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.

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.

Combining spectral and spatial information can significantly improve the classification performance of hyperspectral image (HSI). Currently, a lot of spectral–spatial HSI classification methods have been proposed. However, the task of HSI classification has remained challenging since the number of training samples is limited in real scenarios. In this article, we propose a novel HSI classification framework with single sample, in which the spectral self-similarity and spatial polygon structure information are fully combined to improve the classification performance. On the one hand, spectral self-similarity is used to expand training samples, which makes it possible to obtain sufficient samples with minimal cost. On the other hand, polygonal partition is introduced to acquire the geometrical structure of land covers in man-made environments. Specifically, the edge information of geometric objects is captured by polygonal partition, which can be utilized to constrain the spatial range of sample expansion and optimize the classification results. Experimental results on three real HSIs illustrate that the proposed method performs very well under small training sample size even when the number of samples is single per class.

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.

It is well known that classification accuracy highly relies on the number of labeled samples. However, it is difficult to obtain sufficient labeled samples in real-world applications. To solve this issue, a novel hyperspectral image (HSI) classification method based on polygonal partition is proposed for crop mapping. This method only needs single sample per class as an initial training set. Specifically, multiscale polygonal partition is applied on the first three components of the HSI. Then, a spectral similarity-based sample expansion method is proposed to obtain more labeled samples. Next, a pixel-wise classifier, the support vector machine (SVM), is used to acquire an initial classification result. Finally, classification result is further optimized according to the partition maps. Experimental results show that classification performance of the proposed method is satisfactory even when the number of labeled sample is single for each class.

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.