Supervised and unsupervised approaches for classification of remote sensing data by computer are discussed. Some primary classification results are provided.
讨论了遥感数据监督指非监督和计算机自动分类的问题，并给出了一些初步的分类结果实例。
Classification and pattern recognition of high dimensional remote sensing data are distinctly different from traditional multi-channel remote sensing classification techniques.
高维遥感数据的分类与识别与传统的多光谱遥感分类技术具有明显的区别。
BP neural network is widely used for classification of remote sensing image data nowadays.
BP神经网络近年来广泛地应用于遥感影像分类中。
This classification has also been proved advantageous not only to discriminate the soils from the data of field survey or remote sensing, but also to determine the possibility of soil improvement.
这样的属性分类，还有利于野外识别土壤和确定其改良的难易程度，使调查成果发挥实效。
Discuss the method and technique about integrated multiple levels data fusion based on the characteristics of multi-sources Remote Sensing information fusion classification.
结合多源遥感信息融合分类的特点，在多级数据融合方法集成技术的基础上，初步形成一个对多源遥感信息融合分类的技术框架。
Finally, the vector format data of remote sensing classification result was used to update the original forest stand layer in order to get the available inputs of fire visualization modeling.
最后，利用矢量格式的遥感分类结果对原森林小班图层进行更新，获得新的可燃物类型图层，作为林火可视化模型的输入图层。
It has an important practical significance in offering essential information and data for the decision-maker with recognition and classification the drainage system from the image of remote sensing.
对各种遥感影象的水系加以识别和分类，为决策者提供必要的信息和数据，具有重要的现实意义。
Remote sensing classification is the main method of the analysis of remote sensing data and very important research content in remote sensing image procession.
遥感分类是主要的遥感数据分析方法，是遥感图像处理中的一个非常重要研究内容。
Based on the spectral feature neighborhood, this paper proposes a tolerant rough set classification method to handle the uncertainty in the process of satellite remote sensing data classification.
结合粗糙集理论和遥感数据中地物光谱特征空间分布信息，提出了一种基于光谱特征邻域的容差粗糙集分类方法，用来处理卫星遥感数据分类中的不确定性问题。
Based on the spectral feature neighborhood, this paper proposes a tolerant rough set classification method to handle the uncertainty in the process of satellite remote sensing data classification.
结合粗糙集理论和遥感数据中地物光谱特征空间分布信息，提出了一种基于光谱特征邻域的容差粗糙集分类方法，用来处理卫星遥感数据分类中的不确定性问题。
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