神经网络在土地覆盖分类方面的应用问题。
Neural network is applied to the classification of land cover.
结果表明地理辅助数据参与的BP神经网络用于土地覆盖分类研究可以获得相对较好的分类结果。
The results show that a relatively satisfied classification result can be achieved by using the classification method combined with BP neural network in land cover classification.
结果表明ASAR数据可以广泛应用于多云多雨地区的土地覆盖分类，农作物估产，船只探测和海洋等领域。
The result shows that ASAR data can find a wide use in many fields such as crop growth . monitoring, ocean and ship detection.
然后在充分分析影像不同窗口纹理特征的基础上，提出应用组合纹理特征进行土地覆盖分类和居民地信息提取方法。
Then, land cover classification and residential areas extraction with combined texture feature was proposed by the sufficient analysis of the texture feature with different image window.
分类方法在土地利用和土地覆盖变化研究中占据重要的地位。
Techniques of classification are very importance for Land Use and Cover Change(LUCC).
然后根据所生成的林地、草本和裸地的连续覆盖图，基于专家分类器进行土地利用分类。
Then, based on the continuous land surface map of tree, herbaceous and bare, the land use classification was made using expert classifier.
土地利用与地表覆盖分类为术星遥测之重要研究与应用主题。
Landuse classification is one of the major applications of satellite remote sensing.
土地植被覆盖指数的准确度取决于图像分类准确度，但与分类总准确度没有直接关系。
The accuracy of land vegetative cover index rested with the accuracy of image classification, but had no direct relations with total classification accuracy.
土地植被覆盖指数的准确度取决于图像分类准确度，但与分类总准确度没有直接关系。
The accuracy of land vegetative cover index rested with the accuracy of image classification, but had no direct relations with total classification accuracy.
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