TEXTURAL FEATURES FOR IMAGE CLASSIFICATION
TEXTURAL FEATURES FOR IMAGE CLASSIFICATION
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DOI：
10.1109/tsmc.1973.4309314
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发表时间：
1973-01-01
期刊：
影响因子：
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通讯作者：
DINSTEIN, I
中科院分区：
文献类型：
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作者：
HARALICK, RM;SHANMUGAM, K;DINSTEIN, I
纹理是用于识别图像中感兴趣的对象或区域的重要特征之一，无论图像是显微照片、航空照片还是卫星图像。本文描述了一些易于计算的纹理特征的灰度色调空间依赖性的基础上，并说明了它们在3种不同类型的图像数据的类别识别任务中的应用：5种砂岩的显微照片，1：20 000全色航空照片的8个土地利用类别，地球资源技术卫星（Erts）的多专业图像包含7个土地利用类别。我们使用两种决策规则：一种是决策区域是凸多面体（分段线性决策规则），另一种是决策区域是矩形平行六面体（最小-最大决策规则）。在每个实验中，数据集被分为两部分，训练集和测试集。测试集识别准确率为显微照片的89%，航空摄影图像的82%，卫星图像的83%。这些结果表明，易于计算的纹理特征可能具有广泛的图像分类应用的普遍适用性。
Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.