The problem of insufficient labeled samples has restricted the application of deep learning method in hyperspectral image classification tasks. Fusion of remote sensing images from different sources such as hyperspectral image, Lidar is a common strategy to improve the classification accuracy. However, obtaining multi-source registered remote sensing images of the same area is time-consuming, which limits the application of multi-source strategy in practice. Motivated by the recent success of large models in different fields, we propose to extract depth information from large models and fuse it with hyperspectral images to improve the small sample classification accuracy. Specifically, we use the pre-trained foundation large model to estimate the depth information of hyperspectral images as the depth features, and then input the original spectral features and depth features into the support vector machine to complete the classification. In order to further improve the classification accuracy, we propose to use the sliding window method to extract the depth features of different bands, so as to obtain more rich depth features. A large number of classification experiments on six benchmark datasets verify the effectiveness of the proposed method.

As for hyperspectral images (HSIs), the discrepancy of contiguous spectral information should be the main basis for the identification of ground objects. Due to the difficulty of spectral sequence coding and the spectrum similarity between categories, successful deep-learning-based classification methods always attempt to capture the spatial information to improve the accuracy by convolutional neural networks (CNNs) or other excellent spatial feature extractors. However, extracting spatial features is generally accompanied by the distortion of ground objects distribution and categories boundary. To effectively represent spectral features, the SpectralFormer based on transformer backbone can better capture the long-term dependence of the spectrum, which improves the performance of spectral feature methods significantly. However, it is still unable to compete with advanced spectral–spatial feature methods. To exploit the discriminative advantage of the spectrum fully, this letter introduces an efficient sparse-to-dense optical flow estimation method to track the spectrum variation in the HSI. Then, we take such a variation as a spectrum motion feature to enhance the original spectrum. At last, we continue to use the SpectralFormer to encode the concatenated spectrum sequence for classification. Extensive experiments show that the SpectralFormer enhanced by the spectrum motion feature (SF-SMF) significantly improves the performance of spectral feature methods, even surpassing advanced spectral–spatial feature methods. SF-SMF can avoid interference with additional spatial information to obtain exquisite whole-domain classification maps, showing its practical value. The codes will be public athttps://github.com/sssssyf/SF-SMF.

Hyperspectral image (HSI) classification has always been recognised as a difficult task. It is therefore a research hotspot in remote sensing image processing and analysis, and a number of studies have been conducted to better extract spectral and spatial features. This study aimed to track the variation of the spectrum in hyperspectral images from a sequential data perspective to obtain more distinguishable features. Based on the characteristics of optical flow, this study introduces an optical flow technique for the extraction of spectral flow that denotes the spectral variation and implements a dense optical flow extraction method based on deep matching. Lastly, the extracted spectral flow are combined with the original spectral features and input into a commonly used support vector machine (SVM) classifier to complete the classification. Extensive classification experiments on three benchmark HSI test sets show that the classification accuracy obtained by the spectral flow extracted in this study (SpectralFlow) is higher than traditional spatial feature extraction methods, texture feature extraction methods, and the latest deep-learning-based methods. Furthermore, the proposed method can produce finer classification thematic maps, thereby demonstrating strong practical application potential.

With the rapid development of remote sensing data acquisition technology, there are multimodal images over the same observed scenes. These multimodal remote sensing images could provide complementary valuable information for land cover classification. In this article, we propose a novel self-supervised feature learning and few-shot classification model for multimodal remote sensing images, called S2FL. Specifically, a contrastive learning architecture is investigated to learn spatial feature representations from very high resolution (VHR) image. And the spectral features from hyperspectral data are integrated with learned spatial features for few-shot land cover classification. Classification experiments are conducted on a widely-used dataset, i.e., Houston 2018, to verify the effectiveness and superiority of the proposed S2FL model compared with several state-of-the-art baseline approaches.

Abstract To address the existing problems of capsule networks in deep feature extraction and spatial‐spectral feature fusion of hyperspectral images, this paper proposes a hyperspectral image classification method that combines a deep residual 3D capsule network and Markov random field. Based on this method, the deep spatial‐spectral features of hyperspectral images are extracted using the deep residual 3D convolutional structure, the vector capsules of the features are obtained by the initial capsule layer and mapped into probability capsules via the 3D dynamic routing mechanism to construct the classification probability map, and the spatial structure of the classification results is regularised by the Markov random field to further improve the classification accuracy and performance of the images. Two sets of benchmark hyperspectral images, namely Indian Pines and Pavia University data sets, were used to conduct comparative experiments and ablation study. The experimental results showed that, compared with the conventional convolutional neural network and existing capsule network models, the proposed method not only improves the classification accuracy of the images but also partly eliminates the category noise and affords a more regular classification probability map.

Although deep learning-based approaches have made significant progress in remote sensing image classification, the supervised learning paradigm has shortcomings under a limited number of labeled samples, which restricts the classification performance to a great extent. In this article, we investigate an effective self-supervised feature representation architecture (SSFR) for multimodal remote sensing images few-shot land cover classification. Specifically, we exploit multiview learning strategy to construct multiple views from multimodal remote sensing images. This method builds several complementary views of the same observed scenes from hyperspectral images or different modalities of remote sensing data. Then we build the deep feature extractor to learn high-level feature representations from each view via contrastive learning. The contrastive learning aggregates the samples of the same scene while separating samples of different scenes in the latent space, and this process does not require any labeled information. What’s more, to learn more robust features from different views, we utilize multitask learning strategy to train the feature extraction network. Finally, a lightweight machine learning method is employed to classify the learned features using a few annotated samples. To further demonstrate the self-supervised feature learning capability of the proposed model, we train the feature representation network in multiple source datasets. Comprehensive feature learning and classification experiments have certified the effectiveness and superiority of the proposed method.

In this article, we propose a novel multiscale deep learning network with self-calibrated convolution (MSNetSC) for hyperspectral and light detection and ranging (LiDAR) data collaborative classification. Conventional deep learning methods have limitations in extracting multiscale features at a granular level from multimodality data and fusing these features in a context-awareness way, which will severely restrict the performance of hyperspectral and LiDAR data joint classification. The proposed multiscale deep learning network utilizes a hierarchical residual structure combined with self-calibrated convolution to extract features with different receptive fields, and this can enhance the model’s capability to represent the multimodality data. Besides, we employ spectral and spatial self-attention modules to adaptively calibrate weights of features with different scales, thereby enhancing the discriminative ability of extracted multiscale features. Furthermore, the attentional feature fusion module can dynamically and adaptively fuse the features from multimodality data in a contextual scale-aware way, and this attention-based feature fusion method will further improve the collaborative classification performance of hyperspectral and LiDAR data. Four benchmark multimodality data (i.e., hyperspectral and LiDAR data) sets collected by different sensors and at different acquisition times are employed for joint classification experiments. These comparative classification results and ablation study sufficiently certify the superiority of the proposed model in terms of collaborative classification accuracy when compared with other state-of-the-art methods.

Deep learning models have shown great potential in remote sensing image processing and analysis. Nevertheless, there are insufficient labeled samples to train deep networks, which seriously affects the performance of these models. To resolve this contradiction, we propose a generative self-supervised feature learning (S2FL) architecture for multimodal remote sensing image land cover classification. Specifically, multiple complementary observed views are constructed from multimodal remote sensing images, which are employed for following generative self-supervised learning. The proposed S2FL architecture is capable of extracting high-level meaningful feature representations from multiview data, and this process does not require any labeled information, providing a feasible solution to relieve the urgent need for annotated samples. The learned features are normalized and merged with corresponding spectral information to further improve the discriminative capability of feature representations, and we utilize these fused features for land cover classification. Compared with existing supervised, semi-supervised, and self-supervised approaches, the proposed generative self-supervised model achieves superior performance in terms of feature learning and land cover classification, especially in the small sample classification case.

In this study, we develop a novel deep hierarchical vision transformer (DHViT) architecture for hyperspectral and light detection and ranging (LiDAR) data joint classification. Current classification methods have limitations in heterogeneous feature representation and information fusion of multi-modality remote sensing data (e.g., hyperspectral and LiDAR data), these shortcomings restrict the collaborative classification accuracy of remote sensing data. The proposed deep hierarchical vision transformer architecture utilizes both the powerful modeling capability of long-range dependencies and strong generalization ability across different domains of the transformer network, which is based exclusively on the self-attention mechanism. Specifically, the spectral sequence transformer is exploited to handle the long-range dependencies along the spectral dimension from hyperspectral images, because all diagnostic spectral bands contribute to the land cover classification. Thereafter, we utilize the spatial hierarchical transformer structure to extract hierarchical spatial features from hyperspectral and LiDAR data, which are also crucial for classification. Furthermore, the cross attention (CA) feature fusion pattern could adaptively and dynamically fuse heterogeneous features from multi-modality data, and this contextual aware fusion mode further improves the collaborative classification performance. Comparative experiments and ablation studies are conducted on three benchmark hyperspectral and LiDAR datasets, and the DHViT model could yield an average overall classification accuracy of 99.58%, 99.55%, and 96.40% on three datasets, respectively, which sufficiently certify the effectiveness and superior performance of the proposed method.

To solve the problem of insufficient annotated samples in hyperspectral image classification, the semi‐supervised convolutional generative adversarial network classification model is proposed in this study. The generative adversarial framework constructs an adversarial game, where the generator captures data distribution and generates fake samples, while the discriminator determines whether the input comes from generated or training data. In the proposed method, a deep three‐dimensional (3D) convolutional neural network is used to generate the so‐called fake cube samples and another 3D deep residual network is designed to discriminate the inputs. Furthermore, the generated samples, labelled and unlabelled samples are put into the discriminator for joint training, and the trained discriminator can determine the authenticity of the sample and the class label. This semi‐supervised generative adversarial training strategy can effectively improve the generalisation capability of the deep residual network where the labelled samples are limited. Three widely used hyperspectral images are utilised to evaluate the classification performance of the proposed method: Indian Pines, Pavia University, and Salinas‐A. The classification results reveal that the proposed model can improve the classification performance and achieve competitive results compared with the state‐of‐art methods, especially when there are few training samples.