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1. (WO2018076138) TARGET DETECTION METHOD AND APPARATUS BASED ON LARGE-SCALE HIGH-RESOLUTION HYPER-SPECTRAL IMAGE

Pub. No.:    WO/2018/076138    International Application No.:    PCT/CN2016/103070
Publication Date: Fri May 04 01:59:59 CEST 2018 International Filing Date: Tue Oct 25 01:59:59 CEST 2016
IPC: G06K 9/62
Applicants: SHENZHEN UNIVERSITY
深圳大学
Inventors: LI, Yanshan
李岩山
XU, Jianjie
徐健杰
HUANG, Qinghua
黄庆华
XIA, Rongjie
夏荣杰
XIE, Weixin
谢维信
LIU, Peng
刘鹏
Title: TARGET DETECTION METHOD AND APPARATUS BASED ON LARGE-SCALE HIGH-RESOLUTION HYPER-SPECTRAL IMAGE
Abstract:
A target detection method and apparatus based on a large-scale high-resolution hyper-spectral image. The method comprises: reading a hyper-spectral image corresponding to a target (S101); pre-processing the hyper-spectral image (S102); detecting all candidate spatial spectral domain interest points of the hyper-spectral image to obtain a first set (S103); screening all the candidate spatial spectral domain interest points in the first set according to a response intensity to obtain a second set (S104); performing spectral angle matching according to a spectral curve corresponding to the second set to obtain an image block of a potential target area (S105); performing feature description on the image block, and encoding same to obtain a vector corresponding to the image block (S106); calculating the value of a classification function corresponding to the image block according to the vector corresponding to the image block (S107); if the value of the classification function corresponding to the image block is greater than a classification threshold value, determining that the image block contains the target (S108); if the value of the classification function corresponding to the image block is less than or equal to the classification threshold value, splitting the image block (S109); splitting all the candidate spatial spectral domain interest points according to sub-image blocks to form a first set corresponding to the sub-image blocks (S110); and repeatedly operating on the split sub-image blocks sequentially until the value of a classification function corresponding to a certain sub-image block obtained by means of splitting is greater than the classification threshold value, or a sub-image block obtained by means of splitting reaches a specified minimum size (S111). The detection effect in target detection of a high-resolution hyper-spectral image can be improved.