高光谱遥感影像具有丰富的光谱信息，在地物分类识别方面具有明显的优势。
The hyperspectral remote sensing image is rich in spectrum information, so it can be better to carry on the ground targets classification.
利用该方法进行处理，当高光谱数据维数降低了90%时，9类地物分类实验的总体分类精度可以达到80.2%。
When the data dimensionality is reduced 90% by using the proposed method, the overall classification accuracy of nine classes of ground cover reaches 80.2%.
MSI200型成像光谱仪因能成像，可通过图像明确地物目标，并得到该地物纯光谱信息，可以利用该信息进行地物分类识别并成图。
The imaging spectrometer MSI200 can demonstrate object through image and extract the pure spectral information that can be use to distinguish and engender single image.
实验结果表明，该分类算法对于分类地物目标，进而分析其散射机理是十分有效的。
Experimental results indicate that this classification algorithm is very efficient in classifying targets and analyzing the scattering mechanism.
仔细分析了研究区典型地物的光谱特征和同物异谱与异物同谱现象，并结合实地考察建立了分类体系。
In this paper, we studied the spectral character of our study region in detail and based our classification system on the spectral character and our filed work.
与单纯基于光谱特征的分类方法进行比较，该方法在一定程度上改善了分类精度，细化了地物类别。
Compared with the classification method purely based on spectral character, this method can improve the classification accuracy and refine object classes in some extent.
计算机遥感分类识别原理，是利用地物的光谱能量特征差异性和结构特征差异性来识别地物信息。
The principle of computer-RS classification recognizing is using the difference of feature of structure and spectrum energy to recognize information of surface features.
确定研究区内不同地物采用监督分类和无监督分类时相应的参数和阈值。
Made sure of the parameters and thresholds of different objects with supervised and unsupervised classification.
同时，在模糊C-均值聚类基础上选择训练样本比起直接基于真实地物图选择，减少了主观因素对训练样本选择的影响，因此取得了更高的分类精度。
Selecting train sample on the basis of fuzzy C-mean clustering decreased subjective factor affecting selecting train sample, so higher classification accuracy can be achieved.
分类结果表明：显著性度量是一种合理并有效的两种地物的分类判据。
It's shown by the image classification results that significance is a reasonable and effective criterion for classification between two kinds of objects.
如果能自动地从航空遥感图像中提取出道路网，将会简化城市地物目标的分类和测量过程。
The classification and measuring procedure for geometrical objects of a city will be simplified if its main road network could be automatically extracted from aerial remote sensing images.
试验证明，本文演演算法能够有效地保持地物的形状特征，分类精度相比传统演演算法有所提高。
The experiment shows that the proposed algorithm can effectively maintain the shape feature of the object, and the classification accuracy is higher than traditional algorithms.
使用本模型的七个纹理参数作特征，在六类地物类型210个样本上作实验，由此设计的分类器的识别率为98.6%。
Seven texture parameters of this model are used for classifying six types of remote sense image over 210 samples. It's success percent is 98. 6 %.
本文着重介绍在遥感影象分类中应用数字地面模型（DTM）改正地物反射光谱中地形影响的方法和试验。
In this paper the methods and experiments for applying the digital terrain model(DTM) to improve the classification accuracy are introduced.
本论文研究利用多光谱图像进行自然地物目标分类技术。
The aim of this paper is to study classification approach of topographical objects with Multi-spectral image.
在对城市景观主要建模对象进行分类的基础上，建立了三维空间数据结构，实现了地形和地物模型三维重建的系列算法。
Based on the classification of the primary modeling objects in the city scene, we separately rebuild the model of the terrain and objects according to the 3d spatial data structure.
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.
将纹理信息融合到原始遥感图像中，对于地物的准确识别和分类具有重要的作用。
As an important indicator of spatial structure information in remote sensed images, texture plays major role in accurate land cover mapping.
海岸带作为海洋、陆地和大气共同作用的地带，其地物混杂度大，变化频繁，单纯利用光谱特征分类难以取得理想的精度。
Influenced jointly by such factors as ocean, land and atmosphere, the coastal zone is characterized by the mixing of various land types with high extent of variation.
海岸带作为海洋、陆地和大气共同作用的地带，其地物混杂度大，变化频繁，单纯利用光谱特征分类难以取得理想的精度。
Influenced jointly by such factors as ocean, land and atmosphere, the coastal zone is characterized by the mixing of various land types with high extent of variation.
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