基于多尺度空谱鉴别特征的高光谱图像分类

doi:10.11896／j.issn.1002-137X.2018.12.040

Abstract

Abstract: In order to cope with the unevenness of homogenous regions’ area in hyperspectral images,an algorithm based on multi-scale discriminative spatial-spectral features was proposed.First,the image is processed with multi-scale filters.Then discriminative spatial-spectral information is extracted from the filtered images before put into SVM classifiers.At last,classification results of the filtered image are combined with decision fusion strategy.The experimental results on Indian Pines,Kennedy Space Center and University of Pavia indicate the effectiveness of the extracted spatial information.The overall accuracy of this algorithm can reach up to 96% when 10 percent of samples are randomly selected for training.What’s more,the classification accuracy and Kappa coefficient are higher than the comparative algorithms.

Key words: Hyperspectral images, Land cover classification, Multi-scale, Spatial information

CLC Number:

TP751.1

REN Shou-gang, WAN Sheng, GU Xing-jian, WANG Hao-yun, YUAN Pei-sen, XU Huan-liang. Hyperspectral Image Classification Based on Multi-scale Discriminative Spatial-spectral Features[J].Computer Science, 2018, 45(12): 243-250.

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Hyperspectral Image Classification Based on Multi-scale Discriminative Spatial-spectral Features

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