The spectral variability problem is a big challenge in hyperspectral unmixing. Endmember bundles have been used to address the spectral variability problem by adopting a bundle of endmember spectra to represent one kind of endmember class. Existing endmember bundle extraction algorithms mainly rely on the convex geometry assumption and integrate endmembers from image subsets as endmember bundles. On the one hand, they suffer from high risk of bad performance for real hyperspectral scene where the convex geometry assumption is not satisfied. On the other hand, endmember variabilities within image subsets are neglected, which may lose representative endmembers. In this paper, we propose a novel endmember bundle extraction framework to capture endmember variability by introducing a dynamic optimization mechanism. Endmember bundles are obtained by dynamically minimizing the root-mean-square error between original pixels and reconstructed pixels through an iteration process; and a particle swarm optimization method is introduced to find the optimal endmember combination in each iteration. The proposed endmember bundle extraction framework imposes no assumption on the hyperspectral data distribution and has great potential to be used in complex hyperspectral scenes. Experimental results on two real hyperspectral datasets demonstrate that the proposed algorithm is able to obtain endmember bundles that well express the spectral variability, and the performance of the proposed algorithm is competitive with the state-of-the-art algorithms.

Change detection with remote sensing images (RSIs) plays an important role in the community of remote sensing applications. However, when change detection is conducted with hyperspectral remote sensing images (HRSIs), how to measure the change magnitude between bitemporal HRSIs becomes challenging due to the high dimension of HRSIs. In this article, a novel Distribution Distance based on Inconsistent Adaptive Region (D2IAR) change detection approach is proposed to measure the change magnitude between bitemporal HRSIs for improving the performance of change detection with HRSIs. First, a band selection algorithm called optimal neighborhood reconstruction is employed to reduce the dimensions of HRSIs. Then, an adaptive region around each pixel is generated to explore the contextual feature around each pixel, and kernel density estimation is suggested to estimate the spectral distribution of the pixels within an adaptive region. A distribution distance is defined based on the adaptive region to measure the change magnitude between bitemporal HRSIs. Finally, the change magnitude between pairwise adaptive regions is measured by the proposed distance between the pairwise distributions. Experimental results based on four datasets and comparisons with eight methods indicated the feasibility and superiorities of the proposed D2IAR-based change detection approach with HRSIs. The improvement rates are approximately 0.13%-24.04% for overall accuracy. The code and datasets can be available at: https://github.com/ImgSciGroup/2024-HSICD.

Sample augmentation is effective for improving the supervised performance of land-cover classification with hyperspectral remote sensed image (HRSI) when the training samples are limited. However, numerous existing methods have neglected, considering the interclass-imbalance problem in the process of sample augmentation. In this work, new sample generation and sample balance strategies were promoted and simultaneously combined into an iteration for balancing and improving classification performance with HRSI. First, a sample augmentation with superpixel’s constraint (SASC) is designed to augment the initial training samples set to avoid the overfitting of a sample generation neural network. Second, sample generation based on generative adversarial network (SGGAN) was proposed to generate samples for each class. Then, the proposed SASC, SGGAN, and a pattern recognition neural network named 3 dimensions-convolutional neural network (3-D-CNN) are combined into an iterative classification process called iterative sample generation and balance (ISGB) for balancing the user’s accuracy for each class and optimizing the classification performance. Experiments on four widely used HRSIs are performed. The results when compared with eight state-of-the-art methods based on few-shot learning and generative adversarial network (GAN) efficiently demonstrate the feasibility and superiorities of the proposed approach for improving land-cover classification performance when the initial samples are limited. Moreover, the comparisons of the standard deviation of the user’s accuracies (SDUA) demonstrated the balancing ability of the proposed approach. The code of the proposed approach is available athttps://github.com/ImgSciGroup/ISGBA.

Synthetic Aperture Radar Interferometry (InSAR) technique shows huge potential in the deformation monitoring of coastal seawalls, which is essential to guarantee the safety of people's lives and social property. However, a single view InSAR dataset can only identify satellite-oriented point-like targets (PTs), and the one-dimensional deformation would cause deformation estimation deviation and potential risk undetected. This study developed a multi-view InSAR analysis method to present the first 3D deformation monitoring and structural-level deformation analysis of coastal seawalls. The density and accuracy of selected PTs are improved by a spatial-temporal similarity-based multi-view PTs joint extraction method, and the deformation accuracy is improved through a parallax entropy weighted 3D deformation inversion method. Taking the Donghai Seawall as an example, the 3D deformation of the seawall is revealed, indicating a rapid-slow-steady deformation process.

To explore the airspace structure and safety performance of unmanned aerial vehicle (UAV) system based on spatial digital twins (DTs), the study introduces DTs technology, and combines convolutional neural network (CNN) algorithm with UAV autonomous network. The DTs system of UAV is constructed by using wireless communication technology, and its security performance is simulated. The results show that in the analysis of the system packet loss rate, it is found that with the increase of the acquisition points, the amount of transmitted data only increases slightly, but the packet loss rate does not change significantly. In the analysis of the network performance of the unmanned aircraft system, it is found that the node energy-based weighted clustering algorithm (EWCA) can be used to increase the life of the overall network and enhance its availability by rationally controlling the number of nodes and the number of switching between clusters. As the number of nodes increases, the minimum survival time of each clustering algorithm decreases linearly. When the number of nodes is less than 600, the growth rate of cluster head is higher; When the number of nodes is more than 600, the curve growth is relatively smooth. In the analysis of the probability of network safety interruption, it is found that using the model constructed, when the energy acquisition coefficient is close to 0.5, the energy conversion efficiency is higher, the signal-to-noise ratio is larger. Also, when the number of intermediate nodes is increased to 10, the UAV has the best network safety performance. Therefore, through the research, it is found that the UAV DTs system constructed can significantly improve the safety performance of the UAV during its airspace flight. It can provide experimental references for the widespread application of the UAV in the later period.