融合低秩和形态学的高光谱影像特征提取

高光谱遥感影像具有较高的光谱分辨率,能够精细刻画地物的反射光谱,具有很高的地物分类与识别能力. 但高维波段之间通常具有较高的相关性,冗余度高,为影像处理和分析带来负担. 针对高光谱影像特点的特征提取和选择为有效提取信息提供了保障. 提出一种融合低秩和形态学的特征提取方法(MSEMP),利用低秩来精简高光谱影像中的冗余信息,获取秩最小的光谱紧致表达,并在此基础上利用多形态多尺度结构元素提取形态学剖面,获取影像空间特征. 实验对AVIRIS和ROSIS传感器的两组数据进行测试,通过MSEMP提取特征后进行分类实验,可以获得较高的分类结果,证明了低秩和形态学相结合的特征提取方法的有效性.

Feature Extraction of Hyperspectral Remote Sensing ImageBased on Low Rank and Morphology

Hyperspectral remote sensing images with high spectral resolution can describe the reflection spectrum of ground objects in detail,and represent a good ability to classify and identify the ground objects. However,there is usually a high correlation and redundancy among bands,which brings burden to image processing and analysis. Feature extraction and selection of hyperspectral images provide a guarantee for the effective information extraction. In this paper,a feature extraction method,MSEMP,which integrates low rank and morphological profiles is proposed. Low-rank is utilized to simplify the redundant information and obtain the spectral compact expression with the minimum rank of hyperspectral data. Based on low rank representation of hyperspectral image,morphological profiles are extracted by using multi-shaped and multi-scale elements. The proposed algorithm is tested on AVIRIS and ROSIS data,and experimental results show that the classification accuracy based on MSEMP is higher compared with other methods. It indicates that MSEMP is an efficient feature extraction method.