.png){kind=icon}
返回
Subpixel Component Analysis for Hyperspectral Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2019.2900484.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Land-cover classification with hyperspectral imagery has been an active topic in the remote sensing community. It aims at relating a unique class label to each pixel in the scene, so that it can be well defined by a given land cover type. In this paper, we explore the intrinsic characteristics of hyperspectral imagery from a subpixel-level perspective and propose a new subpixel component analysis (SCA) approach for feature extraction and land-cover classification. The core idea of SCA is that we extract a subpixel attribute component feature from the abundance maps. Compared with the abundance maps, the extracted subpixel feature image shows higher signal-to-noise level and clearer spatial distribution details. In order to deal with spectral variability, as well as obtain representative image endmember signatures and their corresponding abundance maps, we adopt a regional clustering-based spatial preprocessing (RCSPP) strategy for endmember identification, and a partial unmixing model based on mixture tuned matched filtering (MTMF) for abundance estimation. Furthermore, to highlight the spatial distribution details as well as eliminate the noise disturbance in the derived abundance maps, we perform sparse image decomposition on the obtained abundance maps, thus achieving a new subpixel feature representation for classification. Our experimental results reveal that the proposed SCA approach can obtain feature representation with explicit physical meaning, clear spatial distribution details, and better noise robustness, leading to state-of-the-art classification results.
Keyword:
Hyperspectral image classification
partial unmixing
regional clustering=based spatial preprocessing (RCSPP)
subpixel component analysis (SCA)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
机构
引用论文
Deep Pyramidal Residual Networks for Spectral-Spatial Hyperspectral Image Classification用于光谱-空间高光谱图像分类的深度金字塔残差网络
OAAI
.png){kind=icon}
OAAI分享收藏分享收藏Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery高光谱图像物质量化的全约束最小二乘线性光谱混合分析方法
PREAI
分享收藏
A Fast Spatial-Spectral Preprocessing Module for Hyperspectral Endmember Extraction用于高光谱端元提取的快速空间光谱预处理模块
PREAI
分享收藏
查看更多内容