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01,0002,0003,0004,0005,0006,0007,000 |
Source Domain |
Target Domain (b) |
05 0 100 150 |
(c) (a) |
Figu Re 4. (a) A false color composition of a portion of the hyperspectral data set. (b) The mean spectral signature of the classes on the |
source domain. (c) The mean spectral signature of the classes on the target domain. |
in tHe Remote SenSing |
LiteRatuRe, SeVeRaL |
tecHniQueS Ha Ve Been |
pReSente D to S oLVe tHe |
tRanSFeR-LeaRning |
pRoBLem iRReSpecti Ve |
oF tHe cau Se oF tHe Data |
Set SHiFt Between |
SouRce an D taRget |
DomainS. |
Authorized licensed use limited to: ASU Library. Downloaded on March 08,2024 at 03:13:37 UTC from IEEE Xplore. Restrictions apply. |
june 2016 ieee Geoscience and remote sensin G ma Gazine 47 |
In the following, we report the experimental results ob - |
tained on a hyperspectral image acquired by the Hyperion |
sensor of the Earth Observation 1 satellite in an area of the |
Okavango Delta, Botswana [ 32] (see Figure 4 ). For more in - |
formation about the experimental setting and the obtained |
results, see [ 30]. The labeled reference samples were col - |
lected on two spatially disjoint areas with slightly different |
characteristics, thus representing two different domains. |
The samples taken on the first area, which was considered |
as the source domain, were partitioned into a training set |
Ts and a test set Ts by random sampling. Samples taken on |
the second area (i.e., the target domain) were used to derive |
a training set Tt and test set Tt according to the same pro - |
cedure. The estimated Pareto front for the selection of six features is reported in Figure 5 . |
Each point on the two graphs corresponds to a different |
selected feature subset |
F, i.e, a feature set minimizing (1 ). |
In Figure 5(a), the color of the points indicates the overall accuracy (OA) obtained on the source-domain test set |
Ts |
using an SVM classifier trained using Ts (according to the |
reported color scale bar). In Figure 5(b), the color indicates the OA obtained by the same SVM classifier on the target- |
domain test set |
.Tt |
The results show that the solutions with higher relevance |
D result in better classification accuracies on the source do - |
main. However, relevance alone is not sufficient for select - |
ing features that are stable for the classification on the target domain. We observe that the most accurate solutions on the target domain |
Tt are those that exhibit a good tradeoff be - |
tween the relevance and invariance terms, which confirms |
the importance of the invariance term and shows that the |
P measure is able to capture the information of feature |
stability. To select the subset of features that leads to good generalization capabilities on different domains, tradeoff |
solutions between the two competing objectives should be |
identified. The selected subset of features results in an OA |
of 91% on the source domain and 80.7% on the target. The |
set of features selected according to the optimization of |
D |
results in an OA of 92.7% on the source but only 64.4% on the target, showing that the features selected by accounting for the data set shift between the domains can significantly improve the generalization capability on the target domain. |
ADAPTING DATA DISTRIBUTIONS |
The second family reviewed considers DA methods that |
aim to adapt the representation of the original data, regard - |
less of the model that will process them afterward. A review |
of the methods proposed in computer vision and machine |
learning can be found in [ 16]. Here, we will focus on the ap - |
proaches proposed in the remote sensing literature. This type |
of adaptation is often done by relative normalization methods, |
i.e., methods that do not provide physical units as an output |
but that instead provide similarly distributed digital numbers. |
Their aim is to make the data |
distri butions more similar |
across the domains to train a |
single model that can simul - |
taneously classify the source |
and target domains. |
In general, a data represen - |
tation transformation with the aim of making data sources more similar should have the |
following desirable properties: |
◗The method should be able |
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