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and semantic segmentation presented in Table 2 and Fig.1. |
2. Related work |
2.1. Crop type recognition |
Crop type recognition is a subcategory of land use recog- |
nition which involves assigning one of Kcrop categories |
(classes) at a set of desired locations on a geospatial grid. |
For successfully doing so modelling the temporal patterns |
of growth during a time period of interest has been shown |
to be critical [15, 44]. As a result, model inputs are time- |
series of Tsatellite images of spatial dimensions H×W |
withCchannels, X∈RT×H×W×Crather than single ac- |
quisitions. There has been a significant body of work on |
crop type identification found in the remote sensing liter- |
ature [8, 9, 19, 39, 41, 55]. These works typically involve |
1without any convolution operationsmultiple processing steps and domain expertise to guide |
the extraction of features, e.g. NDVI [25], that can be |
separated into crop types by learnt classifiers. More re- |
cently, Deep Neural Networks (DNN) trained on raw op- |
tical data [22, 26, 29, 46, 47, 62] have been shown to outper- |
form these approaches. At the object level, (SITS classi- |
fication) [24, 40, 48] use 1D data of single-pixel or parcel- |
level aggregated feature timeseries, rather than the full SITS |
record, learning a mapping f:RT×C→RK. Among |
these works, TempCNN [40] employs a simple 1D convo- |
lutional architecture, while [48] use the Transformer archi- |
tecture [64]. DuPLo [24] consists of an ensemble of CNN |
and RNN streams in an effort to exploit the complementar- |
ity of extracted features. Finally, [16] view satellite images |
as un-ordered sets of pixels and calculate feature statistics |
at the parcel level, but, in contrast to previously mentioned |
approaches, their implementation requires knowledge of the |
object geometry. At the pixel level (SITS semantic seg- |
mentation), models learn a mapping f(X)∈RH×W×K. |
For this task, [47] show that convolutional RNN variants |
(CLSTM, CGRU) [54] can automatically extract useful fea- |
tures from raw optical data, including cloudy images, that |
can be linearly separated into classes. The use of CNN |
architectures is explored in [45] who employ two models: |
a UNET2D feature extractor, followed by a CLSTM tem- |
poral model (UNET2D-CLSTM); and a UNET3D fully- |
convolutional model. Both are found to achieve equivalent |
performances. In a similar spirit, [7] use a FPN [31] feature |
extractor, coupled with a CLSTM temporal model (FPN- |
CLSTM). The UNET3Df architecture [60] follows from |
UNET3D but uses a different decoder head more suited to |
contrastive learning. The U-TAE architecture [14] follows |
a different approach, in that it employs the encoder part of |
a UNET2D, applied on parallel on all images, and a sub- |
sequent temporal attention mechanism which collapses the |
temporal feature dimension. These spatial-only features are |
further processed by the decoder part of a UNET2D to ob- |
tain dense predictions. |
2.2. Self-attention in vision |
Convolutional [20, 28, 57] and fully-convolutional net- |
works (FCN) [52, 53] have been the de-facto model of |
choice for vision tasks over the past decade. The convo- |
lution operation extracts translation-equivariant features via |
application of a small square kernel over the spatial extent |
of the learnt representation and grows the feature recep- |
tive field linearly over the depth of the network. In con- |
trast, the self-attention operation, introduced as the main |
building block of the Transformer architecture [64], uses |
self-similarity as a means for feature aggregation and can |
have a global receptive field at every layer. Following the |
adoption of Transformers as the dominant architecture in |
natural language processing tasks [6, 12, 64], several works |
10419 |
have attempted to exploit self-attention in vision architec- |
tures. Because the time complexity of self-attention scales |
quadratically with the size of the input, its naive implemen- |
tation on image data, which typically contain more pixels |
than text segments contain words, would be prohibitive. To |
bypass this issue, early works focused on improving effi- |
ciency by injecting self-attention layers only at specific lo- |
cations within a CNN [5, 67] or by constraining their re- |
ceptive field to a local region [38, 42, 65], however, in prac- |
tice, these designs do not scale well with available hard- |
ware leading to slow throughput rates, large memory re- |
quirements and long training times. Following a different |
approach, the Vision Transformer (ViT) [13], presented in |
further detail in section 3.1, constitutes an effort to apply |
a pure Transformer architecture on image data, by propos- |
ing a simple, yet efficient image tokenization strategy. Sev- |
eral works have drawn inspiration from ViT to develop |
novel attention-based architectures for vision. For image |
recognition, [32, 69] re-introduce some of the inductive bi- |
ases that made CNNs successful in vision, leading to im- |
proved performances without the need for long pre-training |
schedules, [43, 59] employ Transformers for dense predic- |
tion, [11,58,71] for object detection and [3,36,68] for video |
processing. Among these works, our framework is more |
closely related to [3] who use ViT for video processing. |
However, we deviate significantly from their design by in- |
troducing a dictionary of acquisition-time-specific position |
encodings to accommodate an uneven distribution of im- |
ages in time, by employing a different input factorization |
strategy more suitable to SITS, and by being interested in |
both global and dense predictions (section 3.4). Finally, |
we are using a multi-token strategy that allows better han- |
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