A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery
A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery
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DOI：
10.1109/tgrs.2006.876704
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发表时间：
2006-09
影响因子：
8.2
通讯作者：
Liangpei Zhang;Xin Huang;B. Huang;Pingxiang Li
中科院分区：
文献类型：
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作者：
Liangpei Zhang;Xin Huang;B. Huang;Pingxiang Li
形状和光谱都是高空间分辨率遥感（HSRRS）影像的重要特征，它们是此类影像上纹理的具体体现。本文提出一种空间特征指数——像素形状指数（PSI），用于描述像素周边局部区域的形状特征。PSI是一种基于像素的特征，它测量各个方向上的灰度相似距离。由于仅靠形状特征不足以对高空间分辨率遥感影像进行分类，我们将通过独立成分分析提取的变换后的光谱特征添加到分类器的输入向量中，以此替代原始的多光谱波段。同时，开发了一种利用支持向量机融合形状和光谱特征的快速算法，以解释复杂的输入向量。将PSI的结果与利用小波变换、灰度共生矩阵以及长宽提取算法提取的一些空间特征进行比较，以测试其有效性。实验表明，PSI能够有效地描述形状特征，并且比其他方法能得到更准确的分类结果。研究发现光谱和形状特征能够相互补充，它们的融合可以提高分类精度，同时还发现变换后的光谱成分更适合用于分类。
Shape and spectra are both important features of high spatial resolution remotely sensed (HSRRS) imagery, and they are concrete manifestation of textures on such imagery. This paper presents a spatial feature index, pixel shape index (PSI), to describe the shape feature in a local area surrounding a pixel. PSI is a pixel-based feature which measures the gray similarity distance in every direction. As merely the shape feature is inadequate for classifying HSRRS imagery, a transformed spectral feature extracted by independent component analysis is added to the input vectors of our classifier, and this replaces the original multispectral bands. Meanwhile, a fast fusion algorithm that integrates both shape and spectral features using the support vector machine has been developed to interpret the complex input vectors. The results by PSI are compared with some spatial features extracted using wavelet transform, gray level co-occurrence matrix, and the length-width extraction algorithm to test its effectiveness. The experiments demonstrate that PSI is capable of describing shape features effectively and result in more accurate classifications than other methods. While it is found that spectral and shape features can complement each other and their integration can improve classification accuracy, the transformed spectral components are also found to be more suitable for classification