Satellite Objects Extraction and Classification Based on Similarity Measure
提出一种改进的弹性网格技术，利用灰度共生矩阵提取纹理特征生成目标指纹，并通过勒贝格度量计算差异度，实现卫星图像中目标的精确分类与识别。
This correspondence paper focuses on classification and recognition of different objects in a satellite image. First, for every object we need to compute its fingerprint as its unique recognition. We improve the traditional elastic grid technique. Every object is partitioned into a set of grids. For each grid, we use its texture feature, a five-tuple features generated by gray level co-occurrence matrix, rather than its center value, an average value of grays, to characterize it. We utilize the feature-standardizing method to handle this five-tuple features and then generate the fingerprint of each grid. An ordered sequence of fingerprints of all grids of an object is viewed as the fingerprint of this object. Furthermore, on the basis of the fingerprints of objects, we use the Lebesgue measure to compute their dissimilarities, and thus these objects are classified. In this paper, we develop the related algorithms. The experimental results show that classification and recognition generated by our method is more accurate than others, which implies that the method of computing dissimilarity is better.