首页> 外文会议>Conference on Remotely Sensed Data and Information; 20070525-27; Nanjing(CN) >Classification of Landsat 7 ETM+ imagery in western mountainous area of Zhejiang based on gray-gradient co-concurrency matrix
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Classification of Landsat 7 ETM+ imagery in western mountainous area of Zhejiang based on gray-gradient co-concurrency matrix

机译:基于灰度梯度并发矩阵的浙西山区Landsat 7 ETM +影像分类

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摘要

Texture feature is becoming more and more important for classification of remote sensing image, especially in remote sensing image in mountainous area. An approach to classification of western mountainous area of Zhejiang land cover using ETM+ imagery is described in this paper. Firstly the gradient images of research area were obtained using different edge detection methods with Roberts, Sobel, Prewitt and Canny operator using ETM+ pan image. The results of four different edge detection methods were evaluated qualitatively and quantitatively. The qualitative evaluations mainly considered the visual effect so that the results of combining edge images with original image for qualitative evaluation, and the edge points, 4-connected component quantities and 8-connected component quantities were adapted to quantitatively evaluate different edges. Then Canny operator was selected as the gradient operator according to the qualitative and quantitative evaluation results of different research area's edge images and fifteen texture features were obtained based on the Gray-Gradient Co-concurrency Matrix consequently through MATLAB programmer with the Canny operator. Finally, the classification results based on the spectral respond feature only and the texture feature with the spectral respond feature were evaluated separately. It shows that the texture features highlight the residents, rivers etc. which the geometric structure of space themselves are more obvious than others; enhancing the undulating the distinction between water and the shadow.
机译:纹理特征对于遥感图像的分类越来越重要,特别是在山区遥感图像中。本文介绍了一种利用ETM +影像对浙江西部山区进行土地分类的方法。首先,使用ETM +平移图像,使用Roberts,Sobel,Prewitt和Canny算子使用不同的边缘检测方法获得研究区域的梯度图像。定性和定量评估了四种不同边缘检测方法的结果。定性评估主要考虑视觉效果,因此将边缘图像与原始图像合并以进行定性评估的结果,以及调整边缘点,4个连接的组件数量和8个连接的组件数量以定量评估不同的边缘。然后根据不同研究区域边缘图像的定性和定量评估结果,选择Canny算子作为梯度算子,然后通过MATLAB程序员和Canny算子,基于灰度梯度并发矩阵,获得15个纹理特征。最后,分别评估仅基于光谱响应特征的分类结果和具有光谱响应特征的纹理特征。它表明,纹理特征突出了居民,河流等,而空间本身的几何结构比其他人更为明显;增强水和阴影之间起伏的区别。

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