.png){kind=icon}
返回
Improving hyperspectral image classification by combining spectral, texture, and shape features
.png){kind=icon}
PRE
AI
DOI：10.1080/01431161.2015.1007251.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Several studies have already demonstrated the efficiency of utilizing spatial information in representation and interpretation of hyperspectral (HS) images. Texture and shape features are known as two important categories of spatial information in various applications of image processing. This study tries to utilize texture and shape features extracted from HS images, as well as spectral information, in order to reduce overall classification errors. These features include morphological profiles (MPs), global Gabor features, and features extracted from conventional and segmentation-based grey-level co-occurrence matrices (GLCMs). Various combinations of these spatial features along with spectral information are fed into a support vector machine (SVM) classifier, and the best combinations for different situations are determined. Experiments on the widely used Indian Pines, Pavia University, and Salinas HS data sets demonstrate the efficiency of the proposed framework in comparison with some recent spectral-spatial classification methods.
Keyword:
WEIGHTED FEATURE-EXTRACTION
SPATIAL CLASSIFICATION
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:
2.6
论文数:
1.2W
被引数:
2.7W
机构
引用论文
.png){kind=icon}
分享收藏Nanocrystalline silicon films as multifunctional material for optoelectronic and photovoltaic applications纳米硅薄膜作为光电和光伏应用的多功能材料
PREAI
分享收藏Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles
PREAI
分享收藏Integration of Segmentation Techniques for Classification of Hyperspectral Images用于高光谱图像分类的分割技术集成
OAAI
OAAI分享收藏
查看更多内容