dataesr/scientific-paragraphs-categorization huggingface dataset:
There is consequently a tendency to simulate similar, albeit potential (subscript pot ) or unstressed, vegetation fluxes in both SPARSE4 scenarios. The inversion capabilities of the coupled model are quite promising since it is apparent that, even with thermal data measured from different directions, the prevailing component temperatures and fluxes (radiative and energy) at the land surface can satisfactorily be retrieved and reproduced with SPARSE4 estimates being more consistent when compared to SPARSE's retrievals. Nevertheless, the method's inversion and turbulent flux reproduction capabilities over the entire Sunobserver polar grid requires further verification. Since we also see no observable improvement in overall flux retrievals especially when thermal radiation directionality effects (oblique-nadir temperature differences) are negligible, a study that will encompass a wide-ranging combination of Sun-Earth-sensor geometries (including the hotspot region) is necessary.
Global solar radiation partitioning Fraction of incoming diffuse radiation (fd) is estimated following the sky clearness index (K T ) method proposed in  Erbs et al. (1982). Where the atmosphere's optical depth data is available, a modified function according to  Carrer et al. (2013)   where K T = RG/(C s μ s ) is the clearness index, RG = S↓ is the global solar irradiance at BOA, C s = 1368 W m -2 the terrestrial solar radiation constant, μ s is the cosine of the solar zenith angle), and τ opt is the aerosol optical thickness. Consequently, RG = RG dir + RG diff = RG dir + fd⋅RG. Fig. C1  illustrates the estimated compared to observed fraction of diffuse (fd) radiation. The sample data used for the diagram was collected at the Bensalem site in Tunisia. The global incoming radiation was measured using a pyranometer with a hemispherical view while the direct radiation was observed using a narrow-view pyrheliometer.
Acknowledgements This work is supported by the  Centre National d' Études Spatiales (CNES)  and the  Région Occitanie  through a fellowship awarded to the first author as well as the  CNES  funded  TRISHNA  project. The authors would like to thank the  European Commission and ANR  for funding in the frame of the collaborative international consortiums FLUXMED and ALTOS financed under the 2018 Joint call of the  WaterWorks2017 ERA-NET Cofund  and the 2019 PRIMA call respectively. This paper is based on historical data acquired during the scientific activities pertaining to the  TREMA (UCA, IRD , ABHT, ORMVAH, DMN, CNESTEN) and  NAILA (INAT, INRGREF, IRD) International Joint Laboratories. The authors thank the Editors and the anonymous referees for their helpful and constructive comments and suggestions.
Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i. e. , the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic.
3) Future research stems to improve the generalization performance and robustness of DL models while considering the limited availability of reliable training samples. The remainder of this paper is structured as follows. Section II introduces the task of HSI Classification (HSIC) and briefly discusses the HSIC paradigm shift from Traditional (Conventional) Machine Learning to Deep Learning (DL) models, describing HSI data characteristics along with the advantages and limitations of DL that are faced while working with HSI. In section III and IV, we give an overview of different forms of HSI representations and basic machine learning strategies, respectively. Section V describes few commonly used types of layers and reviews recent developments (specifically from 2017 onward) of some intensively utilized DL frameworks for HSIC.
II. HYPERSPECTRAL IMAGE CLASSIFICATION (BACKGROUND AND CHALLENGES) A. Traditional to DL Models The main task of HSIC is to assign a unique label to each pixel vector of HSI cube based on its spectral or spectral-spatial properties. Mathematically, an HSI cube can be represented as X = [x 1 , x 2 , x 3 ,. , x B ] T ∈ R B×(N ×M ) , where B represent total number of spectral bands consisting of (N ×M ) samples per band belonging to Y classes where x i = [x 1,i , x 2,i , x 3,i ,. , x B,i ] T is the i th sample in the HSI cube with class label y i ∈ R Y. The classification problem can be considered as an optimization one, in which a mapping function f c (. ) takes the input data X and after applying some transformations over it, obtains the corresponding label Y, to reduce the gap between obtained output and the actual one  [58].
B. Hyperspectral Data Characteristics and DL Challenges Despite the above-discussed DL potentials, there are still some challenges that need to be considered while applying DL to HSI data. Most of these challenges are related to the characteristics of HSI data i. e. hundreds of contiguous and narrow spectral channels with very high spectral resolution and low spatial resolution throughout the electromagnetic spectrum coupled with limited availability of training data. Although the pixels with rich spectral information are useful for classification purposes, however, the computation of such data takes a lot of time and resources. Furthermore, processing such high-dimensional data is a somewhat complex task due to an increased number of parameters. This is known as the curse of dimensionality which considerably influences the classification performance especially in the case of supervised learning  [100].
Since the size of training data is not adequate/insufficient and/or not reliable (i. e. the training samples may not provide any new information to the model or may have similar patterns/structures) to properly train the classifier which may lead the model to overfit. This is known as the Hughes phenomena  [101]  which occurs when labeled training data is significantly smaller than the number of spectral bands present in the data. Lack of labeled HSI data is a major issue in HSIC as labeling of HSI is a time-consuming and expensive task because it usually requires human experts or investigation of real-time scenarios. In addition to high dimensionality, HSIC suffers from various other artifacts like high intra-class variability due to unconfined variations in reflectance values caused by several environmental interferers and degradation of data caused by instrumental noise while capturing the data  [102].
A. Spectral Representation In such representations, each pixel vector is isolated from other pixels and processed based on spectral signatures only which means the pixel is represented only in spectral space x i ∈ R B. Where B can either be the actual number of spectral channels or just relevant spectral bands extracted after some dimensionality reduction (DR) method. Usually, instead of using original spectral bands, a low dimensional representation of HSI is preferred for data processing in order to avoid redundancy and achieve better class separability, without considerable loss of useful information. Dimensionality Reduction (DR) approaches for spectral HSI representation can either be supervised or unsupervised. Unsupervised techniques transform the high dimensional HSI into a low dimensional space without using the class label information, for example, Principal Component Analysis (PCA) and locally linear embedding  [122].
B. Unsupervised Learning In contrast to the supervised learning approach, unsupervised learning techniques learn from the input data with no explicit labels associated with it. These approaches try to identify the underlying statistical structure of input representations or patterns in the absence of corresponding labels. As there is no ground truth available for the training data so it might be difficult to measure the accuracy of the trained model. However, such learning strategies are useful in the cases where we want to learn the inherent structure of such datasets which have a scarcity of training data. The principal component analysis (PCA) is an unsupervised learning technique that can be used to learn a low-dimensional representation of the input. Similarly, k-means clustering is another unsupervised learning method that groups the input data into homogeneous clusters.
C. Semi-supervised Learning The semi-supervised learning technique is halfway between unsupervised and supervised approaches. It learns from the partially labeled datasets that are a small amount of labeled training data can be utilized to label the rest of the unlabeled data. These techniques effectively utilize all available data instead of just labeled data, therefore, these techniques have gained much popularity among the research community and are being widely used for HSIC  [143] -  [146]. The details of these methods are briefly described in section X.
B. Spatial CNN frameworks for HSIC Spatial CNN models only consider spatial information and to extract the spatial information from HSI data, dimensionality reduction (DR) methods are employed on spectral-domain to lower the dimensionality of original HSI data. For instance,  [168]  used PCA to extract the first PC with refined spatial information and fed it to a fully CNN framework for classification. Similarly,  [169]  trained a spatial-based 2D-CNN with one PC. In  [170] , PCA whitened input data considering three PCs is fed to a random patches network as a 2D-CNN classification framework. The method proposed in  [171]  cropped the patches from 2D input images (i. e. images from the different spectral bands) to train a 2D-CNN architecture that learns the data-adaptive kernels by itself.
VII. AUTOENCODERS (AE) Autoencoder (AE) is a popular symmetrical neural network for HSIC due to its unsupervised feature learning capability. AE itself does not perform a classification task instead it gives a compressed feature representation of high-dimensional HSI data. AE consists of an input layer, one hidden or encoding layer, one reconstruction or decoding layer, and an output layer as shown in Figure  8. AE is trained on input data in such a manner to encode it into a latent representation that is able to reconstruct the input. To learn a compressed feature representation of input data, AE tries to reduce the reconstruction error that is minimizing the difference between the input and the output. Whereas, the Stacked Autoencoder (SAE) is built by stacking multiple layers of AEs in such a way that the output of one layer is served as an input of the subsequent layer.
In DNN based HSIC, the model learns features in a hierarchical manner, where lower layers usually extract generic features, when trained on various images. Therefore, the features learned by these layers can be transferred to learn a new classifier for the target dataset. For instance,  [273]  pertained to a two-branch spectralspatial CNN model with an ample amount of training data from other HSIs and then applied the lower layers of the pre-trained model to the target network for the robust classification of target HSI. To learn the target-specific features, higher layers of the target network are randomly initialized and the whole network is fine-tuned by utilizing limited labeled instances of target HSI. Similarly,  [274]  proposed a suitable method to pre-train and finetune a CNN network to utilize it for the classification of new HSIs.
Similarly, a recent work  [279]  proposed a band selection-based transfer learning approach to pre-train a CNN, which retains the same number of dimensions for various HSIs. Furthermore,  [280]  proposed an unsupervised transfer learning technique to classify completely unknown target HSI and  [281]  demonstrate that the networks trained on natural images can enhance the performance of transfer learning for remote sensing Fig. 14:  A general overview of Active Learning data classification as compared to the networks trained from scratch using smaller HSI data.
E. Active Learning Active Learning (AL) iteratively enhances the predictive performance of a classifier by actively increasing the size of training data, for each training iteration, by utilizing an unlabeled pool of samples. In each iteration, AL enhances the training dataset by actively selecting the most valuable instances from the pool of unlabeled data and an oracle (Human or machine-based) assigns the true class labels to these instances. Finally, these useful instances are added to the existing training dataset and the classifier is retrained on this new training dataset. The process continues until a stopping criterion, that maybe the size of the training dataset, the number of iterations, or the desired accuracy score, is achieved. A general framework of AL is illustrated in Figure  14.
Normally, these samples have been chosen randomly, thus high likely, they may be different for different models if the models are executed at different times. The other issue with most of the literature proposed in recent years is overlapping between training/test samples, i. e. , training/validation samples have been randomly selected (including or excluding the above point) for training and validation however, the entire dataset has been passed at testing phase which leads to a highly biased model (as the training samples have already been seen by the model) and produces high accuracy. Thus, in this work, the training/test samples are though chosen randomly (because all the models have been executed at the same time) however, the above point has been taken seriously and the intersection among these samples remain empty.
A. Experimental Datasets The Indian Pines (IP) dataset was gathered by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)  [305]    The Kennedy Space Center (KSC) dataset was gathered in 1996 by AVIRIS  [305] , with wavelengths ranging from 400 to 2500 nm. The image has 512×614 pixels and 176 spectral bands after removal of some low signal-to-noise ratio (SNR) bands. The KSC dataset comprises 5202 labeled samples, with a total of 13             Table I provides a summary description of each dataset used in the following experiments whereas, Table  II  enlists the numbers of disjoint samples (i. e. , Train/Test samples selected from each class) used for all the experimental results. Please note that the number of train/test (i. e. percentage) samples and geographical locations of train/test samples remain the same for all experimental methods (competing methods).
To some extent, all the aforesaid works are based on Convolutional and Recurrent Network and are evaluated on four benchmark Hyperspectral datasets namely Indian Pines (IP), Pavia University (PU), Kennedy Space Center (KSC) and Houston Scene. This survey only pays attention to the robustness of all these models while considering the small sample size of training data to classify HSI for joint spatial-spectral classification. Here we have enlisted the experimental results with detailed discussion on the obtained results. The obtained accuracies for disjoint training and test samples are shown in Tables III, IV, V and VI and Figures 19,  20 21, and 22. All the results shown in the Tables and Figures are obtained using the 10-cross-validation process to compute the overall, average and kappa (κ) accuracy for comparison purposes.
For instance, let us assume the case of Pavia University results, for this particular case, the work  [183]  has the highest average, overall and kappa (κ) accuracies which are 95. 51%, 93. 95%, and 93. 95% respectively in comparison with the average, overall and kappa (κ) accuracies for other comparative works; 92. 55%, 89. 94%, 89. 9% for  [314] , 89. 43%, 86. 25%, 85. 61% for  [315] , 89. 09%, 89. 5%, 85. 5% for  [313] , 82. 05%, 87. 43%, 76. 89% for  [310] , 80. 38%, 83. 63%, 74. 76% for  [312] , 80. 38%, 84. 06%, 74. 32% for  [311] , 77. 8%, 86. 12%, 72. 06% for  [309] , 77. 07%, 83. 83%, 70. 84% for  [59] , and 72. 23%, 82. 12%, 65. 44% for  [308]. Similar observations can be made of the other experimental datasets. The comparative methods mostly misclassify the samples having similar spatial structures (i.
e. , Meadows and Bare Soil classes for Pavia University dataset) as shown in Table  and Figure. Moreover, the overall accuracy for Grapes Untrained is lower than the other classes due to the reasons mentioned above. In a nutshell, one can say, the higher accuracy can be achieved by increasing the number of labeled training samples. Thus a higher number of labeled training samples can produce better accuracies for all competing methods. In general, the works  [183] ,  [314]  outperformed (i. e. stable results) than the other comparative methods especially in the case of less number of labeled training samples. The above leads to conclude that these works are not sensitive to the number of training samples. Moreover, as the number of training samples increases, the accuracies also increases for these methods however, other methods can work better with a higher number of training samples as compared to these methods.
The similar trend has been observed with a higher number of training samples. Thus, one can conclude that the works  [183]  and  [314]  can solve the limited availability of training samples issues to some extent while considering disjoint train/test samples. Moreover, one can conclude that the AE-based models do not perform well as compared to the other models, although the unsupervised methods do not require the samples to be labeled, if there are no constraints, these methods might learn nothing. Moreover, AE has a symmetric architecture that leads to the    [64] , MLR  [308] , SVM  [309] , MLP  [310] , RNN  [59] , LSTM  [311] , GRU  [312] , CNN-1D  [313] , CNN-2D  [314] , CNN-3D  [315] , HybridSN  [200] , and MorphCNN  [183]  on the disjoint train-test dataset for the PU scene. Class a) 1PC b) GT c) MLR d) SVM e) MLP f ) RNN g) LSTM h) GRU i) CNN1D j) CNN2D k) CNN3D l) MorphCNN (90.
55%) Fig. 19 : Classification Maps obtained by MLR  [308] , SVM  [309] , MLP  [310] , RNN  [59] , LSTM  [311] , GRU  [312] , CNN-1D  [313] , CNN-2D  [314] , CNN-3D  [315]  and MorphCNN  [183]  on the disjoint train-test dataset for the UP scene. TABLE IV: Classification results obtained by RF  [64] , MLR  [308] , SVM  [309] , MLP  [310] , RNN  [59] , LSTM  [311] , GRU  [312] , CNN-1D  [313] , CNN-2D  [314] , CNN-3D  [315] , HybridSN  [200] , and MorphCNN  [183]  on the disjoint train-test dataset for the IP scene. Class a) 1PC b) GT c) MLR d) SVM e) MLP f ) RNN g) LSTM h) GRU i) CNN1D j) CNN2D k) CNN3D l) MorphCNN Fig. 20 : Classification Maps obtained by MLR  [308] , SVM  [309] , MLP  [310] , RNN  [59] , LSTM  [311] , GRU  [312] , CNN-1D  [313] , CNN-2D  [314] , CNN-3D  [315]  and MorphCNN  [183]  on the disjoint train-test dataset for the IP scene.
Moreover, AL and/or SL takes the benefits from the selection of the most important samples for training which enables the model to focus more attention on indistinguishable samples for HSIC. Whereas, FSL benefits from the exploration of the relationship between samples to find a Fig. 21 : Classification Maps obtained by MLR  [308] , SVM  [309] , MLP  [310] , RNN  [59] , LSTM  [311] , GRU  [312] , CNN-1D  [313] , CNN-2D  [314] , CNN-3D  [315]  and MorphCNN  [183]  on the disjoint train-test dataset for the UH scene. TABLE VI: Classification results obtained by RF  [64] , MLR  [308] , SVM  [309] , MLP  [310] , RNN  [59] , LSTM  [311] , GRU  [312] , CNN-1D  [313] , CNN-2D  [314] , CNN-3D  [315] , HybridSN  [200] , and MorphCNN  [183]  on the disjoint train-test dataset for the KSC scene. Class RF  [64]  MLR  [308]  SVM  [309]  MLP  [310]  RNN  [316]  LSTM  [311]  GRU  [312]  CNN-1D  [313]  CNN-2D  [314]  CNN-3D  [315]  HybridSN  [200] a) 1PC b) GT c) MLR d) SVM e) MLP f ) RNN g) LSTM h) GRU i) CNN1D j) CNN2D k) CNN3D l) MorphCNN Fig.
22 : Classification Maps obtained by MLR  [308] , SVM  [309] , MLP  [310] , RNN  [59] , LSTM  [311] , GRU  [312] , CNN-1D  [313] , CNN-2D  [314] , CNN-3D  [315]  and MorphCNN  [183]  on the disjoint train-test dataset for the KSC scene. discriminative decision boundary for HSIC. TL makes good use of similarity among different HSI's to reduce the quantity required for training also reduces the number of trainable parameters while boosting the models' robustness. According to the raw data (i. e. , processing the HSI without extracting/learning the features), DA generates more samples which bring a diversity of samples.
XII. CONCLUSION AND FUTURE DIRECTIONS The rich information contained in HSI data is a captivating factor that constitutes the utilization of HSI technology in real-world applications. Moreover, advances in machine learning methods strengthen the deployment potentials of such technologies. In this work, we surveyed recent developments of Hyperspectral Image Classification (HSIC) using state of the art Deep Neural Networks (for instance, Auto-encoder (AE), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Transfer Learning (TL), Few-shot Learning (FSL), Active/Self Learning (AL/SL), and Data Augmentation (DA)) in a variety of learning schemes (specifically, supervised, semi-supervised and unsupervised learning). In addition, we also analyzed the strategies to overcome the challenges of limited availability of training data like Data Augmentation, Few-shot Learning (FSL), Transfer Learning, and Active Learning, etc.
According to the methodologies discussed above, we select some of the representative works to conduct the experiments on benchmark HSI datasets. Although the current HSIC techniques reflect a rapid and remarkable sophistication of the task, further developments are still required to improve the generalization capabilities. The main issue of deep neural network-based HSIC is the lack of labeled data. HSI data is infamous due to the limited availability of labeled data and deep neural networks demand a sufficiently large amount of labeled training data. Section X discussed some widely used strategies to combat the aforesaid issue but significant improvements are still needed to efficiently utilize limited available training data. One direction to solve this problem could be to explore the integration of various learning strategies discussed in section X to cash in the joint benefits.
The IEEE Geoscience and Remote Sensing Society published the University of Houston (UH) dataset-collected by the Compact Airborne Spectrographic Imager (CASI)-in 2013 [307] , as part of its Data Fusion Contest. It is composed of 340 × 1905 pixels with 144 spectral bands. The spatial resolution of this dataset is 2. 5 MPP with a wavelength ranging from 0. 38 to 1. 05 µm. Finally, the ground truth comprises 15 different land-cover classes. The detailed class description and ground truth maps are presented in Figure18aand disjoint Training/Test sample maps are presented in Figures18b and 18c.
HBM datasets are also important in raising awareness about widespread chemical exposures (and other stressors) and their associated health risks. While the monitoring of external exposure sources, such as indoor and outdoor air, workplace air, drinking water, contaminants and pesticide residues in food, is anchored in legislation, human internal exposure as determined by HBM is generally not, except for occupational settings. Although measuring chemicals in consumer products can give additional information on exposure sources (source monitoring), it only reflects potential external exposure  (Schindler et al. 2014). In contrast, HBM measures chemicals and their metabolites in internal fluids or other human specimens, and as such by definition reflects the internal exposure, i. e. the amount that has been taken up by the body via inhalation, oral and dermal uptake.