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首页> 外文会议>Conference on Image Processing: Algorithms and Systems III; 20040119-20040121; San Jose,CA; US >Genetic algorithm for unsupervised classification of remote sensing imagery
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Genetic algorithm for unsupervised classification of remote sensing imagery

机译：遥感影像无监督分类的遗传算法

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Traditionally, an unsupervised classification divides all pixels within an image into a corresponding class pixel by pixel. The number of classes must be selected, but seldom is ascertainable with little information in advance. Moreover, spectral properties of specific informational classes change seasonally for satellite imagery. The relationships between informational classes and spectral classes are not always constant, and relationships defined for one image cannot be extended to others. Thus, the analyst has very limited or no control over the menu of classes and their specific identities. In this study, a Genetic Algorithm is adopted to interpret the cluster centers of an image and to reveal a suitable number of classes to overcome the disadvantage of unsupervised classification. A Genetic Algorithm is capable of dealing with a set of numerous data such as satellite imagery pixels. An optimization consequence of the image classification is introduced and carried out. Through an image process program developed in Mathlab, the GA unsupervised classifier was processed on several test images for validity and on SPOT satellite imagery. The classified SPOT image was compared with finer aerial photographs as a ground truth for the estimation of classification accuracy.
机译：传统上，无监督分类将图像内的所有像素逐像素划分为相应的类别。必须选择类别的数量，但很少可以事先确定信息而无法确定。此外，对于卫星图像，特定信息类别的光谱属性会季节性变化。信息类别和光谱类别之间的关系并不总是恒定的，为一幅图像定义的关系无法扩展到其他图像。因此，分析人员对类菜单及其特定标识的控制非常有限或没有控制。在这项研究中，采用遗传算法来解释图像的聚类中心并揭示适当数量的类，以克服无监督分类的缺点。遗传算法能够处理一组大量数据，例如卫星图像像素。介绍并执行图像分类的优化结果。通过在Mathlab中开发的图像处理程序，在多个测试图像上对GA无监督分类器进行了有效性和SPOT卫星图像处理。将分类的SPOT图像与更精细的航空照片进行比较，以此作为估计分类精度的基础。

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