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Hyperspectral anomaly detection, which aims to search and localize potential targets, is a research area with extensive application prospects and profound implications. In recent years, the emergence of unsupervised and self-supervised deep learning for image reconstruction has provided inspiring solutions for hyperspectral anomaly detection. However, due to sensor-induced and environmental effects, the full-image detection networks inevitably reconstruct anomalies along with the background. Existing detectors indirectly mitigate anomaly reconstruction by imposing constraints on hidden features or loss functions, but they provide unsatisfactory performance in large target detection scenarios. This work straightforwardly addresses this issue from the input source, i.e., introducing the concept of masked autoencoders (MAEs) into fully convolutional networks and further developing a sparse-inspired masked convolutional network (SMCNet) consisting of three mutually supportive components: 1) a hierarchical encoder; 2) a sparse projection layer; and 3) a hierarchical decoder. The encoder employs an adaptive potential anomaly masking strategy, leveraging sparse convolution for extracting multidimensional features of the remaining background. Meanwhile, a sparse-guided projection layer is created by discarding the positional embedding technique to populate the uncoded region and guide the background recovery without introducing anomalies. Finally, the decoder couples the hierarchical structure and a hybrid attention mechanism (local-middle–global and spatial-spectral) to refine the background during image recovery, whereas anomalies in the residual map are highlighted. Extensive experiments using ten typical competitors on six different types of datasets validate the effectiveness and generalization ability of the newly proposed SMCNet method.

Graph convolutional networks (GCNs) have been skillfully employed in hyperspectral image (HSI) classification, exhibiting remarkable performance owing to their unique superiority in handling non-Euclidean graph-structured data. However, the inherent absence of predefined connections between pixels in HSI results in the underutilization of the structural and attribute information of the graph edges. Furthermore, the construction of adjacency matrices for large-scale HSI data imposes a huge computational burden on traditional GCNs. Therefore, in this article, a novel method combining relaxed collaborative representation (RCR) and GCN (RCR-GCN) for hyperspectral classification is proposed. Specifically, RCR is adopted to compute the representation coefficients of each feature, reflecting the similarity and diversity among different sample features. Meanwhile, the representation coefficients are applied as edge attributes in the graph, denoting the weights of the connections between neighboring nodes. After that, GCN is employed to classify the graph nodes. Moreover, an efficient version of the RCR-GCN method is developed to boost the computation, which constructs the graph based on superpixel nodes instead of the pixel nodes by using simple linear iterative clustering (SLIC). Extensive experiments on three HSI image datasets demonstrate that the proposed method outperforms other state-of-the-art methods and achieves more efficiency and feasibility in HSI image classification.

In recent years, the rapid development of remote sensing (RS) technology has led to a drastic increase in the availability of RS images. This calls for the need to develop new methods able to effectively and efficiently retrieve the required instances from a massive amount of RS imagery. In retrieval tasks, finding the nearest-neighbor sample of the retrieval query is a fundamental research topic. Exhaustive comparison is the simplest method to accomplish this task. However, due to the involved computational complexity and memory limitations, this solution is no longer feasible in large data retrieval tasks. As an important branch of approximate nearest-neighbor retrieval (NNR), hash algorithms transform high-dimensional data into low-bit expressions (hash codes) with elements of 0 and 1 to reduce storage and computational costs. Hash algorithms aim to preserve the same nearest-neighbor relationship between the learned hash codes and the original data. Existing hash algorithms are divided into two classes: shallow and deep methods. Furthermore, deep hash algorithms can be divided into (semi-) supervised and unsupervised algorithms. In this article, representative hash-based RS image retrieval (HBRSIR) methods are reviewed, studying the application of hashing in other areas of the RS community and introducing available datasets and evaluation metrics for RS image retrieval (RSIR). The performance of representative and cross-modal hashing methods is validated using two common RSIR datasets (UCMerced and AID) and a cross-modal dataset (DSRSID). Prospects of future work summarizing HBRSIR are also provided.

Convolutional models have provided outstanding performance in the analysis of hyperspectral images (HSIs). These architectures are carefully designed to extract intricate information from nonlinear features for classification tasks. Notwithstanding their results, model architectures are manually engineered and further optimized for generalized feature extraction. In general terms, deep architectures are time-consuming for complex scenarios, since they require fine-tuning. Neural architecture search (NAS) has emerged as a suitable approach to tackle this shortcoming. In parallel, modern attention-based methods have boosted the recognition of sophisticated features. The search for optimal neural architectures combined with attention procedures motivates the development of this work. This article develops a new method to automatically design and optimize convolutional neural networks (CNNs) for HSI classification using channel-based attention mechanisms. Specifically, 1-D and spectral–spatial (3-D) classifiers are considered to handle the large amount of information contained in HSIs from different perspectives. Furthermore, the proposed automatic attention-based CNN (AAtt-CNN) method meets the requirement to lower the large computational overheads associated with architectural search. It is compared with current state-of-the-art (SOTA) classifiers. Our experiments, conducted using a wide range of HSI images, demonstrate that AAtt-CNN succeeds in finding optimal architectures for classification, leading to SOTA results.