Hyperspectral Image Classification Method Research Articles

Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive Filtering

In recent years, different deep learning frameworks were introduced for hyperspectral image (HSI) classification. However, the proposed network models have a higher model complexity, and do not provide high classification accuracy if few-shot learning is used. This paper presents an HSI classification method that combines random patches network (RPNet) and recursive filtering (RF) to obtain informative deep features. The proposed method first convolves image bands with random patches to extract multi-level deep RPNet features. Thereafter, the RPNet feature set is subjected to dimension reduction through principal component analysis (PCA), and the extracted components are filtered using the RF procedure. Finally, the HSI spectral features and the obtained RPNet-RF features are combined to classify the HSI using a support vector machine (SVM) classifier. In order to test the performance of the proposed RPNet-RF method, some experiments were performed on three widely known datasets using a few training samples for each class, and classification results were compared with those obtained by other advanced HSI classification methods adopted for small training samples. The comparison showed that the RPNet-RF classification is characterized by higher values of such evaluation metrics as overall accuracy and Kappa coefficient.

An efficient hyperspectral image classification method for limited training data

AbstractHyperspectral image classification has gained great progress in recent years based on deep learning model and massive training data. However, it is expensive and unpractical to label hyperspectral image data and implement model in constrained environment. To address this problem, this paper proposes an effective ghost module based spectral network for hyperspectral image classification. First, Ghost3D module is adopted to reduce the size of model parameter dramatically by redundant feature maps generation with linear transformation. Then Ghost2D module with channel‐wise attention is used to explore informative spectral feature representation. For large field covering, the non‐local operation is utilized to promote self‐attention. Compared with the state‐of‐the‐art hyperspectral image classification methods, the proposed approach achieves superior performance on three hyperspectral image data sets with fewer sample labelling and less resource consumption.

A Novel Hyperspectral Image Classification Method Using Class-Weighted Domain Adaptation Network

With the development of science and technology, hyperspectral image (HSI) classification has been studied in depth by researchers as one of the important means of human cognition in living environments and the exploration of surface information. Nevertheless, the shortage of labeled samples is a major difficulty in HSI classification. To address this issue, we propose a novel HSI classification method called class-weighted domain adaptation network (CWDAN). First, the convolutional domain adaption network (ConDAN) is designed to align the marginal distributions and second-order statistics, respectively, of both domains via multi-kernel maximum mean discrepancy (MK-MMD) and CORAL loss. Then, the class-weighted MMD (CWMMD) is defined to simultaneously consider the conditional distribution discrepancy and changes of class prior distributions, and the CWMMD-based domain adaptation term is incorporated into the classical broad learning system (BLS) to construct the weighted conditional broad network (WCBN). The WCBN is applied to reduce the conditional distribution discrepancy and class weight bias across domains, while performing breadth expansion on domain-invariant features to further enhance representation ability. In comparison with several existing mainstream methods, CWDAN has excellent classification performance on eight real HSI data pairs when only using labeled source domain samples.

PSSA: PCA-Domain Superpixelwise Singular Spectral Analysis for Unsupervised Hyperspectral Image Classification

Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsupervised HSI classification based on data clustering is highly desired, yet it generally suffers from high computational cost and low classification accuracy, especially in large datasets. To tackle these challenges, a novel unsupervised spatial-spectral HSI classification method is proposed. By combining the entropy rate superpixel segmentation (ERS), superpixel-based principal component analysis (PCA), and PCA-domain 2D singular spectral analysis (SSA), both the efficacy and efficiency of feature extraction are improved, followed by the anchor-based graph clustering (AGC) for effective classification. Experiments on three publicly available and five self-collected aerial HSI datasets have fully demonstrated the efficacy of the proposed PCA-domain superpixelwise SSA (PSSA) method, with a gain of 15–20% in terms of the overall accuracy, in comparison to a few state-of-the-art methods. In addition, as an extra outcome, the HSI dataset we acquired is provided freely online.

EBM3GP: A novel evolutionary bi-objective genetic programming for dimensionality reduction in classification of hyperspectral data

Dimensionality reduction (DR) is vital in hyperspectral image (HSI) classification, and feature extraction and band selection methods have been demonstrated to be effective at accomplishing it. However, both types of methods can only obtain single-level features from HSI spectra, which suffers from insufficient useful information and makes accurate classification of the high dimensionality of HSI pixels challenging. To overcome the shortcomings, this study proposes a novel Evolutionary Bi-objective Genetic Programming-based unsupervised DR approach named EBM3GP for obtaining low-level features (bands) and high-level features from raw HSI spectra simultaneously. In EBM3GP, multi-dimensional trees are used to encode the raw spectrum to low- and high-level features; two mutually restrictive measures are applied to evaluate the amount of information and the redundancy contained in trees (evaluation does not utilize the HSI pixel’s label); multiple trees are optimized through population evolution by combining three types of crossover operators, two types of mutation operators and nondominated sorting method; a Pareto optimal individual is finally output and decoded as a DR strategy. Then, this study applies Random Forest, least squares Support Vector Machine, and Extreme Learning Machine for classification to evaluate the efficacy of the DR strategy. Based on three HSIs (including Indian Pines, Salinas, and Pavia University datasets), EBM3GP is demonstrated to outperform five popular DR methods for HSI classification. Moreover, the EBM3GP is not sensitive to data size and thus is available for DR of small-size HSI datasets.

Tri-CNN: A Three Branch Model for Hyperspectral Image Classification

Hyperspectral Image (HSI) classification methods that use Deep Learning (DL) have proven to be effective in recent years. In particular, Convolutional Neural Networks (CNNs) have demonstrated extremely powerful performance in such tasks. However, the lack of training samples is one of the main contributors to low classification performance. Traditional CNN-based techniques under-utilize the inter-band correlations of HSI because they primarily use 2D-CNNs for feature extraction. Contrariwise, 3D-CNNs extract both spectral and spatial information using the same operation. While this overcomes the limitation of 2D-CNNs, it may lead to insufficient extraction of features. In order to overcome this issue, we propose an HSI classification approach named Tri-CNN which is based on a multi-scale 3D-CNN and three-branch feature fusion. We first extract HSI features using 3D-CNN at various scales. The three different features are then flattened and concatenated. To obtain the classification results, the fused features then traverse a number of fully connected layers and eventually a softmax layer. Experimental results are conducted on three datasets, Pavia University (PU), Salinas scene (SA) and GulfPort (GP) datasets, respectively. Classification results indicate that our proposed methodology shows remarkable performance in terms of the Overall Accuracy (OA), Average Accuracy (AA), and Kappa metrics when compared against existing methods.

Hyperspectral Image Classification Based on the Gabor Feature with Correlation Information

Gabor filter is widely used to extract spatial texture features of hyperspectral images (HSI) for HSI classification; however, a single Gabor filter cannot obtain the complete image features. In the paper, we propose an HSI classification method that combines the Gabor filter (GF) and domain-transformation standard convolution (DTNC) filter. First, we use the Gabor filter to extract spatial texture features from the first two principal components of the dimensionality-reduction HSI with PCA. Second, we use the DTNC filter to extract spatial correlation features from HSI in all bands. Finally, the Large Margin Distribution Machine (LDM) uses the linear fusion of the two kinds of spatial features to classify HSI. The experimental results show that the classification accuracy of Indian Pines, Pavia University, and Kennedy Space Center data sets is 96.64, 98.23, and 98.95% with only 4, 3, and 6% training samples, respectively; and these accuracies are 2–20% higher than the other tested methods. Compared with the hyperspectral information based on SVM, EPF, IFRF, PCA-EPFs, LDM-FL, and GFDN method, the proposed method, GFDTNCLDM, significantly improves the accuracy of HSI classification.

Pyramidal Dilation Attention Convolutional Network With Active and Self-Paced Learning for Hyperspectral Image Classification

In recent years, deep neural networks have been widely used for hyperspectral image (HSI) classification and have shown excellent performance using numerous labeled samples. The acquisition of HSI labels is usually based on the field investigation, which is expensive and time consuming. Hence, the available labels are usually limited, which affects the efficiency of deep HSI classification methods. To improve the classification performance while reducing the labeling cost, this article proposes a semisupervised deep learning (DL) method for HSI classification, named pyramidal dilation attention convolutional network with active and self-paced learning (PDAC-ASPL), which integrates active learning (AL), self-paced learning (SPL), and DL into a unified framework. First, a densely connected pyramidal dilation attention convolutional network is trained with a limited number of labeled samples. Then, the most informative samples from the unlabeled set are selected by AL and queried real labels, and the highest confidence samples with corresponding pseudo labels are extracted by SPL. Finally, the samples from AL and SPL are added to the training set to retrain the network. Compared with some DL- and AL-based HSI classification methods, our PDAC-ASPL achieves better performance on four HSI datasets.

DCN-T: Dual Context Network With Transformer for Hyperspectral Image Classification.

Hyperspectral image (HSI) classification is challenging due to spatial variability caused by complex imaging conditions. Prior methods suffer from limited representation ability, as they train specially designed networks from scratch on limited annotated data. We propose a tri-spectral image generation pipeline that transforms HSI into high-quality tri-spectral images, enabling the use of off-the-shelf ImageNet pretrained backbone networks for feature extraction. Motivated by the observation that there are many homogeneous areas with distinguished semantic and geometric properties in HSIs, which can be used to extract useful contexts, we propose an end-to-end segmentation network named DCN-T. It adopts transformers to effectively encode regional adaptation and global aggregation spatial contexts within and between the homogeneous areas discovered by similarity-based clustering. To fully exploit the rich spectrums of the HSI, we adopt an ensemble approach where all segmentation results of the tri-spectral images are integrated into the final prediction through a voting scheme. Extensive experiments on three public benchmarks show that our proposed method outperforms state-of-the-art methods for HSI classification. The code will be released at https://github.com/DotWang/DCN-T.

Classification via Structure-Preserved Hypergraph Convolution Network for Hyperspectral Image

Graph convolutional network (GCN) as a combination of deep learning and graph learning has gained increasing attention in hyperspectral image (HSI) classification. However, most GCN methods consider the simple point-to-point structure between two pixels rather than the high-order structure of multiple pixels, which is contradict with the real feature distribution of ground object. And the nonlinear property of HSI also brings challenge for precise structural representation in GCN. To tackle these problems, this work proposes a structure preserved hypergraph convolution network (SPHGCN). It first builds a multiple neighborhood reconstruction (MNR) model to reveal the essential resemblance of multiple pixels in nonlinear spectral feature space. With the high-order structure, SPHGCN designs the hypergraph convolution operation for irregular feature aggregation among similar pixels from different regions, which achieves more discriminative features from multiple pixel nodes. Meanwhile, a structure preservation layer is built to optimize the distribution of convolutional features under the guidance of high-order structure. Moreover, SPHGCN integrates local regular convolution and irregular hypergraph convolution to learn the structured semantic feature of HSI. This strategy breaks the boundary restriction in traditional convolution and aggregates semantic feature across different image patches. Experiments on three HSI data sets indicate that SPHGCN outperforms a few state-of-the-art methods for HSI classification.

PCA-Domain Fused Singular Spectral Analysis for Fast and Noise-Robust Spectral–Spatial Feature Mining in Hyperspectral Classification

The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral domain and spatial domain feature extraction in hyperspectral images (HSI). However, PCA itself suffers from low efficacy if no spatial information is combined, whilst 2DSSA can extract the spatial information yet has a high computing complexity. As a result, we propose in this paper a PCA domain 2DSSA approach for spectral-spatial feature mining in HSI. Specifically, PCA and its variation, folded-PCA are utilized to fuse with the 2DSSA, as folded-PCA can extract both global and local spectral features. By applying 2DSSA only on a small number of PCA components, the overall computational complexity has been significantly reduced whilst preserving the discrimination ability of the features. In addition, with the effective fusion of spectral and spatial features, the proposed approach can work well on the uncorrected dataset without removing the noisy and water absorption bands, even under a small number of training samples. Experiments on two publicly available datasets have fully demonstrated the superiority of the proposed approach, in comparison to several state-of-the-art HSI classification methods and deep-learning models.

Spectral–Spatial MLP-Like Network With Reciprocal Points Learning for Open-Set Hyperspectral Image Classification

In recent years, deep-learning-based hyperspectral image (HSI) classification methods have achieved significant development and gradually become widely applied. The existing advanced methods can achieve near-saturation performance with sufficient labels in a closed-set environment (CSE), i.e., training set and test set are all known categories of ground objects. However, the real world is usually open because of the diversity of land covers, i.e., test-set exists unknown categories that are not labeled in the training set. Therefore, the prevalent advanced CSE methods still cannot effectively and robustly handle unknown categories of ground objects in an open-set environment (OSE). Therefore, we propose a spectral-spatial MLP-like network with reciprocal points learning (SSMLP-RPL) to improve the performance of open-set HSI classification. First, a feature learning framework based on reciprocal points learning (RPL) is constructed to model the extra-category space and reduce the risk of open space. The learned feature space enables to enlarge the distance between the known and unknown categories. Besides, we further propose to utilize a learnable dynamic threshold of each known category to effectively distinguish the unknown categories and improve open performance of the model. Second, to enhance the capacity of feature learning, a spectral-spatial MLP-like network (SSMLP) is designed to capture the spectral-spatial feature merely with a series of fully-connected (FC) layers, which mainly involve SpeFC and SpaFC two modules. Among them, the SpaFC module enables to model spacial semantics, and the SpeFC module enables to model long-distance spectral dependence. Extensive experiments on three benchmark HSIs show that SSMLP-RPL has a competitive performance both in CSE and OSE and even surpasses currently advanced closed-set and open-set HSI classification methods. As an end-to-end HSI classification framework of MLP-backbone, SSMLP network can compete with the advanced works based on CNN and transformer. The code will be open at: https://github.com/sssssyf/SSMLP-RPL.

Spectral–Spatial Masked Transformer With Supervised and Contrastive Learning for Hyperspectral Image Classification

Recently, due to the powerful capability at modeling the long-range relationships, Transformer-based methods have been widely explored in many research areas including hyperspectral image (HSI) classification. However, because of lots of trainable parameters and the lack of inductive bias, it is difficult to train a Transformer-based HSI classifier, especially when the number of training samples is limited. To address this issue, in this study, spectral-spatial masked Transformer (SS-MTr) is explored for HSI classification, which uses a two-stage training strategy. In the first stage, SS-MTr pre-trains a vanilla Transformer via reconstruction from masked HSI inputs, which embeds the local inductive bias into the Transformer. In the second stage, the well pre-trained Transformer is cooperated with a fully connected layer and then fine-tuned for the HSI classification. Furthermore, in order to incorporate discriminative feature learning into the SS-MTr, three SS-MTr-based methods, including contrastive SS-MTr (C-SS-MTr), supervised SS-MTr (S-SS-MTr), and supervised contrastive SS-MTr (SC-SS-MTr) are proposed by adding extra branches for specific tasks in parallel with the existing reconstruction task. Specifically, the proposed C-SS-MTr adds a contrastive loss which brings instance discriminability. Besides, the proposed S-SS-MTr builds an extra classification branch for embracing inter-class discriminability and intra-class similarity. Moreover, the proposed SC-SS-MTr combines C-SS-MTr and S-SS-MTr for better generalization. The proposed SS-MTr, C-SS-MTr, S-SS-MTr, and SC-SS-MTr are tested on three popular hyperspectral datasets (i.e., Indian Pines, Pavia University, and Houston). The obtained results reveal that the proposed models achieve competitive results compared with the state-of-the-art HSI classification methods. Code is available at https://github.com/mengduanjinghua/SS-MTr.

Hyperspectral Image Classification Using Spectral–Spatial Token Enhanced Transformer With Hash-Based Positional Embedding

Hyperspectral image (HSI) classification aims to distinguish the category of a land coverage object for each pixel. In an effective way, the transformer architecture has been successfully introduced for the HSI classification task with promising performance. However, existing transformer-based HSI classification methods still suffer from the inability to fully explore both spectral information and spatial information in HSIs. To this end, we propose a Spectral-Spatial Token Enhanced Transformer (SSTE-Former) method with the hash-based positional embedding, which is the first to exploit multiscale spectral-spatial information for transformer-based HSI classification in-depth. Specifically, SSTE-Former accepts multiscale HSI cubes centered on the target pixel, that are preprocessed by PCA. Then, a designed multiscale CNN architecture is utilized to extract short-range spectral-spatial features and generate token embeddings. In parallel, a novel hash-based spatially enhanced positional embedding tailored for HSI cubes is developed to model the correlations within and across multiscale token embeddings. Finally, multiscale token embeddings and hash-based positional embeddings are concatenated and flattened into the transformer encoder for long-range spectral-spatial feature fusion. We conduct extensive experiments on four benchmark HSI datasets and achieve superior performance compared with the state-of-the-art HSI classification methods.

A Spectral–Spatial Feature Rotation-Based Ensemble Method for Imbalanced Hyperspectral Image Classification

Hyperspectral image classification has two key characteristics: spatial dependency and class imbalance. The synthetic oversampling methods for solving the between-class imbalance has the problem of spatial information loss to some extent. However, the feature space optimization methods for solving data within-class complex distribution problems have not been studied extensively. To remedy such issue, we propose in this paper an improved spectral-spatial feature rotation based SVM ensemble method (SSFRoF). More precisely, in the data preprocessing stage and from the perspective of sample space optimization, we devise a spectral-spatial neighborhood based adaptive synthetic sampling algorithm (SSAS) that focuses adaptively on hard-to-learn samples, with artificial samples interpolated in the spectral-spatial neighborhood to further utilize the spatial information. In feature rotation stage and from the perspective of feature space optimization, we innovatively discover that the class overlapping problem can be handled by combining feature extraction, so as to achieve the goal of reducing the difficulty of imbalanced classification. Besides, the SVM and voting procedure are used in the ensembled classification stage. We then conduct a series of experiments to compare the proposed SSFRoS with several baselines in terms of seven metrics. The results show that SSFRoS has significant advantages in dealing with imbalanced hyperspectral image classification problem. In addition, a detailed ablation study shows that the lowest class recall can be improved by 46.67% and 5.26% on Indian Pines and Pavia University respectively, which demonstrates that using SSAS can enhance the recognition of hard-to-learn classes.

A Bayesian Meta-Learning-Based Method for Few-Shot Hyperspectral Image Classification

Few-shot learning provides a new way to solve the problem of insufficient training samples in hyperspectral classification. It can implement reliable classification under several training samples by learning meta-knowledge from similar tasks. However, most existing works perform frequency statistics, which may suffer from the prevalent uncertainty in point estimates (PEs) with limited training samples. To overcome this problem, we reconsider the hyperspectral image few-shot classification (HSI-FSC) task as a hierarchical probabilistic inference from a Bayesian view and provide a careful process of meta-learning probabilistic inference. We introduce a prototype vector for each class as latent variables and adopt distribution estimates (DEs) for them to obtain their posterior distribution. The posterior of the prototype vectors is maximized by updating the parameters in the model via the prior distribution of HSI and labeled samples. The features of the query samples are matched with prototype vectors drawn from the posterior; thus, a posterior predictive distribution over the labels of query samples can be inferred via an amortized Bayesian variational inference approach. Experimental results on four datasets demonstrate the effectiveness of our method. Especially given only three to five labeled samples, the method achieves noticeable upgrades of overall accuracy (OA) against competitive methods.

Soft Instance-Level Domain Adaptation With Virtual Classifier for Unsupervised Hyperspectral Image Classification

Adversarial learning-based unsupervised hyperspectral image (HSI) classification methods usually adapt probability distributions by minimizing the statistical distance between similar pixels of different HSIs. Since the adversarial learning may weaken the discriminability of features, the extracted features will contain a lot of non-discriminative information, pixels with similar features may be classified as different classes. Therefore, directly reducing the statistical distance between similar pixels in a latent space may aggravate misclassification. To this end, we propose an unsupervised HSI classification method called soft instance-level domain adaptation with virtual classifier. First, the domain-invariant features of HSI are extracted by a graph convolutional network. Then, a feature similarity metric-based virtual classifier is constructed to output class probabilities of target-domain samples. Furthermore, to enable similar features of HSIs from different domains to be classified into the same class, the divergence between the real and virtual classifiers is reduced by minimizing the real and virtual classifier determinacy disparity. Finally, to reduce the influence of noisy pseudo-labels, a soft instance-level domain adaptation method is proposed. For each target-domain sample, the confidence coefficients are assigned to its corresponding positive and negative samples in the source domain, and a soft prototype contrastive loss is constructed and minimized to adapt two domains in an instance-level way. Experimental results on five real HSI datasets including Botswana, Kennedy Space Center, Pavia Center, Pavia University, and HyRANK demonstrate the effectiveness of our proposed method.

Distance Constraint-Based Generative Adversarial Networks for Hyperspectral Image Classification

Hyperspectral image (HSI) classification suffers from two serious problems, one is the limited labeled pixels, and the other is the class imbalance problem. As a result, the number of labeled pixels in many categories is not sufficient to characterize the spectral-spatial information, and train a satisfying deep model. By making full use of the information of unlabeled pixels, semi-supervised methods can provide better classification performance in the case of limited labeled pixels. However, they do not take into account the imbalance in the HSI data. As a method of data enhancement, generative adversarial networks focus on the above two problems and have also been widely used for the task of the HSI classification. In this work, we propose a distance constraints-based generative adversarial networks (DGAN) method for HSI classification to address these two problems. The DGAN employs the convolution autoencoder (AE) to extract the latent features of the HSI samples, and considers the reconstructed samples from the AE as the real samples for the later classifier and discriminator. In addition, the DGAN uses two distance constraints to solve the problems of the few labeled samples and class imbalance, the one latent-data distance constraint enforcing the generator to generate HSI samples for each class (especially the minority class), another discriminator-score distance constraint guiding the generator to synthesize samples that resemble the real HSI samples. Finally, the generated samples are combined classwise with the reconstructed samples and the real HSI samples to learn the parameters of the classifier and discriminator. Experimental results show that our method achieves state-of-the-art performance in terms of overall accuracy (OA) when trained with only 0.5%-4% of data sets from Indian Pines, Pavia University, and Botswana. Specifically, our method demonstrates improvements of 5.48%, 8.79%, and 0.91% on these three datasets, respectively. It reveals the great potential of the DGAN model in generating the HSI samples for each class, which contributes to improving the classification performance of the HSI data.

Bag-of-Features-Driven Spectral-Spatial Siamese Neural Network for Hyperspectral Image Classification

Deep learning (DL) exhibits commendable performance in hyperspectral image (HSI) classification because of its powerful feature expression ability. Siamese neural network further improves the performance of DL models by learning similarities within-class and differences between-class from sample pairs. However, there are still some limitations in siamese neural network. On the one hand, siamese neural network usually needs a large number of negative pair samples in the training process, leading to computing overhead. On the other hand, current models may lack interpretability because of complex network structure. To overcome the above limitations, we propose a spectral-spatial siamese neural network with bag-of-features (S3BoF) for HSI classification. First, we use a siamese neural network with 3-D and 2-D convolutions to extract the spectral-spatial features. Second, we introduce stop-gradient operation and prediction head structure to make the siamese neural network work without negative pair samples, thus reducing the computational burden. Third, a bag-of-features (BoF) learning module is introduced to enhance the model interpretability and feature representation. Finally, a symmetric loss and a cross entropy loss are respectively used for contrastive learning and classification. Experiments results on four common hyperspectral datasets indicated that S3BoF performs better than the other traditional and state-of-the-art deep learning HSI classification methods in terms of classification accuracy and generalization performance, with improvements in terms of OA around 1.40%–30.01%, 0.27%–8.65%, 0.37%–6.27%, 0.22%–6.64% for Indian Pines, University of Pavia, Salinas, and Yellow River Delta datasets, respectively, under 5% labeled samples per class.

Bi-Classifier Adversarial Network for Cross-Scene Hyperspectral Image Classification

Labeling hyperspectral images (HSIs) is time-consuming and labor-intensive for researchers, so the deficiency of adequate labeling samples is a giant obstacle to conducting HSI classification. Especially, such issue is exacerbated when there are no available labeled samples in the target scene. For the sake of resolving aforesaid issue, we put forward a novel cross-scene HSI classification method namely bi-classifier adversarial augmentation network (BCAN) so as to transfer knowledge from a similar but different source domain to an unlabeled target domain. First, the source and target domain distributions are aligned by maximizing and minimizing the decision discrepancy between two classifiers, respectively. Then, more accurate samples corresponding to pseudo-labels are selected as reliable samples and added to the training set. Finally, the spectral band random zeroing (SBRZ) method is proposed to expand the training samples for reliable samples, which handles the problem of insufficient network training resulted from insufficient samples in the source domain. By using multi-classifiers for domain adaptation and data augmentation, the accuracy of the network for cross-scene HSI classification tasks are improved. BCAN can extract the source domain’s helpful information to complete the target domain classification task. Experiments conducted on ten HSI data pairs show that BCAN outperforms many state-of-the-art baselines.