基于多尺度视觉特征组合的高分遥感影像目标检测

doi:10.3969/j.issn.0255-8297.2018.03.007

Abstract

Abstract:

To utilize the spatial structure and color information of the target in the high resolution remote sensing images, this paper proposes a method of target detection based on multi-scale visual feature combination. Firstly, we use two types of feature descriptor to express the color and spatial structure information of targets, one is the dense spatial pyramid scale-invariant feature transform (SIFT) descriptor in spatial pyramid of the color transformed images, the other is the multi-scale histogram of oriented gradient (HOG) feature combined with the hue, saturability and value (HSV) features. Secondly, the two sets of features are used to train classifer through support vector machine (SVM). For the color transformed dense SIFT spatial pyramid feature descriptor, the input of SVM is constructed by spatial pyramid matching kernel (SPMK). For the combined HOG and HSV feature, the orderly-combined eigenvectors are taken as the input of the SVM classifer. Finally the intersection of the two classifcation results are regarded as the fnal results. For verifcation, we tested the buildings and fsh rafts in aerial images and the yachts in satellite images. Experimental results show that the proposed method can improve the precision of target detection, which can reach to by 90% or better.

Key words: target detection, multi-scale visual feature, support vector machine (SVM)

CLC Number:

TP79

FENG Fa-jie, LI Jun-ping, DING Ya-zhou, ZHU Kun, CUI Wei-hong. Target Detection from High Resolution Remote Sensing Images Based on Combination of Multi-scale Visual Features[J]. Journal of Applied Sciences, 2018, 36(3): 471-484.

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Target Detection from High Resolution Remote Sensing Images Based on Combination of Multi-scale Visual Features

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