Search

• 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]

Hyperspectral Image (HSI) visualization, which aims at displaying as much material information of original images as possible on a trichromatic monitor with natural color, plays an important role in image interpretation and analysis. However, most of the HSI visualization methods only focus on presenting the detail information of a scene without providing natural colors and distinguishing land covers with similar colors. In order to address this problem, this article proposes a multichannel pulse-coupled neural network (MPCNN)-based HSI visualization method, which consists of the following steps. First, the MPCNN is proposed and explored to fuse the original HSI so as to obtain a fused band with rich spatial details. Then, a color mapping scheme is proposed to determine the weights of red, green, and blue (RGB) channels. Finally, the weighted RGB channels are stacked together for visualization. Experiments performed on four hyperspectral data sets demonstrate that the proposed method not only displays the HSI with nature colors but also improves the details in the image. The effectiveness of the proposed method is demonstrated in terms of both visual effect and objective indexes. [ABSTRACT FROM AUTHOR]

Image decolorization is known to be an effective way in transferring a color image into a gray one while well preserving the major information of all three bands. In this paper, a simple yet effective hyperspectral image visualization framework based on decolorization, named decolorization based hyperspectral visualization, is proposed, which enables us to fully exploit the benefits of decolorization technique. The proposed framework consists of the following two main steps. First, the hyperspectral image is partitioned into nine subsets of adjacent hyperspectral bands and the averaged band of each subset is calculated. Then, the dimension reduced image is further divided into three groups of adjacent bands, and the bands in each group are fused by using an image decolorization method. The main contribution of this paper is that the strong correlations in two different fields, i.e., image decolorization and hyperspectral image visualization, are first built. Experiments performed on several real hyperspectral data sets demonstrate that the proposed framework can obtain outstanding visualization performance in terms of both subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

Most deep metric learning-based image characterization methods exploit supervised information to model the semantic relations among the remote sensing (RS) scenes. Nonetheless, the unprecedented availability of large-scale RS data makes the annotation of such images very challenging, requiring automated supportive processes. Whether the annotation is assisted by aggregation or crowd-sourcing, the RS large-variance problem, together with other important factors [e.g., geo-location/registration errors, land-cover changes, even low-quality Volunteered Geographic Information (VGI), etc.] often introduce the so-called label noise, i.e., semantic annotation errors. In this article, we first investigate the deep metric learning-based characterization of RS images with label noise and propose a novel loss formulation, named robust normalized softmax loss (RNSL), for robustly learning the metrics among RS scenes. Specifically, our RNSL improves the robustness of the normalized softmax loss (NSL), commonly utilized for deep metric learning, by replacing its logarithmic function with the negative Box–Cox transformation in order to down-weight the contributions from noisy images on the learning of the corresponding class prototypes. Moreover, by truncating the loss with a certain threshold, we also propose a truncated robust normalized softmax loss (t-RNSL) which can further enforce the learning of class prototypes based on the image features with high similarities between them, so that the intraclass features can be well grouped and interclass features can be well separated. Our experiments, conducted on two benchmark RS data sets, validate the effectiveness of the proposed approach with respect to different state-of-the-art methods in three different downstream applications (classification, clustering, and retrieval). The codes of this article will be publicly available from https://github.com/jiankang1991. [ABSTRACT FROM AUTHOR]