Hyperspectral images (HSIs) have been widely used for target detection due to their abundant spatial and spectral information. In this article, a shadow-insensitive hyperspectral target detection (HTD) framework based on exposure fusion is proposed, which consists of the following major steps. First, the input HSI is divided into two parts, namely the shadow region and the nonshadow region. Second, total variation-based feature extraction and overexposure operation are performed on the input image to produce two feature images, i.e., the original feature image and the overexposure image. Third, a self-guided constrained energy minimization (SGCEM) detector is performed on the two feature images to detect the targets in shadow and nonshadow regions, respectively. Finally, the detection results obtained on the original feature image and the overexposure image are fused to acquire the final detection result. Extensive experiments conducted on real-world data illustrate that the proposed method can achieve satisfactory results when shadow exists.

Single-modal image registration methods are generally not feasible for visible and infrared images. Besides, multi-modal image registration methods still suffer from uneven distribution of extracted features, low repeatability, and ambiguous features. To address these issues, a coarse-to-fine infrared and visible image registration approach for dual sensor UAV imaging system is proposed, which is resilient to the difference of focal lengths and field of view. First, in the coarse registration step, the infrared image is transformed to the same scale as the visible image by using the similarity transformation. This operation makes the proposed method robust to the variation of field of view. Then, the feature point pairs are initialized using feature detectors in the infrared image’s blocked phase congruency feature map. Next, the feature point pairs are optimized by estimating the offset based on the relationship between the constructed feature descriptors. Finally, using elastic deformation, the pixel-level registered infrared image is obtained. Extensive experiments demonstrate the superior performance of the proposed coarse-to-fine image registration methodology in the real infrared-visible image pairs. The code and dataset are available at https://drive.google.com/drive/folders/1mpUWwHUbKTrBdOrNMNRRnuJclDUAC7nU?usp=sharing.

Pansharpening has benefited from the development of deep learning (DL) and has achieved excellent results. However, most DL-based methods extract local features by convolutional neural networks and do not integrate global features. Moreover, these methods only extract high-frequency features on the high-pass domain (HPD) or only consider image features on the intensity domain (ID). The method that only considers features in one domain may result in insufficient extraction of spatial and spectral features. Therefore, we propose a dynamic local–global network model on dual-domains, that is, HPD and ID. The dynamic local–global feature extraction block (DLGB) is designed to dynamically integrate local and global features to improve the representation capability of the network. To decrease the computational complexity of global feature extraction, a lightweight biaxial nonlocal attention (BNLA) that captures global spatial features in horizontal and vertical directions is proposed. Experiments on GeoEye-1, QuickBird, and WorldView-3 datasets show that the proposed method presents better fusion performance on objective evaluation indices and subjective perception.

With the improvement of imaging equipment, hyperspectral underwater target detection (HUTD) has raised much interest in recent years. The existing HUTD methods do not fully utilize spectral characteristics and need prior information about targets. Besides, the detection performance lacks verification in natural scenarios. In this paper, the authors propose a Feature Bands based Unsupervised underwater target Detection method (FBUD), which aims at finding the optimal feature bands to identify the underwater target near the coastline. Specifically, the normalized difference water index (NDWI) and unmixing technique are adopted to find the target and background pixels. Then, the spectral difference between the target and background is used to find the feature bands. With a simple and fast math operation of the feature bands, the probability map of the underwater target can be easily obtained. Besides, a new unmanned aerial vehicle (UAV)-borne hyperspectral image dataset named HNU-UTD is built for underwater target detection in real-world scenes. Experimental results obtained with the HNU-UTD dataset confirm the accuracy and effectiveness of the proposed detection method, which even outperforms supervised detection methods.

Unmanned aerial vehicle (UAV) hyperspectral imaging has been extensively applied in various fields. However, due to the limited imaging width, hyperspectral images (HSIs) captured by UAV need to be stitched, so as to effectively cover the study area. In this article, an effective seamless stitching method with deep feature matching and elastic warp is proposed for HSIs, which consists of the following major steps. First, for each input HSI, a single-band gray-scale image is obtained by fusing the bands corresponding to the red, green, and blue wavelengths. Second, the feature points of each HSI are obtained with a robust VGG-style network and matched with a graph neural network. After point pairs are obtained, the next step is to estimate the transformation matrix of adjacent images, and a spectral correction method based on intrinsic decomposition is proposed to ensure the spectral consistency of adjacent images. In the final stage, a seam-cutting and multiscale blending strategy is adopted to ensure the spatial consistency of the stitching results. Experimental results on real HSIs show that the proposed method is superior to six representative image stitching approaches.

In this paper, a novel unmanned aerial vehicle (UAV) hyperspectral image stitching framework based on radiation correction and seam-cutting blending is proposed. Firstly, spectral correlation constraints are introduced to eliminate mismatched pairs in the transform matrix estimation step. Then, a spectral correction method based on intrinsic images is proposed to ensure spectral consistency of stitching results. In order to obtain more natural stitching results without edge effect, a seam-cutting and multi-scale blending strategy is adopted in the final blending stage. Experimental results on real unmanned aerial vehicle hyperspectral strip images show that the proposed method is superior to a representative image stitching approach.

Noise usually appears in hyperspectral images (HSIs), and strongly affects the performance of the follow processing and analysis. In recent years, a large number of denoising algorithms have been proposed and it is known that the denoising effect is highly dependent on the accurate estimates of the type and level of noise present in an HSI. This paper focuses on analyzing the real noise in HSIs by separating and estimating the level of noise in HSIs. In consideration of the spectral correlation and the unique spatial structure of stripe noise, the developed method employs Fourier domain analysis and the high correlation among neighboring spectral bands to separate different types of noise. Experimental results show that the level of noise may be quite different for different bands of an HSI, and HSIs captured by different senors or in different scenes.

Matrix separation has a wide range of potential applications and many approaches have been devised to solve it. Especially, for applications involving large-scale data, such as vision tasks, improving the scalability of algorithms has attracted much attention. Reviewing these methods, they mainly involve convex optimization and factorization optimization. Convex optimization models become increasingly costly as the matrix size and rank grow. Factorization optimization models, to a large extent, reduce the computational complexity of matrix separation. l1-filtering applied the generalized Nyström to matrix separation, and proposed the seed-based convex optimization. In this paper, to benefit from the seed strategy, we propose matrix separation based on Low-Rank Matrix Fitting (LMaFit)-Seed, which is an algorithm on low-rank factorization optimization, to enhance the scalability in solving the problems of large-scale matrix separation and to be less time-consuming. We evaluate the proposed method and demonstrate comparisons with several state-of-the-art methods on synthetic data simulations and real sequences experiments.

Majority of present applications use buttons or touch panels as their human-computer interface. The limitation of these methods is that users can not interact with the equipments in a natural way, since people should operate the equipments according to certain rules which are arranged beforehand. This paper proposes a novel interaction system which contains a low cost full-field of view device to make sure users can see the display content from all directions. In addition, a tracking device is set up over users head to identify the user's natural gesture. A target following photography equipment is also combined with the system to provide on-line video chat and remote monitoring functions. Our system can be considered as a new form of human-computer interface and the advantages and potential are discussed in this paper. Finally, a demonstration and experimental results are given to evaluate the validity of the system.