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.

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 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.

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.

Multispectral and synthetic aperture radar (SAR) image fusion is one of the key technologies to improve image quality. The fusion method of multiscale decomposition includes two aspects: the decomposition of the image and the design of the 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 (LS) optimization rolling guidance filtering (RGF) for multiscale decomposition is proposed. All gradients of RGF are optimized by LS 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 the image is extended to multiscale space to achieve scale separation of space-overlapping features, which is convenient for multilevel fusion of image features. In the end, based on the scale of decomposition, the results fall into three layers, and coupled neural P (CNP) system and other rules are designed for different layers of information fusion. The results indicate that this method has a good visual effect and outperforms all the other comparison methods on nine evaluation indexes.

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.