With the rapid development of spectral imaging techniques, classification of hyperspectral
images (HSIs) has attracted great attention in various applications such as land survey and …

随着模式识别, 机器学**, 遥感技术等相关学科领域的发展, 高光谱遥感影像分类研究取得快速
进展. 本文系统总结和评述了当前高光谱遥感影像分类的相关研究进展, 在总结分类策略的基础 …

Anomaly detection has become an important remote sensing application due to the
abundant spectral and spatial information contained in hyperspectral images. Recently …

Spatial features of hyperspectral imagery (HSI) have gained an increasing attention in the
latest years. Considering deep convolutional neural network (CNN) can extract a hierarchy …

Due to unavailability of prior knowledge about anomalies, background suppression (BS) is a
crucial factor in anomaly detection (AD) evaluation. The difficulty in dealing with BS arises …

A joint sparse representation (JSR) method has shown superior performance for the
classification of hyperspectral images (HSIs). However, it is prone to be affected by outliers …

A novel superpixel-based discriminative sparse model (SBDSM) for spectral-spatial
classification of hyperspectral images (HSIs) is proposed. Here, a superpixel in a HSI is …

In hyperspectral image processing, classification is one of the most popular research topics.
In recent years, research progress made in deep-learning-based hierarchical feature …

Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate
classification benefits a large number of applications such as land use analysis and marine …

In this paper, we propose a superpixel-level sparse representation classification framework
with multitask learning for hyperspectral imagery. The proposed algorithm exploits the class …