In the context of the widespread application of unmanned aerial vehicles (UAVs) across various industries, effective path planning in three-dimensional (3-D) environments has emerged as a crucial challenge in their deployment. In real-world applications, UAV path planning missions are usually converted into multi-objective tasks and solved using evolutionary computation, where the optimal flight path should consider both the overall flight route length and potential terrain threat. However, the existing methods usually treat complete paths as individuals, and this modeling approach lacks the evaluation of track points and is unable to fully reflect the quality of the path. In addition, as the quantity of track points increases, it is difficult for the traditional genetic crossover operator to quickly converge to the global optimum in complex high dimensional objective space. Thus, in this paper, we propose a UAV 3-D path planning method utilizing the global-local collaborative modeling approach with a decomposition-based method (P2GLCM). In the P2GLCM method, the global objective functions and the local objective functions are used to evaluate the path and track points, respectively, to achieve accurate modeling. In addition, to efficiently utilize the high-quality track points in the candidate paths, a dominance relationship approach is introduced to guide the generation of offsprings in a point-by-point manner, improving the search capability in complex objective space. The experimental results on 3-D environments with unified representation of voxels demonstrate that P2GLCM outperforms current methods in convergence and effectiveness.

The limitations of remote sensing sensor technology make it difficult to simultaneously capture earth observation information presented in different forms within a single remote sensing image. Acquiring more plentiful target information through fusion technology has therefore remained a research hotspot. The fusion of multispectral (MS) and synthetic aperture radar (SAR) imagery integrates spectral and backscatter information, thereby improving land cover (LC) classification effects. However, current pixel-level fusion methods often fail to adequately account for the model differences between SAR and MS images, leading to spectral-spatial inconsistencies and severe degradation from speckle noise. To address this problem, A fusion method is proposed based on Bidirectional Aggregation of Saliency in the Human Visual System (BASHVS). First, the SAR and MS images are decomposed into base and detail layers using a synchronized anisotropic diffusion algorithm. Subsequently, the detail layer is fused using a New Sum of Modified Anisotropic Laplacian (NSMAL) algorithm. Finally, for base layer fusion, pixel saliency and structural saliency are extracted bidirectionally. The BASHVS is compared with 16 existing fusion methods using 10 evaluation metrics. The results demonstrate that BASHVS achieves the best comprehensive performance and significantly improves the visual quality of the fused images. LC classification using BASHVS fused images shows an average increase of 1.050% in overall accuracy and 0.014 in Kappa coefficient compared to the original MS images, confirming its advantage for LC classification. The source code of BASHVS is shared at https://github.com/CHUANGL8346/BASHVS.

After earthquake disasters, ensuring that emergency rescue operations reach affected areas as quickly as possible, with the aim of maximizing the rescue of lives and property and minimizing secondary disaster losses, is the primary task of post-earthquake emergency response. Remote sensing technology, characterized by its large-scale coverage, non-contact nature, and rapid response capabilities, provides valuable information for disaster area assessment and post-earthquake emergency response path planning. However, existing post-disaster emergency path planning studies often fail to utilize this rich information. Traditional path planning algorithms insufficiently consider the disaster situation, hindering the efficient utilization of rescue forces and resources. To address these challenges, this study proposes a disaster-aware path planning method based on reinforcement learning for post-earthquake emergency response (P2DARL). The P2DARL method utilizes real disaster information provided by high-resolution remote sensing images and models it in a reinforcement learning environment. This allows the intelligent agent to learn optimal post-earthquake emergency response path planning strategies through continuous trial-and-error interactions with the environment. This approach facilitates the optimal allocation of rescue resources and enhances rescue efficiency. Experiments using real earthquake disaster images demonstrate that the P2DARL method effectively plans paths that cover more affected centers in mid-short distance scenarios, maximizing rescue efficiency and significantly reducing casualties and economic losses resulting from earthquake disasters.

Hyperspectral image (HSI) classification has become a core task in hyperspectral remote sensing interpretation, with deep learning dominating due to its ability to learn hierarchical features without manual engineering. As the model complexity has grown, manual design limitations have prompted a shift to automated approaches such as differentiable architecture search (DARTS), where the architectures are optimized for greater accuracy and efficiency. However, applying gradient-based neural architecture search (NAS) methods directly to hyperspectral classification presents several challenges. Regarding search space design, there is a lack of lightweight operators that can mitigate the spectral variability, spatial heterogeneity, and scale differences inherent in hyperspectral imagery. In terms of search strategy, the traditional DARTS approach directly derives the topology from operation weights, which can lead to suboptimal topological structures, and thus affects the performance of the network in HSI classification. In this article, to address these issues, we propose L3M, which is a lightweight multiscale and multiattention HSI classification network based on multistage search. The proposed approach introduces a novel lightweight operator to address the spectral variability, spatial heterogeneity, and scale differences in HSIs. The operation search and topology search are also decomposed into a multistage process to prevent a suboptimal network by searching for and determining the topological order of the candidate operations in a predefined operation space. L3M was validated on four public datasets, where the proposed model demonstrated a superior classification performance, compared to other lightweight models, while maintaining a low parameter count, low model complexity, and high inference speed.

Night object tracking (NOT) is aimed at tracking objects under low-illumination conditions at night. Existing works concentrate on thermal infrared modality, while some RGB and thermal infrared (RGB-T) data also contain night scenes. However, night scenes in these datasets are mostly well-lit, making it challenging to fully cover low-illumination scenarios. In this article, we focus on the NOT task and build up a novel low-light visible and thermal infrared (LOL-T) multimodal benchmark dataset for NOT-156. To achieve night vision, we design a common-aperture LOL-T camera by integrating a highly dynamic low-light visible imaging sensor with a thermal infrared sensor in a common aperture optical system. The proposed dataset consists of 156 video sequences and a total of 170k annotated frames, including various low-illumination night scenes such as dark rooms, streets, corridors, and so on. Compared with existing datasets, NOT-156 has more comprehensive and distinctive attributes (thermal variation, noise, high illumination overexposure, etc.). Comprehensive experiments are carried out to evaluate the performance of the advanced visible, infrared, and visible-thermal trackers on the proposed NOT-156 dataset. The authors believe that NOT-156 has great potential in the application and development of night vision. The dataset will be made available athttp://rsidea.whu.edu.cn/NOT156_dataset.htm.

To address the limitations of existing deep learning-based hyperspectral image compression methods in accurately modeling the rate-distortion problem, we propose a coupled multi-scale attention spatial-spectral high-fidelity compression network (MSAHiFiC). MSAHiFiC employs a super-prior network to estimate bitrate and guide rate-distortion optimization, enhancing performance under constrained bitrate conditions. A multi-scale spectral attention module is introduced to capture spectral dependencies across varying inter-band distances and preserve key spectral features during downscaling. A spectral fidelity term is further incorporated into the loss function to improve reconstruction accuracy. Experiments on three benchmark hyperspectral datasets—HySpecNet-11k, XiongAn, and WHU-Hi—demonstrate that MSAHiFiC outperforms state-of-the-art methods by achieving 5% higher spectral fidelity and 6% improvement in reconstruction accuracy under a low bitrate of 0.5 bpp.

When facing natural disasters like sudden floods at night, due to sudden nature disasters, high timeliness of rescue information and complexity and diversity of rescue environment, it is difficult for ground personnel to perform rescue operations in disaster area in time. Remote sensing UAV technology plays an escalating role in disaster relief due to fast response and high flexibility advantages. However, with low nighttime visibility level, complex post-disaster environment, and numerous obstructions, traditional UAV’s visible light remote sensing struggles to achieve accurate rescue detection at night. Therefore, the multimodal detection methods are investigated using low-light and infrared modalities, exploring the integration of data fusion and detection in night rescue applications, and examine the advantages and disadvantages of different anomaly detection methods here. This paper provides the following contributions: 1) a fully-annotated low-light infrared co-observation multimodal remote sensing image dataset for nighttime emergency rescue, termed MRSI-NERD; 2) a benchmark test for most state-of-the-art unsupervised anomaly detection methods to thoroughly explore their capability in extracting useful information from normal samples; and 3) a low-light infrared bimodal fusion method based on frequency domain feature decomposition, which enhances the performance of detectors. The performance of eight types of traditional or deep learning-based detection methods on the MRSI-NERD dataset is reported, including metrics such as ROC-AUC, FPR, TPR, etc. Additionally, a comprehensive analysis of the principles and performance of each category of detection methods is provided.

Multispectral and SAR image fusion is one of the key technologies to improve image quality. The fusion method of multi-scale decomposition includes two aspects: the decomposition of image and the design of fusion rule. There are some problems in the traditional decomposition methods, such as gradient reversals, halos and other artifacts, and limited scale separation of space overlapping features. In addition, the quality of fusion images is greatly affected by the fusion rule design. Therefore, a novel method based on least squares optimization rolling guidance filtering for multi-scale decomposition is proposed. All gradients of rolling guidance filtering are optimized by least squares to suppress artifacts such as gradient inversion, and then combined with Gaussian filtering for image decomposition to eliminate interference texture and speckle noise while preserving edge details. At the same time, the decomposition of image is extended to multi-scale space to achieve scale separation of space overlapping features, which is convenient for multi-level fusion of image features. In the end, based on the scale of decomposition, the results fall into three layers, and coupled neural P system and other rules are designed for different layers of information fusion. The results indicate that this method has good visual effect and outperforms all the other comparison methods on nine evaluation indexes.

Hyperspectral remote sensing image (HSI) has the unique advantages of spectral continuity as well as synchronous acquisition of both image and spectra of objects, which can achieve precise classification. For HSI classification, the deep learning (DL) methods have been fully developed due to its powerful layer by layer nonlinear feature learning ability, which is currently the mainstream method. However, current classification networks often refer directly to the fixed framework of natural image processing, and usually only focus on accuracy as an optimization goal, resulting in poor HSI data adaptation capability and parameter redundancy. In addition, HSIs usually have dozens of types of ground objects, with similar and mixed spectra, and the phenomenon of overlapping clusters is aggravated, making it difficult to distinguish similar objects. In this paper, a lightweight precise classification network (LPCN) for HSI based on multi-objective optimization was proposed. In LPCN, traditional fixed architectures are avoided, hierarchical lightweight search spaces are designed, and spectral attention mechanisms for similar objects are incorporated to improve the separability. Moreover, the accuracy and parameter function of the classification network are independently modeled and optimized simultaneously for reducing the amount of network parameters. The effectiveness of LPCN is proved by experiments with three HSI datasets, with 16, 16, and 22 types of ground objects, respectively.

Remote sensing image scene classification methods based on deep learning have been widely studied and discussed. However, most of the network architectures are directly reliant on natural image processing methods and are fixed. A few studies have focused on automatic search mechanisms, but they cannot weigh the interpretation accuracy and the parameter quantity for practical application. As a result, automatic global search methods based on multiobjective evolutionary computation have more advantages. However, in the ranking process, the network individuals with large parameter quantities are easy to eliminate, but a higher accuracy may be obtained after full training. In addition, evolutionary neural architecture search methods often take several days. In this article, in order to solve the above concerns, we propose an efficient multiobjective evolutionary automatic search framework for remote sensing image scene classification deep learning network architectures (E2SCNet). In E2SCNet, eight kinds of lightweight operators are used to build a diversified search space, and the coding connection mode is flexible. In the search process, a large model retention mechanism is implemented through two-step multiobjective modeling and evolutionary search, where one step involves the "parameter quantity and accuracy," and the other step involves the "parameter quantity and accuracy growth quantity." Moreover, a super network is constructed to share the weight in the process of individual network evaluation and promote the search speed. The effectiveness of E2SCNet is proven by comparison with several networks designed by human experts and networks obtained by gradient and evolutionary computing-based search methods.

Hyperspectral remote sensing images (HSI-RS) capture the fine spectral information of terrain, but also bring large data volume, which poses a huge challenge to the storage, transmission and even processing. HSI-RS compression achieves image critical information representation lossless or lossy with high fidelity through effective spatial-spectral redundancy removal. As an extension of the research field of traditional natural image compression technology, there has not yet been a study concluding and comparing the various HSI-RS compression methods. The paper first introduces and explains the differences between the HSI-RS compression and natural image compression. Then the methods for HSI-RS compression are investigated and can be divided into five main categories: 1) transformed-based; 2) prediction-based; 3) dictionary-based; 4) decomposition-based; 5) learning-based. Experiments are carried out to make a comparison on the fidelity and rate distortion performance. At the end of the paper, the future development directions for HSI-RS compression are discussed.

Planning a practical three-dimensional (3-D) flight path for unmanned aerial vehicles (UAVs) is a key challenge for the follow-up management and decision making in disaster emergency response. The ideal flight path is expected to balance the total flight path length and the terrain threat, to shorten the flight time and reduce the possibility of collision. However, in the traditional methods, the tradeoff between these concerns is difficult to achieve, and practical constraints are lacking in the optimized objective functions, which leads to inaccurate modeling. In addition, the traditional methods based on gradient optimization lack an accurate optimization capability in the complex multimodal objective space, resulting in a nonoptimal path. Thus, in this article, an accurate UAV 3-D path planning approach in accordance with an enhanced multiobjective swarm intelligence algorithm is proposed (APPMS). In the APPMS method, the path planning mission is converted into a multiobjective optimization task with multiple constraints, and the objectives based on the total flight path length and degree of terrain threat are simultaneously optimized. In addition, to obtain the optimal UAV 3-D flight path, an accurate swarm intelligence search approach based on improved ant colony optimization is introduced, which can improve the global and local search capabilities by using the preferred search direction and random neighborhood search mechanism. The effectiveness of the proposed APPMS method was demonstrated in three groups of simulated experiments with different degrees of terrain threat, and a real-data experiment with 3-D terrain data from an actual emergency situation.

Due to the unavoidable influence of sparse and Gaussian noise during the process of data acquisition, the quality of hyperspectral images (HSIs) is degraded and their applications are greatly limited. It is therefore necessary to restore clean HSIs. In the traditional methods, low-rank and sparse matrix decomposition methods are usually applied to restore the pure data matrix from the observed data matrix. However, due to the fact that the optimization of the${l}_{0}$-norm for the sparse modeling is a nonconvex and NP-hard problem, convex relaxation and regularization parameters are usually introduced. However, convex relaxation often leads to inaccurate sparse modeling results, and the sensitive regularization parameters can lead to unstable results. Thus, in this article, to address these issues, an accurate multiobjective low-rank and sparse denoising framework is proposed for HSIs to achieve accurate modeling. The${l}_{0}$-norm is directly modeled as the sparse noise and is optimized by an evolutionary algorithm, and the denoising problem is converted into a multiobjective optimization problem through simultaneously optimizing the low-rank term, the sparse term, and the data fidelity term, without sensitive regularization parameters. However, since the low-rank clean image and sparse noise of the HSI are encoded into a solution, the length of the solution is too long to be optimized. In this article, a subfitness strategy is constructed to achieve effective optimization by comparing the objective function values corresponding to each band for each solution. The experiments undertaken with simulated images in 11 noise cases and four real noisy images confirm the effectiveness of the proposed method.

Hyperspectral object tracking is aimed at tracking targets by using both the spatial information and abundant spectral information, overcoming the drawbacks of traditional RGB tracking in complex scenarios, such as the low resolution or background clutter. However, the current hyperspectral object tracking methods usually have a high computational complexity, due to the huge data volume, making them difficult to apply to real-time applications on edge devices (e.g., robots, unmanned aerial vehicles, and satellites) with limited computational resources. In this paper, a lightweight dual Siamese network for onboard hyperspectral object tracking—termed SiamOHOT—is proposed for real-time and onboard tracking. Specifically, a joint spatial-spectral knowledge distillation method is proposed to teach a lightweight dual Siamese tracker to learn from a deep tracker— SiamHYPER—so that the number of parameters can be compressed to improve the computational efficiency. In addition, a deep learning inference optimizer is introduced to fuse the layers with similar functions and quantify the parameters of the network, to further promote the processing speed when deployed on an embedded platform. The proposed lightweight model was verified using the 2021 WHISPERS Hyperspectral Object Tracking Challenge dataset, and achieved a superior efficiency and accuracy. In addition, a prototype system was built integrating a snapshot hyperspectral imager, the SiamOHOT tracking algorithm, and an artificial intelligence edge device (NVIDIA Jetson Xavier NX), to realize real-time imaging and tracking. The inference speed of the optimized SiamOHOT network is nearly doubled when compared to the teacher model on the prototype system.

Hyperspectral remote sensing band selection picks out characteristic feature combination to weaken the strong correlation caused by spectral continuity. However, it is difficult for traditional methods with fixed strategies to search the entire space and make adjustments for the optimization process. Thus, the solutions obtained can be mostly local optima. In this paper, a novel adaptive multi-strategy particle swarm optimization for hyperspectral image remote sensing band selection (AMSPSO_BS) is introduced to obtain a subset solution suitable for classification. The problem is modeled as an effective fitness function, and the quotient of the linear discriminant value and the mean mutual information (LD/MMI) is used to remove the redundancy between bands. The randomly generated solutions are then encoded to form a population, which rely on various particle update strategies (PUS) with different reference positions for updating. During the particle motion, the effect of each strategy on population evolution is considered comprehensively and reflected in the change of selection probability. And the motion parameters are dynamically adjusted to balance the global and local capabilities. Four hyperspectral remote sensing image datasets were utilized to conduct band selection experiments, to confirm the effectiveness of AMSPSO_BS.

Deep learning based remote sensing scene classification methods have become a research hotspot, but they can not fully mine the image information due to the architecture comes directly from natural image. The automatic search method-based network architecture has then attracted a lot of attention benefits by its ability to independently learn the network structure suitable for remote sensing data. However, in the process of search and sorting, slightly larger models with better performance after full training are often eliminated due to insufficient training. Moreover, the methods often spend a lot of time searching. In this paper, a micro network architecture evolutionary search method is proposed (MAE-Net), the contributions are reflected in the slightly larger model retention mechanism by two-layer multi-objective functions and the super network mechanism used to reduce search time through weight sharing. The effectiveness is proved by comparison with human expert and search based networks on NWPU45 dataset.

Remote sensing image data clustering is a tough task, which involves classifying the image without any prior information. Remote sensing image clustering, in essence, belongs to a complex optimization problem, due to the high dimensionality and complexity of remote sensing imagery. Therefore, it can be easily affected by the initial values and trapped in locally optimal solutions. Meanwhile, remote sensing images contain complex and diverse spatial-spectral information, which makes them difficult to model with only a single objective function. Although evolutionary multiobjective optimization methods have been presented for the clustering task, the tradeoff between the global and local search abilities is not well adjusted in the evolutionary process. In this article, in order to address these problems, a multiobjective sine cosine algorithm for remote sensing image data spatial-spectral clustering (MOSCA_SSC) is proposed. In the proposed method, the clustering task is converted into a multiobjective optimization problem, and the Xie-Beni (XB) index and Jeffries-Matusita (Jm) distance combined with the spatial information term (SI_Jm measure) are utilized as the objective functions. In addition, for the first time, the sine cosine algorithm (SCA), which can effectively adjust the local and global search capabilities, is introduced into the framework of multiobjective clustering for continuous optimization. Furthermore, the destination solution in the SCA is automatically selected and updated from the current Pareto front through employing the knee-point-based selection approach. The benefits of the proposed method were demonstrated by clustering experiments with ten UCI datasets and four real remote sensing image datasets.

Feature selection is an effective way to handle the strong correlation of hyperspectral image data by screening the significant features, and is generally accepted to be a multiobjective optimization problem. Nevertheless, due to the randomness of the strategies and the ambiguity of the optimization directions, the existing multiobjective evolutionary optimization based feature selection methods can suffer from inefficient search and loss of search space with promising solutions when faced with the high-dimensional and multi-peak search space. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) employs a decomposition framework to provide exact guidance for the optimization directions. Unfortunately, random operators are still used, leading to inadequate local optimization. Thus, evolutionary strategies with search preference such as clonal selection may be necessary for local search. In this paper, a novel decomposition-based multiobjective clonal selection algorithm for feature selection (MOCSA/D_FS) is proposed to obtain a feature subset with a superior classification performance. In MOCSA/D_FS, the information entropy and the ratio of the relative scatter value and mutual information are utilized as two objective functions to evaluate the information amount and redundancy. A series of subproblems are then obtained by decomposing the multiobjective problem through weight vectors, with anl2-norm constraint used to balance the search space. Subsequently, a clonal selection method with search space preference performs a detailed local search on each subproblem, which can fully exploit the potential optimal space. The effectiveness and generalizability of the proposed method was confirmed by experiments on four hyperspectral remote sensing image datasets.

Thermal infrared (TIR) remote-sensing imagery can allow objects to be imaged clearly at night through the long-wave infrared, so that the fusion of thermal infrared and visible (VIS) imagery is a way to improve the remote-sensing interpretation ability. However, due to the large radiation difference between the two kinds of images, it is very difficult to match them. One of the most important issues is the lack of comprehensive consideration of the modality-specific information and modality-shared information, which makes it difficult for the existing methods to obtain a modality-invariant feature representation. In this article, a cross-modality image matching network, which we refer to as CMM-Net, is proposed to realize thermal infrared and visible image matching by learning a modality-invariant feature representation. First, in order to extract the modality-specific features of the imagery, the framework constructs a shallow two-branch network to make full use of the modality-specific information, without sharing parameters. Second, in order to extract the high-level semantic information between the different modalities, modality-shared layers are embedded into the deep layers of the network. In addition, three novel loss functions are designed and combined to learn the modality-invariant feature representation, that is, the discriminative loss of the non-corresponding features in the same modality, the cross-modality loss of the corresponding features between different modalities, and the cross-modality triplet (CMT) loss. The multimodal matching experiments conducted with ground- and airborne-based thermal infrared images and visible images showed that the proposed method outperforms the existing image matching methods by about 2% and 6% for the ground and airborne images, respectively.

The deep learning (DL)-based scene classification methods have been obtained the remarkable attention for the high spatial resolution remote sensing (HRS) imagery. However, from one aspect, the existing DL methods in HRS image scene classification are usually the variations of the natural image processing methods and often the inherent network structures; from another aspect, the strenuous and significant efforts have been devoted to the design of relevant network structures by human experts. In this paper, learning from the natural evolution, the deep neural network is expected to be globally evolved by the machine for automatically adapting the structure of the HRS imagery, a multi-objective neural architecture search based HRS image scene classification method is proposed (RSSM-Net). The two objectives of minimizing a classification error and the computational complexity have been simultaneously optimized through the evolutionary multi-objective method, the competitive neural architectures in a Pareto solution set are then obtained. The effectiveness is proved by the experiment of the UC Merced dataset with several networks designed by human experts.

Feature selection is an effective way to reduce the data dimensionality of hyperspectral imagery and obtain a better performance in the subsequent applications, such as classification. The ideal approach is to obtain the optimal tradeoff between two criteria for hyperspectral image feature selection: 1) information preservation and 2) redundancy reduction. However, constructing a hyperspectral feature selection model for the above two criteria is difficult due to the complexity of hyperspectral imagery. Although evolutionary multiobjective optimization methods have been recently presented to simultaneously optimize the above criteria, they cannot control the global exploration versus local exploitation capabilities in the search space for the hyperspectral feature selection problem. Thus, in this article, a novel discrete sine cosine algorithm (SCA)-based multiobjective feature selection (MOSCA_FS) approach is proposed for hyperspectral imagery. In the proposed method, a novel and effective framework of multiobjective hyperspectral feature selection is designed. In the framework, the ratio between the Jeffries-Matusita (JM) distance and mutual information (MI) is modeled to minimize the redundancy and maximize the relevance of the selected feature subset. In addition, another measurement - the variance (Var) - is applied for maximizing the information amount. Furthermore, to resolve the discrete hyperspectral feature selection problem, a novel discrete SCA is first proposed, which enhances the selection of the ideal feature subset. The effectiveness and universality of the proposed method was verified by experiments with ten University of California at Irvine (UCI) data sets, five hyperspectral image data sets, and one spectral data set of typical surface features.

Hyperspectral images (HSIs) are typical high-dimensional and complex data. As such, the clustering of HSIs is a challenging task. Out of the motivation to find the low-dimensional structure representation of the high-dimensional data, sparse subspace clustering (SSC) methods have been proposed in recent studies. Sparse representation is an important technique in SSC, which is aimed at obtaining the sparse coefficient matrix of the HSI data. Generally speaking, the acquisition of the sparse coefficient matrix is an ill-posed problem, and the existing methods introduce an extra condition as a regularization term to resolve it. However, the regularization parameter is determined manually, which is difficult and lacks self-adaptability. Hence, in this article, a multi-objective SSC method for hyperspectral imagery is proposed, which simultaneously optimizes the sparse term and the data fidelity term. In addition, the spatial structure information of the HSIs is often neglected in the processing model, and thus, a spatial prior term, as the third optimization objective function, is also tested in this article. As a result, there is no need to manually set a regularization parameter. Furthermore, by using the l0norm as the sparse term, this reduces the error caused by the convex relaxation of the other norms. In the proposed method, a multi-objective optimization model is first used to acquire the sparse coefficient matrix, in which a strategy for constructing the dictionary is proposed for more precise and efficient multi-objective optimization. In addition, a knee point-based selection method is utilized to automatically select the optimal sparse representation solution from the Pareto front. The adjacency matrix is then constructed according to the sparse coefficient matrix. Finally, a spectral clustering method is used to obtain clustering results. Experiments undertaken with four HSI data sets confirm the effectiveness of the proposed method.

The tailings reservoir is an inevitable part of the production of metal mines, and due to it is usually the accumulation of waste residue and waste water, the risk source of artificial debris flow with high potential energy has been formed and the environmental risk cannot be underestimated. Thus, it is an important disaster and environmental protection project for the mining enterprises. However, the existing methods cannot conduct a comprehensive disaster and environmental monitoring, considering the remote sensing technology is an effective method for the large-scale monitoring, thus, this global monitoring will be carried out through a novel UAV-ground hyper-spectral joint observation and processing, where the UAV hyper-spectral image, the ground hyper-spectral data of the water and waste residue, and water quality testing report will be used. In addition, the study area is in Xinjiang, the Belt and Road.

For hyper-spectral image band selection, there are two main key concerns, which are the curial information preservation and the redundancy information reduction. Since the two objectives are contradictory, the single-objective based band selection methods are usually unsatisfactory, thus, a superior approach which can obtain a trade-off between them is needed. In order to address this problem, an evolutionary computation method called sine cosine algorithm which has the capabilities of the global exploration and the local exploitation is applied, and its multi-objective discrete version is designed for multi-objective band selection. In addition, in this paper, two novel measures are utilized for meeting the requirements. The effectiveness of the proposed method is confirmed by the experimental results obtained with two real hyper-spectral images.

Clustering of remote sensing imagery is a tough task due to the particular and complex structure of remote sensing images and the shortage of known information. In this paper, we propose a fully automatic spectral-spatial fuzzy clustering method using an adaptive multiobjective memetic algorithm (AMOMA) for multispectral remote sensing imagery. This approach is made up of two automatic layers: an automatic determination layer and an automatic clustering layer. The first layer seeks the optimal number of clusters through a self-adaptive differential evolution algorithm. The second layer then takes advantage of the AMOMA for spectral-spatial clustering using the optimal number of clusters. The knee point from the Pareto front is then selected through the angle-based method in every generation, and we then compare the knee points between generations to output the final optimal solution. The effectiveness of the proposed method is verified by the experimental results obtained with three remote sensing data sets.