• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>GIScience & remote sensing >Channel bar feature extraction for a mining-contaminated river using high-spatial multispectral remote-sensing imagery
【24h】

Channel bar feature extraction for a mining-contaminated river using high-spatial multispectral remote-sensing imagery

机译：使用高空间多光谱遥感影像提取受污染的河流的河道特征

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

Mapping and monitoring changes of geomorphological features over time are important for understanding fluvial process and effects of its controlling factors. Using high spatial resolution multispectral images has become common practice in the mapping as these images become widely available. Traditional pixel-based classification relies on statistical characteristics of single pixels and performs poorly in detailed mapping using high resolution multispectral images. In this work, we developed a hybrid method that detects and maps channel bars, one of the most important geomorphological features, from high resolution multispectral aerial imagery. This study focuses on the Big River which drains the Ozarks Plateaus region in southeast Missouri and the Old Lead Belt Mining District which was one of the largest producers of lead worldwide in the early and middle 1900s. Mapping and monitoring channel bars in the Big River is essential for evaluating the fate of contaminated mining sediment released to the Big River. The dataset in this study is 1m spatial resolution and is composed of four bands: Red (Band 3), Green (Band 2), Blue (Band 1) and Near-Infrared (Band 4). The proposed hybrid method takes into account both spectral and spatial characteristics of single pixels, those of their surrounding contextual pixels and spatial relationships of objects. We evaluated its performance by comparing it with two traditional pixel-based classifications including Maximum Likelihood (MLC) and Support Vector Machine (SVM). The findings indicate that derived characteristics from segmentation and human knowledge can highly improve the accuracy of extraction and our proposed method was successful in extracting channel bars from high spatial resolution images.
机译：随时间推移绘制和监视地貌特征的变化对于了解河流过程及其控制因素的影响非常重要。随着这些图像变得广泛可用，使用高空间分辨率的多光谱图像已成为映射中的普遍做法。传统的基于像素的分类依赖于单个像素的统计特性，并且在使用高分辨率多光谱图像的详细映射中表现不佳。在这项工作中，我们开发了一种混合方法，可从高分辨率多光谱航空影像中检测并绘制通道条，这是最重要的地貌特征之一。这项研究的重点是流经密苏里州东南部Ozarks高原地区的大河和老铅带矿区，后者是1900年代初和中期全球最大的铅生产国之一。在大河中绘制和监视河道条对于评估释放到大河中的受污染的采矿沉积物的命运至关重要。本研究中的数据集为1m空间分辨率，由四个波段组成：红色（波段3），绿色（波段2），蓝色（波段1）和近红外（波段4）。所提出的混合方法考虑了单个像素的光谱和空间特性，其周围上下文像素的光谱和空间特性以及对象的空间关系。我们通过将其与两个基于像素的传统分类（包括最大似然（MLC）和支持向量机（SVM））进行比较来评估其性能。这些发现表明，从分割和人类知识中得出的特征可以大大提高提取的准确性，并且我们提出的方法成功地从高分辨率图像中提取了通道条。

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