计算机科学 ›› 2014, Vol. 41 ›› Issue (2): 107-110.

• CCML 2013 • 上一篇    下一篇

样本自适应多特征加权的高分辨率遥感图像分类

常纯,李士进,万定生,冯钧   

  1. 河海大学计算机与信息学院 南京210098;河海大学计算机与信息学院 南京210098;河海大学计算机与信息学院 南京210098;河海大学计算机与信息学院 南京210098
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61170200,61370091)资助

Sample-specific Multiple Features Weighting-based High-resolution Remote Sensing Image Classification

CHANG Chun,LI Shi-jin,WAN Ding-sheng and FENG Jun   

  • Online:2018-11-14 Published:2018-11-14

摘要: 高分辨率遥感影像能够提供丰富的地物细节,但各种地物空间分布复杂,同类目标呈现出较大的光谱异质性,给传统模式识别分类器带来极大的挑战。提出了一种样本自适应多特征加权的遥感图像分类方法。常见的多特征组合分类器未能充分利用各种特征之间的局部相关性,提出通过分析测试样本局部特征相关性,探究各个特征在不同样本的分类中所占权重的不同,据此对不同分类器进行自适应加权。在一个大型遥感图像数据库上的实验结果表明,不同特征在遥感图像中对不同样本的分类作用是不同的,样本自适应特征加权法将平均分类精度从78.3%提高到90%。

关键词: 遥感图像分类,自适应加权,特征组合,多分类器 中图法分类号TP391.4文献标识码A

Abstract: High-resolution remote sensing image can provide rich feature details.However,a variety of terrain has complex spatial distribution,and spectral heterogeneity of similar landcovers appears largely,which bring great challenge to traditional pattern recognition classifier.For this purpose,this paper put forward a novel multi-classifier combination method for remote sensing image classification based on adaptive weights adjustment for different query samples.Previous multiple features combination classifiers fail to make full use of local correlation among them,with a unifying weight for all the samples.This paper explored different weights of each feature in classification on different test samples,according to different local distributions.The experimental results on a large remote sensing image database show that different features in remote sensing image classification of different samples have different effects,and the sample-specific multiple features weighting-based method presented in this paper enhances the average classification accuracy from 78.3% to 90%.

Key words: Remote sensing image classification,Adaptive weighting,Features combination,Multiple classifiers

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