The low spatial resolution of hyperspectral images leads to the coexistence of multiple ground objects in a single pixel (called mixed pixels). A large number of mixed pixels in a hyperspectral image hinders the subsequent analysis and application of the image. In order to solve this problem, a novel sparse unmixing method, which considers highly similar patches in nonlocal regions of a hyperspectral image, is proposed in this paper. This method exploits spectral correlation by using collaborative sparsity regularization and spatial information by employing total variation and weighted nonlocal low-rank tensor regularization. To effectively utilize the tensor decomposition, nonlocal similar patches are first grouped together. Then, these nonlocal patches are stacked to form a patch group tensor. Finally, weighted low rank tensor regularization is enforced to constrain the patch group to obtain an estimated low rank abundance image. Experiments on simulated and real hyperspectral datasets validated the superiority of the proposed method in better maintaining fine details and obtaining better unmixing results.

With the rapid development of aerospace and various remote sensing platforms, the amount of data related to remote sensing is increasing rapidly. To meet the application requirements of remote sensing big data, an increasing number of scholars are combining deep learning with remote sensing data. In recent years, based on the rapid development of deep learning methods, research in the field of hyperspectral image (HSI) classification has seen continuous breakthroughs. In order to fully extract the characteristics of HSIs and improve the accuracy of image classification, this paper proposes a novel 3D channel and spatial attention-based multi-scale spatial'spectral residual network (termed CSMS-SSRN). The CSMS-SSRN framework uses a three-layer parallel residual network structure by using different 3D convolutional kernels to continuously learn spectral and spatial features from their respective residual blocks. Then, the extracted depth multi-scale features are stacked and input into the 3D attention module to enhance the expressiveness of the image features from the two aspects of channel and spatial domains, thereby improving the accuracy of classification. The CSMS-SSRN framework proposed in this paper can achieve better classification performance on different HSI datasets.

In this paper, a spectral-spatial convolution neural network with Siamese architecture (SSCNN-S) for hyperspectral image (HSI) change detection (CD) is proposed. First, tensors are extracted in two HSIs recorded at different time points separately and tensor pairs are constructed. The tensor pairs are then incorporated into the spectral-spatial network to obtain two spectral-spatial vectors. Thereafter, the Euclidean distances of the two spectral-spatial vectors are calculated to represent the similarity of the tensor pairs. We use a Siamese network based on contrastive loss to train and optimize the network so that the Euclidean distance output by the network describes the similarity of tensor pairs as accurately as possible. Finally, the values obtained by inputting all tensor pairs into the trained model are used to judge whether a pixel belongs to the change area. SSCNN-S aims to transform the problem of HSI CD into a problem of similarity measurement for tensor pairs by introducing the Siamese network. The network used to extract tensor features in SSCNN-S combines spectral and spatial information to reduce the impact of noise on CD. Additionally, a useful four-test scoring method is proposed to improve the experimental efficiency instead of taking the mean value from multiple measurements. Experiments on real data sets have demonstrated the validity of the SSCNN-S method.

Change detection (CD) is a hot issue in the research of remote sensing technology. Hyperspectral images (HSIs) greatly promote the development of CD technology because of their high resolution in the spectral domain. However, some traditional CD methods currently applied to low-dimensional and multispectral images cannot adapt to the complex high-dimensional features of the HSIs. In addition, the spectral measurements of the HSI contain a lot of noise and redundancy, which greatly contaminates spectral-only information for CD. In order to fully extract the discriminant features of HSI to improve the accuracy of CD, this article proposes a three-directions spectral-spatial convolution neural network (TDSSC). A novel method for three-direction decomposition of hyperspectral change tensors is proposed-change tensor is decomposed along the spectral direction and two spatial directions to get a single tensor containing the spectral information and two kinds of tensors containing the spectral-spatial information. TDSSC uses 1-D convolution to extract spectral features from the spectral direction as well as reducing the tensor dimension, which helps the latter network to be lightweight and significantly improves the speed of change detection. Also, it uses 2-D convolution to extract spectral-spatial features from two spatial directions of the reduced tensor, and to extract features from different directions to improve the accuracy and Kappa value of CD. The experimental results of three real hyperspectral datasets show that TDSSC is superior to most existing CD methods.

Locality-preserving projection (LPP) has been widely used in feature extraction. However, LPP does not use data category information and uses the ${L}_{2}$ -norm for distance measurement, which is highly sensitive to outliers. In this paper, we consider the LPP weight matrix from a supervised perspective and combine the low-rank regression method to propose a new model to discover and extract features. By using the ${L}_{2,1}$ -norm to constrain the loss function and the regression matrix, not only is the sensitivity to outliers reduced but the low-rank condition of the regression matrix is also restricted. Then, we propose a solution to the optimization problem. Finally, we apply the method to a series of face databases, handwriting digital datasets and palmprint datasets to test the performance, and the experimental results show that this method is effective compared with some existing methods.

Exploration of multiple priors on observed signals has been demonstrated to be one of the effective ways for recovering underlying signals. In this paper, a new spectral difference-induced total variation and low-rank approximation (termed SDTVLA) method is proposed for hyperspectral mixed denoising. Spectral difference transform, which projects data into spectral difference space (SDS), has been proven to be powerful at changing the structures of noises (especially for sparse noise with a specific pattern, e.g., stripes or dead lines present at the same position in a series of bands) in an original hyperspectral image (HSI), thus allowing low-rank techniques to get rid of mixed noises more efficiently without treating them as low-rank features. In addition, because the neighboring pixels are highly correlated and the spectra of homogeneous objects in a hyperspectral scene are always in the same low-dimensional manifold, we are inspired to combine total variation and the nuclear norm to simultaneously exploit the local piecewise smoothness and global low rankness in SDS for mixed noise reduction of HSI. Finally, the alternating direction methods of multipliers (ADMM) is employed to effectively solve the SDTVLA model. Extensive experiments on three simulated and two real HSI datasets demonstrate that, in terms of quantitative metrics (i.e., the mean peak signal-to-noise ratio (MPSNR), the mean structural similarity index (MSSIM) and the mean spectral angle (MSA)), the proposed SDTVLA method is, on average, 1.5 dB higher MPSNR values than the competitive methods as well as performing better in terms of visual effect.