Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery
Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery
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DOI：
10.1080/014311602331291215
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发表时间：
2004-12
影响因子：
3.4
通讯作者：
Le Wang;Wayne P. Sousa;Peng Gong
中科院分区：
文献类型：
--
作者：
Le Wang;Wayne P. Sousa;Peng Gong
IKONOS 1米全色和4米多光谱图像被用来绘制位于巴拿马加勒比海岸蓬塔加莱塔的一个研究地点的红树林。我们假设，光谱之间的分离度红树林物种将提高作为基本的空间单位，而不是像元的对象。三种不同的分类方法进行了研究：最大似然分类（MLC）在像素级，最近邻（NN）分类在对象级，和混合分类，集成了像素和基于对象的方法（MLCNN）。特别是对象分割，这是基于对象的分类的关键步骤，我们开发了一种新的方法来选择最佳尺度参数的帮助下，巴塔查亚距离（BD），一个著名的指标类可分性在传统的基于像素的分类。BD值在像素级和一系列更大的尺度上的比较不仅支持了我们最初的假设，而且还帮助我们确定了一个最佳尺度，在这个尺度上分割的对象有可能达到最佳的分类精度。在三种分类方法中，MLCNN的平均准确率最高，为91.4%。讨论了基于像素和基于对象的分类方法的优点和局限性。
IKONOS 1-m panchromatic and 4-m multispectral images were used to map mangroves in a study site located at Punta Galeta on the Caribbean coast of Panama. We hypothesized that spectral separability among mangrove species would be enhanced by taking the object as the basic spatial unit as opposed to the pixel. Three different classification methods were investigated: maximum likelihood classification (MLC) at the pixel level, nearest neighbour (NN) classification at the object level, and a hybrid classification that integrates the pixel and object-based methods (MLCNN). Specifically for object segmentation, which is the key step in object-based classification, we developed a new method to choose the optimal scale parameter with the aid of Bhattacharya Distance (BD), a well-known index of class separability in traditional pixel-based classification. A comparison of BD values at the pixel level and a series of larger scales not only supported our initial hypothesis, but also helped us to determine an optimal scale at which the segmented objects have the potential to achieve the best classification accuracy. Among the three classification methods, MLCNN achieved the best average accuracy of 91.4%. The merits and restrictions of pixel-based and object-based classification methods are discussed.