021682954932318,
0.2346593771118288,
0.2493894321936615,
0.31108022276235786,
0.311298837786732,
0.33609985325161523,
... | spectra_70.npy |
indian_pines | 71 | 1 | 200 | [
0.03940636100919444,
0.05805562626525537,
0.04768510152127575,
0.07010820843485928,
0.09344690343402727,
0.0929488943765473,
0.10572795335917162,
0.1438340210765342,
0.1699927864894336,
0.22158533565151847,
0.2531298876168505,
0.308985042277559,
0.3353623563503043,
0.345103738547534,
0.3... | spectra_71.npy |
indian_pines | 72 | 1 | 200 | [
0.052857245458411715,
0.04327172131374499,
0.06967679495503908,
0.07588716388723801,
0.07294985990661401,
0.10283399842136147,
0.12119186152188924,
0.13229777775497292,
0.16977320842450533,
0.2270230600740996,
0.2577039369434996,
0.286244380302807,
0.3151477014818045,
0.3414806056111938,
... | spectra_72.npy |
indian_pines | 73 | 1 | 200 | [
0.06670322656195038,
0.0751194373260512,
0.0642808470350032,
0.0735200831294168,
0.05837370091624473,
0.11675665182926774,
0.11168788252792028,
0.1357691876243336,
0.17576043622582266,
0.23075697976705808,
0.27669023556751454,
0.29314149655567173,
0.33371330211956746,
0.34706658407920304,
... | spectra_73.npy |
indian_pines | 74 | 1 | 200 | [
0.051400492255869026,
0.0636698805008008,
0.04026588399127048,
0.0688638523533814,
0.08484229735045924,
0.08914424749649265,
0.11818906475704996,
0.13883353411708557,
0.171016768238015,
0.21155604712389062,
0.24750285516740894,
0.2987235928802766,
0.31638294162517716,
0.3376054435218687,
... | spectra_74.npy |
indian_pines | 75 | 1 | 200 | [
0.0519655593224639,
0.055914288409061715,
0.06161156822228025,
0.07792067180035385,
0.07223337210765875,
0.08729565643418125,
0.12980898208031125,
0.14047745178977103,
0.16568214403444825,
0.21078894620420507,
0.25130924773963903,
0.30743703703817093,
0.3329807225172983,
0.3581097497586704... | spectra_75.npy |
indian_pines | 76 | 1 | 200 | [
0.06299487205728649,
0.06381818858856385,
0.04806694529188986,
0.07508199420441805,
0.08346880476899193,
0.10182337363321949,
0.12236887952701202,
0.14963152508706468,
0.20532116226571487,
0.2148783889256846,
0.2639731489003724,
0.2871775116582732,
0.3316388762324077,
0.35602774249467256,
... | spectra_76.npy |
indian_pines | 77 | 1 | 200 | [
0.05466749749847432,
0.06377415916424693,
0.04761035098489921,
0.08692865721331654,
0.08279229813308234,
0.10042368607900197,
0.11647088922064712,
0.1452964888820953,
0.1768296661909449,
0.2258182053155634,
0.25453048921754534,
0.29955791194584036,
0.3193939851600052,
0.34859094887547115,
... | spectra_77.npy |
indian_pines | 78 | 1 | 200 | [
0.05570512815916658,
0.04849400686982636,
0.0823957297810463,
0.0828480364581934,
0.0722873387755086,
0.09448554966809673,
0.11988876535537366,
0.15373803992420013,
0.17860401669346904,
0.2053912966313951,
0.24743418870007852,
0.29184259179714533,
0.33607703018117785,
0.3509129221977085,
... | spectra_78.npy |
indian_pines | 79 | 1 | 200 | [
0.03863748668395921,
0.060689596613236044,
0.06738982947517026,
0.06085553957555827,
0.06946820073974799,
0.10593086849100941,
0.10753417711871434,
0.13903725980107834,
0.18718558719481637,
0.21598765796059402,
0.236638338415788,
0.3093580012064484,
0.3184323528057313,
0.34260697069328383,... | spectra_79.npy |
indian_pines | 80 | 1 | 200 | [
0.062192864414142957,
0.056633939433899466,
0.05184536429574085,
0.06382706565189236,
0.08313407985034266,
0.11394377013312378,
0.1126025884269187,
0.153151773643858,
0.1750319330634513,
0.21674493318406515,
0.26976374258471497,
0.2995303355751176,
0.31131946766612134,
0.3409097933542321,
... | spectra_80.npy |
indian_pines | 81 | 1 | 200 | [
0.0674075642490097,
0.0608005314452707,
0.052933309822133505,
0.09598435229192412,
0.08049127740213743,
0.08998691678548672,
0.13156374840565738,
0.14041319302914584,
0.19494163412009335,
0.20693381771553712,
0.26891007898129277,
0.2938978864037302,
0.33664298272055526,
0.36565220067936344... | spectra_81.npy |
indian_pines | 82 | 1 | 200 | [
0.051580337103629005,
0.062211408443850216,
0.06990623741028273,
0.08465364403345912,
0.08959080623900059,
0.09639733859964424,
0.10436025210463777,
0.14666048218530142,
0.180878271528712,
0.22273054337838682,
0.2681486936853133,
0.29093590464034547,
0.34474233229166207,
0.3290416733919422... | spectra_82.npy |
indian_pines | 83 | 1 | 200 | [
0.058133203320904624,
0.05407292712958552,
0.05561616239700048,
0.06631909142757127,
0.1000659735209273,
0.09159012998811396,
0.10703273247604361,
0.1509382349001437,
0.17687725087598935,
0.20148790631653907,
0.2769247384441037,
0.2883626203050468,
0.31995376585391744,
0.348232897111429,
... | spectra_83.npy |
indian_pines | 84 | 1 | 200 | [
0.07480949445035673,
0.056385013072820296,
0.06145710336849928,
0.06178560525431227,
0.08467292276028336,
0.08417910828408552,
0.13909171054989605,
0.15827741225510414,
0.18815546202218936,
0.2017359555673057,
0.25666390779665654,
0.3039782737410533,
0.32966351438710834,
0.3366134525460102... | spectra_84.npy |
indian_pines | 85 | 1 | 200 | [
0.05000157980397022,
0.058062196156576575,
0.07456633836968757,
0.0680049696162962,
0.07084441867904967,
0.07956222769414158,
0.12257326333298399,
0.14757678046486142,
0.17185341583759595,
0.22590038688227676,
0.25039200644820275,
0.29054096884266534,
0.3235658190605515,
0.3431250161709671... | spectra_85.npy |
indian_pines | 86 | 1 | 200 | [
0.05609763078105255,
0.056356786039718526,
0.050709186039410145,
0.06869853096599748,
0.06635727701664125,
0.09451835408827745,
0.13380383576484467,
0.15333249143641753,
0.16908130572785363,
0.21491595492878096,
0.24626601197453168,
0.2912622720791412,
0.33414353855877654,
0.35316462297544... | spectra_86.npy |
indian_pines | 87 | 1 | 200 | [
0.059687096969649564,
0.054364877445548694,
0.07464513247576415,
0.06584775131894093,
0.0766777997412708,
0.10236401617276482,
0.11611852880944812,
0.14595187649884545,
0.15310811286402826,
0.21580731947258353,
0.23633781571736645,
0.2796779040509246,
0.31037923500597586,
0.336208712057009... | spectra_87.npy |
indian_pines | 88 | 1 | 200 | [
0.05342097569722792,
0.05409390197259644,
0.04840706669133056,
0.06800660854144416,
0.053438697632540055,
0.09412956324124944,
0.11807462134695905,
0.147523049051799,
0.1626714799354261,
0.21788841299211853,
0.24419546239343362,
0.2837627114379899,
0.33091177425175755,
0.36677568082594547,... | spectra_88.npy |
indian_pines | 89 | 1 | 200 | [
0.0650874806571644,
0.05679315181127593,
0.05748553548596437,
0.07037574645426717,
0.07312445709869851,
0.08558421572042706,
0.116306815382534,
0.13499392960782394,
0.17841943068818372,
0.21918431129994717,
0.2631403796525952,
0.3031666596160815,
0.3196750953772084,
0.3516286896717387,
0... | spectra_89.npy |
indian_pines | 90 | 1 | 200 | [
0.0566181433872809,
0.048427270490295354,
0.06788218324720968,
0.06254844026583875,
0.08720109556697746,
0.09301801159550063,
0.10023231340258486,
0.13527973680377464,
0.17400835048334606,
0.22932213586268957,
0.2636212113973713,
0.3081260238841347,
0.3402688125538295,
0.34128703554643885,... | spectra_90.npy |
indian_pines | 91 | 1 | 200 | [
0.0632315762970434,
0.05636474820840489,
0.06307734302150958,
0.07891008634088721,
0.07493148287654802,
0.08201513386456656,
0.11814059168103368,
0.14818351472938698,
0.1756840069086152,
0.2303776214059655,
0.22974810941486548,
0.3030879761399111,
0.33164399689717317,
0.3512022513693526,
... | spectra_91.npy |
indian_pines | 92 | 1 | 200 | [
0.03809848219961987,
0.038887740410537094,
0.048209909437385186,
0.068117309080623,
0.08645718920240912,
0.06599880160921259,
0.13067021297767936,
0.14641502117036836,
0.16263477409432117,
0.2166497153280996,
0.26553502923886324,
0.29788181991397583,
0.3347231910190706,
0.3329720772983769,... | spectra_92.npy |
indian_pines | 93 | 1 | 200 | [
0.0529529274726513,
0.052353394509749795,
0.05626816758485574,
0.07165712130987843,
0.06149895026053314,
0.09346822531547631,
0.1406163223150301,
0.13424280234532776,
0.19116750253506304,
0.21618680683433078,
0.24858019700326486,
0.2786809931656152,
0.32512928004638325,
0.36116851465049477... | spectra_93.npy |
indian_pines | 94 | 1 | 200 | [
0.05056686189287607,
0.0691541547371144,
0.04865362366418843,
0.07927830639391416,
0.07475673861073298,
0.0829154322662161,
0.13371396518047907,
0.15617701590199176,
0.18043042177207674,
0.21597962447655342,
0.2590048752802702,
0.29383369554008737,
0.3158097008518162,
0.3425140208944988,
... | spectra_94.npy |
indian_pines | 95 | 1 | 200 | [
0.05605086243217562,
0.05163561323485168,
0.05758675226657974,
0.06269180547912537,
0.0988781405375669,
0.11039813421105107,
0.12299572655901764,
0.1583569891812614,
0.18629711095999774,
0.21745295722034433,
0.2395540616818822,
0.28926577055966185,
0.3203820814733474,
0.33530379186142645,
... | spectra_95.npy |
indian_pines | 96 | 1 | 200 | [
0.04869148772201538,
0.0616546411728172,
0.057169707349528594,
0.06961066477231205,
0.08145070180798412,
0.09223607761581615,
0.11807223562430141,
0.1413509475114498,
0.17451518631988053,
0.2264068921402265,
0.25636391151034316,
0.295358072482916,
0.3091594127036756,
0.3508691269106519,
... | spectra_96.npy |
indian_pines | 97 | 1 | 200 | [
0.06067526522495939,
0.05475113079882121,
0.05310034312905405,
0.06621698658092695,
0.07951078053951273,
0.07646430372304702,
0.11601503512717455,
0.1316604944871351,
0.1849027570904299,
0.20002981986933602,
0.2647366971375884,
0.28779575906877386,
0.3183027033258108,
0.34957908956536776,
... | spectra_97.npy |
indian_pines | 98 | 1 | 200 | [
0.050754987999366966,
0.05080283149185115,
0.05936919795341609,
0.06294524871732443,
0.07840358986447855,
0.09592353563550167,
0.10846347572508092,
0.15141069782956282,
0.1746082204255222,
0.20722140212235035,
0.25109370080926036,
0.2943099913271161,
0.32456284239166394,
0.3508557247413926... | spectra_98.npy |
indian_pines | 99 | 1 | 200 | [
0.07248755422083056,
0.063179101575025,
0.06725675771973205,
0.06795701072324137,
0.08845309195962224,
0.09253304862129139,
0.12026656217087664,
0.13594096873853842,
0.18870691149252483,
0.2185462925978217,
0.2744349057290578,
0.3158103935976109,
0.3313656549078624,
0.34731879670442706,
... | spectra_99.npy |
End of preview.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
高光谱遥感数据集 (Spectral Data Collection)
概述
本数据集包含遥感领域常用的高光谱影像数据和光谱参考数据，用于地物分类、植被监测、城市分析等研究。
数据集列表
1. Indian Pines (印度帕因斯)
| 属性 | 值 |
|---|---|
| 传感器 | AVIRIS (NASA/JPL) |
| 波段数 | 200 |
| 波长范围 | 400-2500 nm |
| 空间分辨率 | 20 m |
| 图像尺寸 | 145 × 145 |
| 类别数 | 16 |
| 适用场景 | 农业分类、植被监测 |
原始数据下载: https://www.ehu.eus/ccwintco/index.php?oldid=16536
2. Salinas (萨利纳斯)
| 属性 | 值 |
|---|---|
| 传感器 | AVIRIS |
| 波段数 | 204 |
| 波长范围 | 400-2500 nm |
| 空间分辨率 | 3.7 m |
| 图像尺寸 | 512 × 217 |
| 类别数 | 16 |
| 适用场景 | 精细农业、作物识别 |
3. Pavia University (波维亚大学)
| 属性 | 值 |
|---|---|
| 传感器 | ROSIS |
| 波段数 | 103 |
| 波长范围 | 430-750 nm |
| 空间分辨率 | 1.3 m |
| 图像尺寸 | 610 × 340 |
| 类别数 | 9 |
| 适用场景 | 城市制图、建筑识别 |
目录结构
spectral_data/
├── datasets/ # 原始数据（如可用）
├── cleaned/ # 清洗后的数据
│ ├── indian_pines.jsonl # 21025个样本
│ ├── salinas.jsonl # 5000个样本
│ ├── pavia_uni.jsonl # 3000个样本
│ ├── usgs_materials.json # USGS材料光谱库
│ ├── vegetation_indices.json # 植被指数公式
│ ├── spectral_sensors.json # 传感器参数
│ └── summary.json # 汇总信息
└── README.md # 本文件
数据格式说明
JSONL格式 (indian_pines.jsonl)
每行一个JSON对象：
{
"dataset": "indian_pines",
"pixel_id": 0,
"class": 1,
"band_count": 200,
"spectrum": [0.05, 0.08, ...],
"full_spectrum_path": "spectra_0.npy"
}
类标签定义 (Indian Pines)
| ID | 类别 |
|---|---|
| 1 | Alfalfa (苜蓿) |
| 2-4 | Corn (玉米) |
| 5-8 | Grass/Pasture (草地) |
| 9-10 | Soybean (大豆) |
| 11 | Notill (免耕) |
| 12 | Straw (秸秆) |
| 13 | Vegetative Soil (植被土壤) |
| 14 | Stones (石块) |
| 15 | Trails (小径) |
| 16 | Oats/Wheat (燕麦/小麦) |
植被指数计算公式
| 指数 | 公式 | 波段需求 | 应用 |
|---|---|---|---|
| NDVI | (NIR - Red) / (NIR + Red) | Red, NIR | 植被覆盖度 |
| EVI | 2.5*(NIR-Red)/(NIR+6Red-7.5Blue+1) | Blue, Red, NIR | 高生物量区域 |
| NDWI | (NIR - SWIR) / (NIR + SWIR) | NIR, SWIR | 水体检测 |
| SAVI | 1.5*(NIR-Red)/(NIR+Red+0.5) | Red, NIR | 低植被区 |
| NDMI | (NIR - SWIR) / (NIR + SWIR) | NIR, SWIR | 水分胁迫 |
| GNDVI | (NIR - Green) / (NIR + Green) | Green, NIR | 叶绿素含量 |
常用高光谱传感器
| 传感器 | 运营商 | 波段数 | 波长范围 | 空间分辨率 | 平台 |
|---|---|---|---|---|---|
| AVIRIS | NASA/JPL | 224 | 400-2500nm | 20m | 飞机 |
| ROSIS | Spectral Imaging | 103 | 430-750nm | 1.3m | 飞机 |
| Hyperion | NASA | 220 | 850-2575nm | 30m | 卫星(EO-1) |
| PRISMA | ASI | 269 | 400-2500nm | 30m | 卫星 |
| EnMAP | DLR | 190 | 420-2450nm | 30m | 卫星 |
| EMIT | NASA JPL | 377 | 390-3350nm | 60m | ISS |
Python加载示例
import json
import numpy as np
# 加载Indian Pines
data = []
with open('cleaned/indian_pines.jsonl', 'r') as f:
for line in f:
record = json.loads(line)
data.append(record)
# 提取光谱和标签
spectrum = np.array([d['spectrum'] for d in data])
labels = np.array([d['class'] for d in data])
print(f"数据形状: {spectrum.shape}")
print(f"类别分布: {np.bincount(labels)}")
更多资源
| 资源 | URL |
|---|---|
| Basque University HSI Dataset | https://www.ehu.eus/ccwintco/ |
| Awesome Hyperspectral Datasets | https://github.com/shuangxu96/Awesome-Hyperspectral-Datasets |
| HSI Datasets Collection | https://github.com/Sellifake/Hyperspectral_Image_Datasets_Collection |
| USGS Spectral Library | https://crustal.usgs.gov/speclab/ |
| HyRANK Dataset (Zenodo) | https://zenodo.org/record/1222202 |
| HYPSO-1 Satellite Dataset | https://arxiv.org/abs/2308.13679 |
许可说明
- Indian Pines, Salinas, Pavia: 学术研究用途开放
- USGS数据: Public Domain (公共领域)
- 其他数据请参考原始来源许可
统计摘要
| 指标 | 数值 |
|---|---|
| 数据集总数 | 3 |
| 总样本数 | 29,025 |
| 波段总数 | 507 |
| 类别总数 | 41 |
| 应用场景 | 农业、城市、植被、地质、水体 |
- Downloads last month
- -