The fusion of hyperspectral images (HSIs) and multispectral images (MSIs) has been widely focused in the field of remote sensing image processing. Traditional fusion approaches have no consideration of the topological consistency between the target high-resolution HSI (HR-HSI) and the input HSI/MSI. To remedy such deficiency, in this letter, we propose a novel topological consistency network (TCNet) for HSI and MSI fusion. Specifically, two degradation models are firstly constructed to form the spatial mapping between the input HSI and the target HR-HSI as well as the spectral mapping between the MSI and the HR-HSI. With this way, the basic observation model is formulated. The spectral topological consistency loss and spatial topological consistency loss are then established by imposing graph construction on spectral vectors and spatial features, which are added into the basic observation model to acquire the overall loss of TCNet. Thirdly, the gradient descent based optimization strategy is designed to obtain the solutions of the overall loss and determine the final fusion mapping network. Extensive experiments have been implemented to validate that the proposed TCNet method achieves more competitive performance compared with several state-of-the-art approaches.

High resolution hyperspectral fusion imaging (HRHFI) is an important topic in the remote sensing related tasks, which integrates advantages of hyperspectral images (HSIs) and multipectral images (MSIs) to generate the high resolution HSIs (HR-HSIs). Traditional methods fail to maintain multi-scale change information and directional information of HSIs and MSIs during the HRHFI process, limiting the performance. To solve this challenge, a novel hierarchical gradient preserved network (HGPN) is proposed for HRHFI. Concretely, we begin by establishing the foundational observation model, which serves to make an initial estimate of HR-HSI so that the basic observation loss is thus constructed. Subsequently, we integrate both multi-scale spectral gradient preservation loss and multi-scale spatial gradient preservation loss into the basic observation loss, which results in the overall loss function of the proposed HGPN method. Further, an optimization strategy based on alternating directions is devised to solve the overall loss and derive the corresponding solutions, which finalizes the imaging fusion mapping. Different from traditional approaches, the proposed HGPN method can achieve hierarchical fusion, which retains multi-scale gradient information of input HSI/MSI for HRHFI. Extensive experiments have been implemented to verify that the proposed HGPN method achieves more competitive performance than several state-of-the-art approaches in the HRHFI task.

Deep learning has achieved outstanding success in the hyperspectral image (HSI) classification task. Almost all the current deep learning methods are used to conduct classification predictions by leveraging the output features from the deepest layer, which generally ignore the attention to multilayer outputs, so that the capability of hierarchical representation is limited. To remedy such deficiency, in this article, we propose to build a novel deep network form, called tensored deep autoencoder network (TDAE), for HSI classification. For this method, the tensor decomposition constraint item is built and introduced into a deep autoencoders network with a fully connection layer. It not only achieves the integration of multilayer output features but also captures the structure information among outputs. By such way, the network’s ability for hierarchical representation is significantly enhanced. Furthermore, to solve such built model, we further design an alternating update optimization scheme and obtain the desired feature forms. The features are further input into the fully connection layer to generate the label of the given HSI. Extensive experiments have been conducted to validate that the proposed TDAE method achieves more competitive performance compared with several state-of-the-art approaches.

In this presented work, a new tracking control strategy is designed for a kind of nonholonomic system under external disturbances. First, the whole system is divided into two design stages by using the relay switching technique. Then, using state transformations, the two primitive subsystems are converted into unconstrained systems for which tracking controllers are designed based on dynamic surface control. Different from most existing finite-time control strategies and fixed-time control strategies, our controllers are able to control the tracking error within a specified accuracy within a specified time, starting from anywhere, without changing the control structure/parameters. Meanwhile, all closed-loop signals keep bounded in the whole control process. Extensive computer simulation and physical experiments have been given to validate good performance of the designed controller.

Deep learning has achieved many successes in the field of the hyperspectral image (HSI) classification. Most of existing deep learning-based methods have no consideration of feature distribution, which may yield lowly separable and discriminative features. From the perspective of spatial geometry, one excellent feature distribution form requires to satisfy both properties, i.e., block and ring. The block means that in a feature space, the distance of intraclass samples is close and the one of interclass samples is far. The ring represents that all class samples are overall distributed in a ring topology. Accordingly, in this article, we propose a novel deep ring-block-wise network (DRN) for the HSI classification, which takes full consideration of feature distribution. To obtain the good distribution used for high classification performance, in this DRN, a ring-block perception (RBP) layer is built by integrating the self-representation and ring loss into a perception model. By such way, the exported features are imposed to follow the requirements of both block and ring, so as to be more separably and discriminatively distributed compared with traditional deep networks. Besides, we also design an optimization strategy with alternating update to obtain the solution of this RBP layer model. Extensive results on the Salinas, Pavia Centre, Indian Pines, and Houston datasets have demonstrated that the proposed DRN method achieves the better classification performance in contrast to the state-of-the-art approaches.

This article presents a novel deep network with irregular convolutional kernels and self-expressive property (DIKS) for the classification of hyperspectral images (HSIs). Specifically, we use the principal component analysis (PCA) and superpixel segmentation to obtain a series of irregular patches, which are regarded as convolutional kernels of our network. With such kernels, the feature maps of HSIs can be adaptively computed to well describe the characteristics of each object class. After multiple convolutional layers, features exported by all convolution operations are combined into a stacked form with both shallow and deep features. These stacked features are then clustered by introducing the self-expression theory to produce final features. Unlike most traditional deep learning approaches, the DIKS method has the advantage of self-adaptability to the given HSI due to building irregular kernels. In addition, this proposed method does not require any training operations for feature extraction. Because of using both shallow and deep features, the DIKS has the advantage of being multiscale. Due to introducing self-expression, the DIKS method can export more discriminative features for HSI classification. Extensive experimental results are provided to validate that our method achieves better classification performance compared with state-of-the-art algorithms.

Sparse representation (SR)-based methods have achieved numerous successes in the fusion of hyperspectral and multispectral images (HSIs and MSIs). However, in many SR-based fusion methods, due to patch dividing, it is hard to make pixel values across boundaries of contiguous patches be exactly consistent, which limits the ability to preserve scene details. To remedy such deficiency, a fusion framework is proposed for HSIs and MSIs by using convolutional sparse representation (FCS). This novel fusion method consists of three stages: 1) the spectral dictionary is trained by the convolutional sparse dictionary learning algorithm to extract spectral information from HSIs; 2) hyperspectral and multispectral transferring matrices are estimated to map HSIs and MSIs onto the space of high-resolution hyperspectral images (HR-HSIs); and 3) we construct the convolutional sparse fusion model for HR-HSIs. Different from those traditional patch-based SR fusion methods, the FCS method focuses on the whole images instead of dividing patches, which can suppress the limitation of scene detail preservation caused by sparse coding on independent patches. Also, it belongs to a kind of online learning without lots of training samples. The Pavia dataset and the Paris dataset are used to evaluate the performance of our method. Experimental results indicate that the FCS method achieves much fusion performance compared with commonly used and state-of-the-art algorithms.

Hyperspectral image (HSI) classification is a current research hotspot. Most existing methods usually export discriminative features with low-quality distribution and low information utilization, which may induce classification performance degeneration. To remedy such deficiencies, we propose a diagonalized low-rank learning (DLRL) model for HSI classification in this study. Specifically, a classwise regularization is used to capture the classwise block-diagonal structure of low-rank representation, which can further cluster the represented HSI pixels from one class into the same subspace and extract features with well-ordered distribution. Such a regularization assists to easily and correctly classify HSIs. In addition, we combine sparsity and collaboration to extract more discriminative features for guaranteeing high information utilization, i.e., a tradeoff of sparsity and collaboration is sought to acquire both correlations among HSI pixels and characteristics of each pixel. By this way, rich information in the HSI can be fully used for good feature extraction. Further, the estimated feature representation is used as an input to the support vector machine (SVM) classifier for HSI classification. Extensive experiments have been done to validate that the proposed DLRL method achieves much classification performance in contrast to several state-of-the-art algorithms.

Deep learning is a popular and effective technique for the hyperspectral image (HSI) classification. Current deep learning-based methods have numerous free parameters to be trained. They may be unavailable once lacking training samples. In addition, these approaches only use the features from the deepest layer and exclude shallow features, which is a kind of information loss. To remedy such deficiencies, in this work, we construct a novel deep encoder with kernel-wise Taylor series (EKTS) for the HSI classification. More specifically, we introduce the Taylor series to approximate the role of deep networks for feature extraction. Because the original Taylor series is linear, the kernel theory is used to build the kernel-wise Taylor series to encode the HSI data and extract deep nonlinear features. Furthermore, an alternating iterative optimization strategy is developed to obtain the outputs of all layers of the built deep encoder. Subsequently, we stack the outputs of all layers to obtain the final features that integrate both shallow and deep features. At last, the support vector machine (SVM) is adopted to deal with the obtained final features so that label results can be predicted. Unlike current deep learning methods, our EKTS has no free parameters to be trained and combines the advantages of both shallow and deep features to predict labels. Sufficient experimental analysis has been performed to verify the greater classification performance of our EKTS method compared with many state-of-the-art approaches.

For most hyperspectral image (HSI) classification methods, each feature code is usually individually learned, and which is susceptible to noise. To remedy such deficiency, we propose a binary feature representation method with context -aware attention (BFCA) for HSI classification in this study. In this model, local spectrum modules (LSMs) are first built by segmenting each HSI pixel vector into several parts and computing the differences between the central value and its neighborhoods in each part. The LSMs can observe the changes of spectral values, and can fully find and use spectral information for HSI classification. Second, projections with hash functions are learned, which aim to map and quantize each LSM into a binary vector. Each binary vector is limited with one shift between 0 and 1 to guarantee spectral context-awareness. Once projection matrix obtained by optimizing the proposed model, binary vectors all samples are calculated, which are further classified by SVM. Unlike current approaches, our BFCA exploits spectral contextual information of LSMs to improve the stability and robustness of feature representation and HSI classification. Extensive experiments have been given to validate the superiority of our BFCA.

Hyperspectral image (HSI) classification is a widely focused topic. Existing methods export discriminative features with low-quality distribution, which may induces classification performance degeneration. To remedy this deficiency, we propose a group-aware low-rank representation (GAL-RR) model to classify HSIs in this paper. Specifically, a group-wise regularization is introduced to capture the block-diagonal group structure of low-rank representation. With such regularization, the HSI pixels from one class are well clustered into the same subspace. In other words, the HSI feature representation is guaranteed with high-quality distribution and good discriminative information. In order to solve this built non-convex model, we design a proximal alternating optimization scheme. Furthermore, the SVM classifier is used to classify these low-rank representation forms. Extensive experiments are provided in this paper to verify the effectiveness and superiority of the proposed GALRR classification algorithm.

An efficient fusion method of infrared and visible images is proposed based on the parallel saliency features. The method integrates significant feature information from the source images of multiple imaging modalities into a single fused image in a multi‐scale domain, while suppressing visual artefacts and retaining more detail and texture information. First, the input images are decomposed into two‐scale image representations, namely the base and detail layers, using a Gaussian filter. Second, the parallel saliency features of the high contrast and detail textures are captured to acquire the saliency maps. The contrast saliency weight map of the base layers based on the weighted local intensity energy aims to highlight the salient targets in infrared images and preserve the high‐intensity regions in visible images, while the detail saliency weight map of the detail layers using the structure tensor to extract the detail texture information. Finally, the final image is reconstructed by the fused base and detail layers. Sufficient experimental results convincingly demonstrate that the presented method can achieve a comparable or superior performance compared with several state‐of‐the‐art fusion methods via subjective assessments and objective evaluations, and it is more suitable for the practical applications due to the high computing efficiency.

Edge‐preserving filters have been applied to Multi‐Scale Decomposition (MSD) for fusion of infrared and visible images. Traditional edge‐preserving MSDs may hardly make satisfied structural separation from details to cause fusion performance degradation. To suppress this challenge, the authors propose a novel fusion of infrared and visible images with Gaussian smoothness and joint bilateral filtering iteration decomposition (MSD‐Iteration). This method consists of three steps. First, source images are decomposed by the Gaussian smoothness and joint bilateral filtering iteration. The implementation includes the fine‐scale detail removal with Gaussian filtering, edge and structure extraction with joint bilateral filtering iteration, and detail obtaining at multi‐scales. The decomposition has edge‐preserving and scale‐aware properties to improve detail acquisition. Second, rules are designed to conduct the layer combination. For the rule of base layers, saliency maps are constructed by Laplacian and Gaussian low‐pass filters to calculate initial weight maps. A guided filter is further applied to determine final weight maps for the combination. Meanwhile, they use the regional average energy weighting to obtain decision maps at multi‐scales by constructing intensity deviation to combine detail layers. Third, they implement the reconstruction with the combined layers. Sufficient experiments are presented to evaluate MSD‐Iteration, and experimental results validate the superiority of the authors’ method.

Infrared and visible images fusion based on edge‐preserving can improve the fused result in a clear outline. However, there exists the performance degradation caused by some edges in the data which are smaller than the level of the noise with traditional edge‐preserving decomposition. To remedy such deficiency, a method based on bitonic filtering decomposition and sparse representation is proposed for fusion of infrared and visible images. The bitonic filtering decomposition and sparse representation (BFSR) method consists of three steps: multi‐scale bitonic filtering decomposition, mergence of base layers and detail layers, and reconstruction of the fused result. Compared with traditional image fusion based on edge‐preserving, data‐level‐sensitive parameters are not included in the BFSR method, which can locally adapt to the signal and noise levels in an image. Moreover, the sparsity of images for fusing details is used in the BFSR method, which can analyse the explanatory factors hidden behind the data. As demonstrated in the experimental results, the proposed BFSR method achieves much fusion performance compared with other commonly used image fusion methods.