Hyperspectral image (HSI) characteristics of the abundant spectral information are favored by many scholars, but the challenge is how to select relevant features from such high-dimensional data. Band selection (BS), one of the most fundamental dimensionality reduction (DR) techniques, removes redundant bands while providing a subset of bands that can preserve high information content and low noise for further HSI classification. Cuckoo search (CS) algorithm is well known for its high performance of searching relevant features but struggles to get rid of local extremes in the late iteration. Therefore, in this article, an unsupervised BS method based on the heterogeneous CS algorithm with matched filter (HCS-MF) is proposed for HSI classification, in which an optimization model is constructed based on the sensitivity of the matching filter to noise. To reduce the similarity between selected bands, a mapping method based on neighborhood band grouping (NBG) is proposed. In addition, an automatic recommendation strategy based on sliding spectrum decomposition (SSD) is proposed to determine the minimum recommended number of selected bands in different scenes. The superiority of the selected subset of bands is verified by random forest, support vector machine (SVM), and edge-preserving filtering-based SVM (EPF-SVM) classifiers. Experimental results on three well-known datasets demonstrate the robustness and superiority of the proposed HCS-MF algorithm compared with the state-of-the-art methods, such as marginalized graph self-representation (MGSR), neighborhood grouping normalized matched filter (NGNMF), and region-wise multiple graph fusion (RMGF).

With the increasing spectral dimension of hyperspectral images (HSI), how correctly choose bands based on band correlation and information has become more significant, but also complicated. Band selection is a combinatorial optimization problem, and intelligent optimization algorithms have been shown to be crucial in solving combinatorial optimization problems. However, major of them only use a single objective as the selection index, while neglecting the overall features of hyperspectral images, which may lead to inaccuracy in object detection. To tackle this, we propose a band selection method based on a multi-objective cuckoo search algorithm (MOCS) when constructing a multi-objective unsupervised band selection model based on the amount of information and correlation of the bands (MOCS-BS). Specifically, an adaptive strategy based on population crowding degree is first proposed to assist Lévy flight in overcoming the influence of the parameter constancy. Then, an information-sharing strategy based on grouping and crossover is designed to balance the search ability between global exploration and local exploitation, which can overcome the shortcomings caused by the lack of information interaction between individuals. Finally, the HSI classification experiments are performed by Random Forest and KNN classifiers based on the subset of bands selected by the proposed MOCS-BS method. The proposed method is compared with state-of-the-art algorithms including neighborhood grouping normalized matched filter (NGNMF) and multi-objective artificial bee colony with band selection (MABC-BS) on four HSI datasets. The experimental results demonstrate that MOCS-BS is more effective and robust than other methods.

Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification tasks because of their excellent local spatial feature extraction capabilities. However, because it is difficult to establish dependencies between long sequences of data for CNNs, there are limitations in the process of processing hyperspectral spectral sequence features. To overcome these limitations, inspired by the Transformer model, a spatial–spectral transformer with cross-attention (CASST) method is proposed. Overall, the method consists of a dual-branch structures, i.e., spatial and spectral sequence branches. The former is used to capture fine-grained spatial information of HSI, and the latter is adopted to extract the spectral features and establish interdependencies between spectral sequences. Specifically, to enhance the consistency among features and relieve computational burden, we design a spatial–spectral cross-attention module with weighted sharing to extract the interactive spatial–spectral fusion feature intra Transformer block, while also developing a spatial–spectral weighted sharing mechanism to capture the robust semantic feature inter Transformer block. Performance evaluation experiments are conducted on three hyperspectral classification datasets, demonstrating that the CASST method achieves better accuracy than the state-of-the-art Transformer classification models and mainstream classification networks.

A novel filter is proposed for edge-preserving decomposition of an image. It is different from previous filters in its locally adaptive property. The filtered image contains local means everywhere and preserves local salient edges. Comparisons are made between our filtered result and the results of three other methods. A detailed analysis is also made on the behavior of the filter. A multiscale decomposition with this filter is proposed for manipulating a high dynamic range image, which has three detail layers and one base layer. The multiscale decomposition with the filter addresses three assumptions: 1) the base layer preserves local means everywhere; 2) every scale's salient edges are relatively large gradients in a local window; and 3) all of the nonzero gradient information belongs to the detail layer. An effective function is also proposed for compressing the detail layers. The reproduced image gives a good visualization. Experimental results on real images demonstrate that our algorithm is especially effective at preserving or enhancing local details.