Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection
Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection
复制标题
用于构建区域检测的多尺度框架中基于块的图像特征的表示
DOI：
10.3390/rs8020155
复制
发表时间：
2016-02-01
期刊：
影响因子：
5
通讯作者：
Wu, Guofeng
中科院分区：
文献类型：
--
作者：
Hu, Zhongwen;Li, Qingquan;Wu, Guofeng
城市建成区的精确提取和制图在许多社会、经济和环境研究中起着重要作用。在本文中，我们提出了一种新的方法，从高空间分辨率的遥感图像的建成区检测，使用基于块的多尺度特征表示框架。首先将图像分割成小块，提取小块的光谱、纹理和结构特征，并采用多尺度框架表示，然后使用一组改进的Harris角点选择小块作为训练样本;最后，通过最小化到训练样本的归一化光谱、纹理和结构距离来获得构建索引图像，并且通过对索引图像进行阈值化来获得建成区图。实验结果表明，该方法对不同场景、不同空间分辨率的高分辨率光学和合成孔径雷达图像都是有效的。
The accurate extraction and mapping of built-up areas play an important role in many social, economic, and environmental studies. In this paper, we propose a novel approach for built-up area detection from high spatial resolution remote sensing images, using a block-based multi-scale feature representation framework. First, an image is divided into small blocks, in which the spectral, textural, and structural features are extracted and represented using a multi-scale framework; a set of refined Harris corner points is then used to select blocks as training samples; finally, a built-up index image is obtained by minimizing the normalized spectral, textural, and structural distances to the training samples, and a built-up area map is obtained by thresholding the index image. Experiments confirm that the proposed approach is effective for high-resolution optical and synthetic aperture radar images, with different scenes and different spatial resolutions.