The semivariogram in comparison to the co-occurrence matrix for classification of image texture
The semivariogram in comparison to the co-occurrence matrix for classification of image texture
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DOI：
10.1109/36.729366
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发表时间：
1998-11-01
影响因子：
8.2
通讯作者：
de Miranda, FP
中科院分区：
文献类型：
--
作者：
Carr, JR;de Miranda, FP
比较了半方差函数和共生矩阵用于数字图像纹理分类的准确性，并使用试验点评估了在以下六个不同光谱波段上获得的图像：1)SPOT HRV，近红外；2)陆地卫星专题地图(TM)，可见红色；3)印度遥感(IRS)LISS-II，可见绿色；4)麦哲伦、金星、S波段微波；5)航天飞机成像雷达(SIR)-C，X波段微波；6)SIR-C、L波段微波图像。对于微波图像，半方差函数纹理度量比基于共生矩阵的分类器具有更高的分类精度，而对于光学图像，分类精度更低。
Semivariogram functions are compared to cooccurrence matrices for classification of digital image texture, and accuracy is assessed using test sites, Images acquired over the following six different spectral bands are used:1) SPOT HRV, near infrared;2) Landsat thematic mapper (TM), visible red;3) India Remote Sensing (IRS) LISS-II, visible green;4) Magellan, Venus, S-band microwave;5) shuttle imaging radar (SIR)-C, X-band microwave;6) SIR-C, L-band microwave.The semivariogram textural measure provides a larger classification accuracy than a classifier based on a co-occurrence matrix for the microwave images and a smaller classification accuracy for the optical images.