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Sun glint in hyperspectral images (HSIs) leads to undesirable spectral interference, which severely affects subsequent image interpretation, such as environmental monitoring of coastal areas. Sun glint removal methods aim to recover a high quality image without sun glint from the original image. Most methods depend on an assumption that the near infrared band is strongly absorbed by water. However, this assumption is not always reliable because the infrared radiation in shallow or turbid water can be reflected back by the seabed or sediment, rather than being fully absorbed. Therefore, the reflected infrared radiation still contains sun glint and these methods cannot sufficiently remove sun glint from HSIs. To address this problem, a texture-aware total variation (TATV)-based method is proposed to remove sun glint from HSIs. The original HSI first is formulated as a desired clean image and a sun glint image. Then, in order to remove the sun glint, we propose a variational model where the different spectral characteristics of sun glint and other surrounding materials are considered. Specifically, we propose a texture-aware total variation regularized method to heavily penalize the variation of the sun glint areas. Experiments performed on simulated and real data sets demonstrate that our method can greatly outperform other state-of-the-art methods in removing sun glint. [ABSTRACT FROM AUTHOR]

• A global and local information based visualization framework is proposed. • Edge-preserving filtering is first adopted to visualize hyperspectral image. • Experimental results show outstanding performance compared with other methods. In this paper, an edge-preserving filtering and principal component analysis (PCA)-based visualization method is proposed for hyperspectral images, in which both global and local image information of hyperspectral images (HSIs) are taken into account in the proposed visualization framework that consists of the following major steps. First, the band number of the original image is reduced with averaging-based image fusion (AIF). Then, the edge-preserving filtering is performed on the dimension reduced image so as to decompose it into two components, i.e., the base layers which contain the large-scale boundary information, and the detail layers which contain the mid- and small-scale edges and textures. Next, the base layers are fused with the principal component analysis method, and the detail layers are fused with the weighted sum method, in which the fusion weights are determined by the transform matrix of the base layers. Finally, the fused detail layers are enhanced with histogram equalization and combined with the fused base layers to visualize the hyperspectral image. Experimental results on four real hyperspectral data sets demonstrate that the proposed approach performs the best in improving image contrast and preserving the details. In addition, compared with seven state-of-the-art visualization methods, the quantitative metrics obtained by the proposed method on four data sets have been increased by 80.45%, 69.29%, 138.61%, and 23.21% on average in terms of separability of features (SF), standard deviation (SD), average gradient (AG), and entropy (EN). [ABSTRACT FROM AUTHOR]

In this paper, a novel shift-invariant dual-tree complex shearlet transform (SIDCST) is constructed and applied to infrared and visible image fusion. Firstly, the mathematical morphology is used for the source images. Then, the images are decomposed by SIDCST to obtain the low frequency sub-band coefficients and high frequency sub-band coefficients. For the low frequency sub-band coefficients, a novel sparse representation (SR)-based fusion rule is presented. For the high frequency sub-band coefficients, a scheme based on the theory of adaptive dual-channel pulse coupled neural network (2APCNN) is presented, and the energy of edge is used for the external input of 2APCNN. Finally, the fused image is obtained by performing the inverse SIDCST. The experimental results show that the proposed approach can obtain state-of-the-art performance compared with conventional image fusion methods in terms of both objective evaluation criteria and visual quality. [ABSTRACT FROM AUTHOR]

Semantic segmentation of remote sensing images aims to assign a specific label or class to each pixel in an image, which plays an extremely important role in scene understanding. Currently, many advanced deep learning-based semantic segmentation methods have been developed. However, these methods are always based on disjoint labels to identify ground objects while ignoring the correlation (e.g., semantic, shapes, materials, etc.) among different ground objects, which limits the segmentation performance of remote sensing images. To solve this issue, we propose a hierarchical class graph for semantic segmentation of high resolution remote sensing images, which can learn structured relation among different ground objects. Specifically, first, we construct hierarchical class graphs based on different attributes and layers. Then, a three-layer hierarchical segmentation framework is developed to learn the correlation among different ground objects. Finally, a decision fusion method is designed to fuse the benefits of different hierarchical attributes and layers. More importantly, the influence of different hierarchical class graphs on the segmentation performance is detailedly analyzed. Extensive experiments on the iSAID and Vaihingen datasets reveal that all studied segmentation methods with hierarchical class graph can obtain better segmentation performance compared to ones without hierarchical class graph. The limitation of the proposed method is that the training time of the segmentation model tends to increase a bit because of considering the correlation among different ground objects. • Construct a multi-layer hierarchical class graph for semantic segmentation. • Propose a three-layer hierarchical graph framework for semantic segmentation. • Explore the effectiveness of different attributes on semantic segmentation. • A fuzzy set-based decision fusion method is proposed. [ABSTRACT FROM AUTHOR]

Infrared and visible image fusion is an effective image processing technique to obtain more comprehensive information, which can help people better understand various scenarios. In this paper, a novel infrared and visible image fusion method is proposed which fully considers the attributes of objects in source images. Benefiting from the attribute and the edge-preserving filters, the prominent objects in the infrared source image are effectively extracted. Then, the weight-based Laplacian pyramid fusion strategy is adopted to get more natural fusion results. The experimental results on the public image fusion datasets and a new infrared–visible video fusion dataset show that the proposed method achieves state-of-the-art fusion performance in terms of both visual and objective evaluations. The proposed algorithm is also implemented in an infrared–visible dual sensor system, which demonstrated the practicability of our fusion method. • A novel attribute guided image fusion method is proposed. • A dual infrared and visible imaging system is constructed. • A high-quality infrared–color visible image and video dataset is established. [ABSTRACT FROM AUTHOR]

Multi-modal image fusion is widely used in various fields, especially in military, medical, industrial detection and other fields. Image fusion can integrate redundant and complementary information of two or more multi-modal images into one image, so that the fused image contains more useful information. In this paper, we construct a novel dataset for Multi-modal Image Fusion Applications (MOFA), including four modals: visible, near-infrared (NIR), long-wavelength infrared (LWIR) and polarization. The MOFA dataset contains 1062 images of 118 groups, in which 450 are indoor and 612 are outdoor. The dataset is applied to different image fusion applications, including general multi-modal image fusion, fusion based image super-resolution and image restoration. Multiple image fusion methods are compared and analyzed on this dataset with a qualitative assessment of subjective and objective metrics. Based on the experiments, the advantages and disadvantages of different methods are discussed. Moreover, the challenging problems of image fusion are concluded. • Build a multi-modal imaging device. • Construct a multi-modal dataset, including visible, NIR, LWIR and polarization. • Explore the effectiveness of the dataset for different image processing tasks. • Analyze the advantages and disadvantages of different fusion methods. [ABSTRACT FROM AUTHOR]