Object-based classification with features extracted by a semi-automatic feature extraction algorithm - SEaTH
Object-based classification with features extracted by a semi-automatic feature extraction algorithm - SEaTH
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DOI：
10.1080/10106049.2011.556754
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发表时间：
2011-01-01
影响因子：
3.8
通讯作者：
Pontius, Robert Gilmore, Jr.
中科院分区：
文献类型：
--
作者：
Gao, Yan;Marpu, Prashanth;Pontius, Robert Gilmore, Jr.
基于对象的图像分析（OBIA）使用与图像对象所包含的像素相关的对象特征（或属性）来辅助图像分类。这些目标特征包括光谱、形状、纹理和上下文特征。有数百个可用的功能，识别那些可以提高类之间的可分性是至关重要的OBIA。SEaTH算法计算给定特征的对象类的SEaTH。SEaTH算法避免了寻找重要特征和阈值的耗时的试错实践。本文测试的SEaTH算法Landsat-7增强型专题制图仪（ETM+）图像在一个异质景观与多个土地覆盖类。结果表明，SEaTH是其他自动化方法的一个强有力的替代方案，与参考数据的一致率为79%。相比之下，基于对象的最近邻分类器产生66%的协议和基于像素的最大似然分类器产生69%的协议。
Object-based image analysis (OBIA) uses object features (or attributes) that relate to the pixels contained by the image object to assist in image classification. These object features include spectral, shape, texture and context features. With hundreds of available features, the identification of those that can improve separability between classes is critical for OBIA. The Separability and Thresholds (SEaTH) algorithm calculates the SEaTH of object-classes for the given features. The SEaTH algorithm avoids time-consuming trial-and-error practice for seeking important features and thresholds. This article tests the SEaTH algorithm on Landsat-7 Enhanced Thematic Mapper (ETM+) imagery in a heterogeneous landscape with multiple land cover classes. The results suggest SEaTH is a strong alternative to other automated approaches, yielding an agreement of 79% with reference data. In comparison, an object-based nearest neighbour classifier yielded 66% agreement and a pixel-based maximum likelihood classifier yielded 69% agreement.