The need for precise and efficient monitoring of the Earth’s surface has become increasingly urgent. However, existing methods primarily rely on Siamese-based neural network, which both restrict the extraction of change information and reduce computational efficiency. To address these issues, we propose a One-Stream neural network (OSMT) based on Multi-level feature Fusion for hyperspectral image (HSI) change detection (CD). The proposed method leverages the multi-level semantic change features of the bi-temporal images. First, a band-wise image fusion strategy is utilized to obtain the fused difference image of the bi-temporal HSIs. Then, a residual connection block with spatial-spectral attention mechanism is employed to extract multi-level change features. Next, a multi-level feature fusion module composed of Transformer encoders is used to fuse the change features of different levels, including spatial details at the lower level and semantic information at the higher level. Finally, a classifier is employed to predict the detection results. Experimental results on one public dataset validate the effectiveness of our proposed method.

Noise corruption commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of hyperspectral un-mixing algorithms. The noise formulation of HSIs is relatively complex and would change in conjunction with different devices and imaging settings. For real applications, applying denoising approaches without accurate close-to-reality noise modeling before unmixing may not improve, but rather degrade the unmixing performance. This study formulates a close-to-reality noise model and proposes a learning-based hyperspectral image denoising method for hyperspectral un-mixing. In the experiments, several widely used unmixing algorithms were employed to verify the effect of the proposed method. The experimental results on both synthetic and real demonstrated that our proposed method can handle HSI data with various gain settings and helps to improve the unmixing performance effectively.

Recently, convolution neural networks (CNNs) have flourished in hyperspectral image (HSI) change detection (CD). However, these approaches typically rely on single-scale and single-level features to obtain change information, limiting the further boost detection accuracy. To solve the above problem, we propose a novel multiscale and multilevel spatial–spectral features aggregated neural network ($\text{M}^{2}\text{S}^{2}$Net) for HSI CD. The proposed method leverages the multiple spatial–spectral (SS) features of bi-temporal images to mine temporal change information. First, the HSIs are cropped into patches with different spatial and spectral sizes to provide various scales SS information and alleviate the problem of spectral redundancy. Then, the patches are utilized to capture the multiscale SS features in the residual connection block (ResBlock). These bi-temporal features are inputted into the Transformer encoder-based feature fusion module to learn the discriminative change representations. Via the global receive field of the self-attention mechanism, various fine-grained change information is learned from the features at different scales. Finally, the change features of each level are aggregated to produce the change map. The experiments on two public HSI datasets verify the effectiveness of the proposed method. Specifically, the overall accuracy (OA) of the$\text{M}^{2}\text{S}^{2}$Net surpasses the second-best method by 0.56% and 0.10% on the Farmland and River datasets, respectively.

Hyperspectral image change detection (HSI CD) refers to identifying and analyzing differences between two HSIs acquired in the same area but at different times. However, current deep learning (DL)-based methods have limitations in fully exploiting the change information between bitemporal images and utilizing multilevel features. To address these issues, we propose a novel Multi-level features Fused and Change information Enhanced neural Network (MFCEN) for HSI CD. The proposed MFCEN method leverages the hierarchical low- and high-level features of bitemporal images, allowing for the direct capture and enhancement of change features. First, a Siamese-based network is employed to extract multilevel features from the bitemporal images, including low-level spatial details and high-level semantic features. Within the temporal change information branch (TCIB) at each level, the change features are reinforced by the semantic features of each image, and the change features act as a guiding force to direct the feature extraction of each bitemporal image to focus more on the change region. Next, the enhanced change features of each level are fed into the multilevel features fusion module (MFFM) to aggregate the fine-grained details and high-level semantics. Finally, the fused features, enriched with multilevel change information, are utilized for CD. The experiments demonstrate that our proposed MFCEN outperforms existing methods on three public datasets. The code will be available athttps://github.com/Ding201901/MFCEN.

Autoencoders (AEs) have demonstrated excellent performance in the field of hyperspectral unmixing (SU), due to their self-supervised nature and ease of implementation. Recently proposed AE-based networks contend that local spatial information limits further improvement in unmixing accuracy and tends to explore and utilize global information, which improves unmixing accuracy at the expense of increased computational complexity. However, we believe that precise unmixing can be achieved by fully leveraging local information. In this article, we propose a unidirectional local-attention AE network (ULA-Net) that explores spatial information pixel by pixel and achieves accurate spatial–spectral feature fusion. ULA-Net utilizes unidirectional local attention (ULA) module to calculate the correlation between neighboring pixels and the central pixel within local regions, extracting discriminative local information. Moreover, ULA-Net effectively extracts relevant spatial information and suppresses irrelevant information based on a double fusion strategy (DFS) module. This process achieves more accurate control over the contribution of spatial information by implementing information fusion in both the pixel and feature dimensions. To address spectral variability, we implement the extended linear mixing model (ELMM) in the decoder part to improve unmixing accuracy without increasing the number of parameters. We conduct ablation experiments to investigate the roles of each module. Experimental results on both synthetic and real datasets demonstrate the effectiveness of the proposed network.

Hyperspectral images (HSIs) have been widely used in remote sensing change detection for environment monitoring and urban studies. Aiming to leverage the rich information of HSIs, we proposed a joint spatial-spectral-temporal attention method for HS image change detection in this paper. To this end, we put together the convolutional block attention module (CBAM) and the recurrent neural network (RNN) to the end-to-end network. The CBAM can get spatial and spectral feature representation, and the latter can use the temporal information by a long short-term memory. Experiments on one HSIs change detection dataset demonstrated that our proposed method could get effective performance in HSIs for change detection.

Hyperspectral image (HSI) change detection (CD) has gained much attention in remote sensing. However, most deep learning methods are restricted by a limited receptive field, without leveraging temporal information, and the need for many training samples. In this letter, we proposed a Transformer Encoder-based HSI CD framework called CDFormer. First, space and time encodings are added to the pixel sequence to guide transformers to exploit change information of space and time by the pixel embedding (PE) module. Second, the self-attention component of Transformer Encoder module has a global space-time receptive field to mine the correlation and interaction between bi-temporal features, enhancing the utilization of temporal dependencies. Next, the multi-head attention mechanism learns several attentions and extracts the joint weighted spatial-spectral-temporal features, which improves the feature discrimination ability of the changes. Finally, the detection result is predicted using a fully connected network. It is notable to mention that the proposed method only uses a few labeled samples to train the network. Experiments on two HSI datasets demonstrate that our proposed method can get effective performance in HSI CD.