• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Classification of Very High Spatial Resolution Imagery Based on a New Pixel Shape Feature Set
【24h】

Classification of Very High Spatial Resolution Imagery Based on a New Pixel Shape Feature Set

机译：基于新像素形状特征集的超高分辨率图像的分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This letter presents a novel spatial features extraction method for the high spatial resolution multispectral imagery (HSRMI) classification. First, Canny filter algorithm is applied to extract the edge information to obtain the fuzzy edge map. Secondly, adaptive threshold value for each pixel's homogeneous region (PHR) calculation is determined based on the fuzzy edge map and original image. Next, the PHR for every pixel is obtained based on the fuzzy edge map, adaptive threshold value and original image. And then, the pixel shape feature set (PSFS) is extracted based on the PHR. Lastly, SVM classifier is applied to classify the hybrid spectral and PSFS. Two different experiments were performed to evaluate the performance of PSFS, in comparison with spectral, gray level co-occurrence matrix (GLCM) and the existing pixel shape index (PSI). Experimental results indicate that the PSFS achieved the highest accuracy, hence, providing an effective spectral-spatial classification method for the HSRMI.
机译：这封信提出了一种用于高空间分辨率多光谱图像（HSRMI）分类的新颖空间特征提取方法。首先，使用Canny滤波算法提取边缘信息以获得模糊边缘图。其次，基于模糊边缘图和原始图像确定用于每个像素的均质区域（PHR）计算的自适应阈值。接下来，基于模糊边缘图，自适应阈值和原始图像获得每个像素的PHR。然后，基于PHR提取像素形状特征集（PSFS）。最后，使用SVM分类器对混合频谱和PSFS进行分类。与光谱，灰度共现矩阵（GLCM）和现有像素形状指数（PSI）相比，进行了两个不同的实验来评估PSFS的性能。实验结果表明，PSFS达到了最高的准确性，因此为HSRMI提供了一种有效的光谱空间分类方法。

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