Cross-scene hyperspectral image (HSI) classification has received widespread attention due to its practicality. However, domain adaptation-based cross-scene HSI classification methods are typically tailored for a specific target scene involved in model training and require retraining for new scenes. We instead propose an novel spectral-spatial adversarial multi-domain synthetic network (S2AMSnet) that can be trained on a single source domain (SD) and generalized to unseen domains. S2AMSnet improves the robustness of the model to the unseen domain by expanding the diverse distribution of the SD. Specifically, to spatially and spectrally generate diversified generative domain (GD), the spectral-spatial domain generation network (S2DGN) is designed, and two S2DGNs with the same structure but not shared parameters are enabled to generate diversified GD through two-step min-max strategy. A Multi-domain mixing module is employed to expand the diversity of the GD further and enhance their class-domain semantic consistency information. Additionally, a multi-scale mutual information regularization network is used to constrain the S2DGN so that the intrinsic class semantic information of its generated GD does not deviate from the SD. A Semantic consistency discriminator with spectral-spatial feature extraction capability is utilized to capture class-domain semantic consistency information from diverse GD to obtain cross-domain invariant knowledge. Comparative analysis with eight state-of-the-art transfer learning methods on three real HSI datasets, along with an ablation study, validates the effectiveness of the proposed S2AMSnet in the cross-scene HSI classification task. The codes of this work will be available at https://github.com/daxichen/S2AMSnet.

Hyperspectral target detection (HTD) is a method that recognizes objects of interest in a scene by a priori target spectrum. Local details and global information on the spectra are critical for accurate target identification. Detectors with excellent discrimination of spectral differences can better highlight targets while suppressing background. To this end, this article proposes an HTD method based on spectral graph contrast clustering assignment and the spectral graph transformer (SGT) to solve these problems. Specifically, for local-global feature extraction of spectra, the pixel spectra are first constructed as the spectral graph. Then, the representations of the first- or higher-order neighbors of the nodes in the spectral graph are aggregated using graph convolutional networks to extract the local detail information of the spectra. The self-attention in Transformer is utilized to learn the global information of the spectra. Second, a novel spectral graph contrast clustering assignment method is proposed to equip the model with excellent spectral discrimination ability. It maintains clustering consistency by swapping predictive clustering assignments while maximizing the similarity of semantically similar graph clusters and keeping other semantically different graph clusters away from them to better discriminate differences between spectra. Finally, comparisons with seven state-of-the-art HTD methods on four real hyperspectral datasets and ablation studies verify the effectiveness of the proposed method in HTD.

Deep learning-based hyperspectral target detection (HTD) methods are limited by the lack of prior information. Self-supervised learning is a kind of unsupervised learning, which mainly mines its own self-supervised information from unlabeled data. By training the model with such constructed valid posterior information, a valuable representation model can be learned and can get rid of the dependence of deep models on prior information. To this end, this article proposes a self-supervised spectral-level contrastive learning-based HTD (SCLHTD) method to train a model with spectral difference discrimination capability for HTD in a self-supervised manner. First, the hyperspectral images (HSIs) to be detected are sampled in odd and even bands, and the obtained band subsets are then used to train the corresponding adversarial convolutional autoencoders. Feature extraction part of the trained encoder is then used as the data augmentation function, where the positive and negative pairs are constructed through data augmentation, and the backbone is used to extract the representative vectors of the augmented samples. Second, the representative vectors are mapped to the spectral contrast space using spectral contrastive head, where the similarity and dissimilarity of spectra are learned by maximizing the similarity of positive pairs while minimizing the similarity of negative pairs, so that the backbone can discriminate spectral differences. Finally, aiming at suppressing the background, edge-preserving filters are used in conjunction with space information to process the detection results acquired by utilizing spectrum information via cosine similarity to generate the final detection results. Experimental results illustrate that the proposed SCLHTD method can achieve superior performances for HTD.

With the development and progress of deep learning, the use of deep learning technology for hyperspectral target detection has achieved excellent results. However, most deep-learning-based methods do not effectively suppress background. This paper presents a contrastive learning-based hyperspectral target detection (CLHTD) for this purpose. The positive and negative pairs are constructed through data augmentation, and the backbone is used to extract the representative vectors of the augmented samples. Then the representative vectors are mapped to the spectral and the cluster contrast space using their corresponding contrastive head, respectively. In the contrast space, the similarity and dissimilarity of spectra and clusters are learned by maximizing the similarity of positive pairs while minimizing the similarity of negative pairs, to increase the difference between the representative vectors of target and background. Finally, the detection result is obtained through the cosine distance. Experimental results illustrate that the proposed CLHTD algorithm can achieve superior performances for hyperspectral target detection.

When predicting data for which limited supervised information is available, hyperspectral target detection methods based on deep transfer learning expect that the network will not require considerable retraining to generalize to unfamiliar application contexts. Meta-learning is an effective and practical framework for solving this problem in deep learning. This article proposes a new meta-learning based hyperspectral target detection using Siamese network (MLSN). First, a deep residual convolution feature embedding module is designed to embed spectral vectors into the Euclidean feature space. Then, the triplet loss is used to learn the intraclass similarity and interclass dissimilarity between spectra in embedding feature space by using the known labeled source data on the designed three-channel Siamese network for meta-training. The learned meta-knowledge is updated with the prior target spectrum through a designed two-channel Siamese network to quickly adapt to the new detection task. It should be noted that the parameters and structure of the deep residual convolution embedding modules of each channel in the Siamese network are identical. Finally, the spatial information is combined, and the detection map of the two-channel Siamese network is processed by the guiding image filtering and morphological closing operation, and a final detection result is obtained. Based on the experimental analysis of six real hyperspectral image datasets, the proposed MLSN has shown its excellent comprehensive performance.

High dynamic range (HDR) technology is one of the most widely used ways to improve image quality, and fusion of a series of low dynamic range (LDR) images is the main measure to obtain a HDR image. However, because moving objects are often found in a series of LDR images, the fused HDR images produce ghostly shapes. In order to eliminate ghosts, this paper proposes a ghost-free multi-exposure fusion method. Firstly, aligning the moving object in the input multi-exposure sequence images with the moving object in the reference image, and the aligned sequence images are obtained. In order to consider assigning more weight to pixels in the better exposure area, two weighting functions are defined. One is to measure pixel values relative to the overall brightness and adjacent exposure images, and the other one is to reflect pixel values within a range that has a larger global gradient relative to other exposures. Based on these two weighting functions, the low exposure sequence images aligned in the Laplacian pyramid are finally fused. Through experimental comparison, the obtained image has no ghost, good visual effect, and rich details.