单波段单极化高分辨率SAR图像纹理分类研究

以2003年7月淮河洪水监测获取的高分辨率SAR图像为试验数据，首先对数据进行了分析，指出了高分辨率SAR图像的特点，之后通过小波变换对图像进行两层小波分解得到子图像，并在选择合适的能量计算窗口条件下，计算子图像的纹理能量，最后使用了BP神经网络方法进行纹理分类。研究结果表明，采用小波纹理分类方法对高分辨率SAR图像分类是可行的，可以获得高的分类精度，同时也指出了纹理分类的不足和进一步研究的方向。

THE APPLICATION OF THE WAVELET TEXTURE METHOD TOTHE CLASSIFICATION OF SINGLE-BAND, SINGLE-POLARIZEDAND HIGH-RESOLUTION SAR IMAGES

This paper has classified the high-resolution SAR images obtained along the Huaihe River in July 2003. The authors first analyzed the high-resolution SAR image and pointed out the disturbing factors. After filtering the noise, the SAR image was decomposed with wavelet transform and the energy of the textures was computed by selecting a small window. Finally, the BP algorithm was used to classify the textures. The results indicate that, with the wavelet texture classification method, the high classification precision for the single-band, single-polarized and high-resolution SAR images can be obtained. This paper has also analyzed the shortages of the texture classification method for high-resolution SAR images and pointed out the research direction of the high-resolution SAR image classification.