A supervised detection algorithm is presented to detect the target region in hyperspectral imagery. 给出一种有监督检测算法以检测高光谱图像中的区域目标。
High dimensionality of hyperspectral imagery leads to the existing of much redundant information. 冗余信息的大量存在,给高光谱数据的分析和处理带来很大的困难。
Research on Anomaly Target Detection and Subpixel Mapping in Hyperspectral Imagery(HIS) 高光谱图像的异常目标检测及亚像元定位研究
Recently, hyperspectral imagery has come into the picture, which provides a very high spatial resolution while capturing extremely fine radiometric resolution data. 最近,高光谱图像已生效的图片,它提供了一个非常高的空间分辨率,同时拍摄的极其精细的辐射分辨率数据。
Support Vector Data Description Based on Adaptive Anomaly Detection Method in Hyperspectral Imagery(HIS) 基于支持向量描述的自适应高光谱异常检测算法