Convolutional neural networks (CNNs) have attracted much attention in change detection for their superior feature learning ability. However, most of the existing CNN-based change detection methods adopt an early-fusion or late-fusion strategy to fuse low-level spatial details or high-level semantic information. So far, the impact of multi-level fusion strategy across multitemporal hyperspectral (HS) images, and its application to change detection, remains unexplored. In this paper, we propose a multi-level encoder-decoder attention network (ML-EDAN), which allows the network to make full use of the hierarchical features for change detection in HS images. A two-stream encoder-decoder framework is taken as the backbone to exploit and fuse the hierarchical features from all the convolutional layers of multitemporal HS images. Within the encoder-decoder, a contextual-information-guided attention module is developed to yield more effective spatial-spectral feature transfer in the network. After fully obtaining the multi-level hierarchical features, the long short-term memory (LSTM) subnetwork is devised to analyze temporal dependence between multitemporal images. Moreover, the proposed ML-EDAN is trained in an end-to-end manner with a new joint loss function considering both reconstruction error and pixel-wise classification error. The experiments are conducted on three data sets, demonstrating the effectiveness of the proposed ML-EDAN in HS change detection in comparison with widely-accepted state-of-the-art methods.

Hyperspectral image (HSI) change detection aims to identify the differences in multitemporal HSIs. Recently, a graph convolutional network (GCN) has attracted increasing attention in the field of remote sensing due to its advantages in processing irregular data. In comparison with a convolutional neural network (CNN) that can only perform convolution operations on data with the assumption of the Euclidean structure, GCN adopts a graph structure to flexibly capture the characteristics and structure information of non-Euclidean data. In this article, we propose a novel dual-branch difference amplification GCN (D ² AGCN) for HSI change detection with limited samples, which allows the network to fully extract and effectively amplify the difference features of multitemporal HSIs for change detection. The dual-branch structure can effectively extract sufficient different features to facilitate the detection of the changed areas. As far as we know, this is the first time that GCN has been introduced into HSI change detection. A difference magnification module is designed to suppress similar regions and highlight the feature differences between the multitemporal HSIs in the dual-branch structure, which increases the distinction between change and nonchange classes. The visual and quantitative experimental results on three real hyperspectral datasets (i.e., China, Bay Area, and Santa Barbara) show that the proposed D ² AGCN outperforms most of the state-of-the-art methods in HSI change detection with limited training samples.

Hyperspectral image (HSI) change detection is a challenging task that focuses on identifying the differences between multi-temporal HSIs. The recent advancement of convolutional neural network (CNN) has made great progress on HSIs change detection. However, due to the limited receptive field, most CNN based change detection models trained with sufficient labeled samples cannot flexibly model the global information that is essential for distinguishing complex objects, thereby achieving relatively-low performance. In this paper, we propose a dual-branch local information enhanced graph-transformer change detection network to fully exploit the local-global spectral-spatial features of the multi-temporal HSIs with limited training samples for change recognition. Specifically, the proposed network is composed of a cascaded of local information enhanced graph-transformer (LIEG) blocks, which jointly extracts local-global features by learning local information representation to enhance the information of graph-transformer. A novel graph-transformer is developed to model global spectral–spatial correlation between graph nodes, enabling the spectral information preservation of HSIs and accurate change detection of areas with various sizes. Extensive experiments have proved that our method achieves significant performance improvement than other state-of-the-art methods on four commonly used HSI datasets.

Hyperspectral image (HSI) change detection is a technique for detecting the changes between multitemporal HSIs of the same scene. Many existing change detection methods have achieved good results, but there still exist problems as follows: i) mixed pixels exist in HSI due to the low spatial resolution of hyperspectral sensor and other external interference, and ii) many existing deep learning based networks cannot make full use of the correlation difference information between the bitemporal images. These problems are not conducive to further improving the accuracy of change detection. In this paper, we propose an abundance matrix correlation analysis network based on hierarchical multi-head self-cross-hybrid attention (AMCAN-HMSchA) for HSI change detection, which hierarchically highlights the correlation difference information at the sub-pixel level to detect the subtle changes. The endmember sharing based abundance matrix learning module (AMLM) maps the changed information between bitemporal HSIs to the corresponding abundance matrices. The hierarchical multi-head self-cross-hybrid attention (MSchA) extracts the enhanced difference features by constantly comparing the self-correlation with cross-correlation between the abundance matrices of the HSIs. Then, the difference features are concatenated and fed into the fully connected layers to obtain the change map. Experiments on three widely used datasets show that the proposed method has superior performance compared with other state-of-the-art methods.

Hyperspectral (HS) pansharpening is of great importance in improving the spatial resolution of HS images for remote sensing tasks. HS image comprises abundant spectral contents, whereas panchromatic (PAN) image provides spatial information. HS pansharpening constitutes the possibility for providing the pansharpened image with both high spatial and spectral resolution. This article develops a specific pansharpening framework based on a generative dual-adversarial network (called PS-GDANet). Specifically, the pansharpening problem is formulated as a dual task that can be solved by a generative adversarial network (GAN) with two discriminators. The spatial discriminator forces the intensity component of the pansharpened image to be as consistent as possible with the PAN image, and the spectral discriminator helps to preserve spectral information of the original HS image. Instead of designing a deep network, PS-GDANet extends GANs to two discriminators and provides a high-resolution pansharpened image in a fraction of iterations. The experimental results demonstrate that PS-GDANet outperforms several widely accepted state-of-the-art pansharpening methods in terms of qualitative and quantitative assessment.

The effective fusion of multisource data helps to improve the performance of land cover classification. Most existing convolutional neural network (CNN)-based methods adopt an early/late fusion strategy to fuse the low-/high-level features for classification, which still has two inherent challenges: 1) the conventional convolution operation performs a weighted average operation on each pixel in the receptive field, which will reduce the discriminability of the center pixel due to the influence of the interference pixels and 2) the spatial–spectral features of the hyperspectral image (HSI), the elevation features of light detection and ranging (LiDAR), and the complementary features between the multimodal data are not fully exploited, which results in the reduction of classification accuracy. In this article, an effective multibranch feature fusion network with self- and cross-guided attention (MB2FscgaNet) is proposed for the joint classification of LiDAR and HSI. The main concern of this article is how to accurately estimate more effective spectral–spatial-elevation features and yield more effective transfer in the network. Specifically, MB2FscgaNet adopts a multibranch feature fusion architecture to fully exploit the hierarchical features from LiDAR and HSI level by level. At each level of the network, a self- and cross-guided attention (SCGA) is developed to assign a higher weight to interesting areas and channels of LiDAR and HSI feature maps to obtain refined spectral–spatial-elevation features and provide complementary information cross-guidance between LiDAR and HS. We further designed a spectral supplement module (SeSuM) to improve the discriminative ability of the center pixel. Comparative classification results and ablation studies demonstrate that the proposed MB2FscgaNet achieves competitive performance against state-of-the-art methods.