Study Of Extraction Information Of Land Cover Based On SPOT5 Panchromatic Images

Remote Sensing Images is an important approach of obtaining information of land use due to its high-resolution, easy-obtaining and economy. Because of the complexity of land use/land cover, human had to obtain information of land use by visual interpretation. By all appearances the approach is not able to satisfy the requirement of obtaining information fast and exactly. Although human had obtained many research achievements of computer interpretation, few of them are applied to practice.The classification of Remote Sensing images includes two basic methods: the pixel-based and the object-based method. Most pixel-based classification methods can be subsumed under the cluster analysis. For example, with the aid of statistical methods, with fuzzy-logic techniques or with neural networks, them can be assigned to one class. The features to be classified are generally the spectral signatures of pixel. Pixel-based methods are hitherto the most commonly used type of classification in remote sensing. In recent years, these purely pixel-based methods have increasingly reached their limits. Object-based image analysis shows greater potential, because it has a very large feature basis (shape, size, texture and neighbourhood) for classification, and can integrate additional data from other data sources easily. Just because of the great potential of object-based analysis, recent research work increasingly goes in this direction.Based on comprehensive study on a large number of research results of object-oriented classification of Remote Sensing images, this study puts forward a new automatic feature analysis method-SaT method. This method can solve the problems of slow speed of feature selecting and missing optimal eigenvalues. This segmentation object-oriented processing classifier has been applied to land-cover classification for Jianchuan County of 2238Km2. This classifier outperforms an overall accuracy of 92.75% and Kappa of 0.91, and can get producer accuracy and costumer accuracy of 90% or higher. This approach excel common classifiers, such as :⑴the maximum likelihood classifier;⑵the maximum likelihood classifier with texture analysis;⑶artificial neural network. Study result shows that the object-oriented analysis + SaT has good application perspective.