土地覆盖
支持向量机
计算机科学
模式识别(心理学)
比例(比率)
上下文图像分类
多光谱图像
分割
人工智能
统计分类
图像分割
特征提取
遥感
特征(语言学)
图像分辨率
图像(数学)
数据挖掘
土地利用
地理
地图学
语言学
哲学
土木工程
工程类
作者
Leiguang Wang,Qinling Dai,Zheng Chen
标识
DOI:10.1109/wicom.2010.5600261
摘要
The availability of high-resolution (HR) remote sensing multispectral imagery brings opportunities and challenges for land cover classification. The methodology of multiscale segmentation is wildly accepted for feature extraction and classification in HR image. However, the relationship among chosen scale parameters, selected features, and classification accuracy is less considered. A classification approach combining the hierarchy segment algorithm and SVM is presented in this paper. Firstly, a family of nested image partitions with ascending region areas is constructed by iteratively merging procedure; Then, multiscale morphological features are extracted in every segmentation level; Finally, the classification accuracy in different scales are compared and analyzed. The experiments shown that a more conservative scale parameter benefits land cover classification algorithm and different land objects has different optimal scale for classification.
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