A target detection method for hyperspectral image based on mixture noise model

Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification

As an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks (DCNNs). However, there are still several challenges that hinder the practical applications of RSSC, such as complex composition of land cover, scale-variation of objects, and redundant and noisy areas for scene classification. In order to mitigate the impact of these issues, we propose an adaptive discriminative regions learning network for RSSC, referred as ADRL-Net briefly, which locates discriminative regions effectively for boosting the performance of RSSC by utilizing a novel self-supervision mechanism. Our proposed ADRL-Net consists of three main modules, including a discriminative region generator, a region discriminator, and a region scorer. Specifically, the discriminative region generator first generates some candidate regions which could be informative for RSSC. Then, the region discriminator evaluates the regions generated by region generator and provides feedback for the generator to update the informative regions. Finally, the region scorer makes prediction scores for the whole image by using the discriminative regions. In such a manner, the three modules of ADRL-Net can cooperate with each other and focus on the most informative regions of an image and reduce the interference of redundant regions for final classification, which is robust to the complex scene composition, object scales, and irrelevant information. In order to validate the efficacy of the proposed network, we conduct experiments on four widely used benchmark datasets, and the experimental results demonstrate that ADRL-Net consistently outperforms other state-of-the-art RSSC methods.

Self-Dictionary Regression for Hyperspectral Image Super-Resolution

Due to sensor limitations, hyperspectral images (HSIs) are acquired by hyperspectral sensors with high-spectral-resolution but low-spatial-resolution. It is difficult for sensors to acquire images with high-spatial-resolution and high-spectral-resolution simultaneously. Hyperspectral image super-resolution tries to enhance the spatial resolution of HSI by software techniques. In recent years, various methods have been proposed to fuse HSI and multispectral image (MSI) from an unmixing or a spectral dictionary perspective. However, these methods extract the spectral information from each image individually, and therefore ignore the cross-correlation between the observed HSI and MSI. It is difficult to achieve high-spatial-resolution while preserving the spatial-spectral consistency between low-resolution HSI and high-resolution HSI. In this paper, a self-dictionary regression based method is proposed to utilize cross-correlation between the observed HSI and MSI. Both the observed low-resolution HSI and MSI are simultaneously considered to estimate the endmember dictionary and the abundance code. To preserve the spectral consistency, the endmember dictionary is extracted by performing a common sparse basis selection on the concatenation of observed HSI and MSI. Then, a consistent constraint is exploited to ensure the spatial consistency between the abundance code of low-resolution HSI and the abundance code of high-resolution HSI. Extensive experiments on three datasets demonstrate that the proposed method outperforms the state-of-the-art methods.