• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Malaysian Journal of Computer Science >A New Curvelet-Based Texture Classification Approach for Land Cover Recognition of SAR Satellite Images
【24h】

A New Curvelet-Based Texture Classification Approach for Land Cover Recognition of SAR Satellite Images

机译：基于曲波的纹理分类新方法用于SAR卫星图像土地覆盖识别

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Texture recognition of synthetic aperture radar (SAR) images, an important technique in the remote sensing area, has been deeply interested in the past decade. It is a key method to analyze this special case of images in practical applications. Watershed transform seems to be a proper method utilized to segment images. However, speckle noise in SAR images and the low resolution of edges make the segmentation and texture recognition difficult in a watershed transformation. Because of excellent results from curvelet transform in feature extraction and filtering as well as watershed advantages in image segmentation, an efficient method to recognize and segment various textures in SAR images is proposed. In this paper, a new algorithm for texture recognition of SAR images is presented. Four main steps in texture recognition of SAR images have been developed in the proposed algorithm. First, the curvelet transform is applied to the SAR image so that the existent image noise is reduced as much as possible. In the second step, the features of various textures in SAR image are extracted based on sub-bands from curvelet transform. In the third step, a label matrix based on the extracted features is formed by the watershed transform. In this matrix, a label is given to a single texture in SAR image which represents watershed regions. Finally, by applying watershed transform tothe matrix, the textures of SAR image are classified and recognized. The proposed scheme has been tested on both agricultural and urban SAR images. Experimental results demonstrate the efficiency of the proposed approach in texture recognition of SAR imagery.
机译：合成孔径雷达（SAR）图像的纹理识别是遥感领域的一项重要技术，在过去十年中引起了人们的极大兴趣。在实际应用中，分析图像的特殊情况是一种关键方法。分水岭变换似乎是用于分割图像的适当方法。但是，SAR图像中的斑点噪声和边缘的低分辨率使分水岭变换中的分割和纹理识别变得困难。鉴于曲波变换在特征提取和滤波中的优异效果以及图像分割中的分水岭优势，提出了一种有效的识别和分割SAR图像中各种纹理的方法。本文提出了一种新的SAR图像纹理识别算法。该算法开发了SAR图像纹理识别的四个主要步骤。首先，将Curvelet变换应用于SAR图像，以尽可能减少现有的图像噪声。第二步，基于来自Curvelet变换的子带，提取SAR图像中各种纹理的特征。第三步，通过分水岭变换形成基于提取特征的标记矩阵。在该矩阵中，为代表分水岭区域的SAR图像中的单个纹理提供了标签。最后，通过对矩阵进行分水岭变换，对SAR图像的纹理进行分类和识别。该建议方案已在农业和城市SAR图像上进行了测试。实验结果证明了该方法在SAR图像纹理识别中的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号