An automatic bridge detection technique for multispectral images
An automatic bridge detection technique for multispectral images
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DOI：
10.1109/tgrs.2008.923631
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发表时间：
2008-09-01
影响因子：
8.2
通讯作者：
Samal, Ashok
中科院分区：
文献类型：
--
作者：
Chaudhuri, D.;Samal, Ashok
自遥感早期以来，从图像中提取特征一直是研究人员的目标。虽然在一些应用中已经取得了重大进展，但在准确识别建筑物和道路等高级特征领域仍有许多工作要做。本文提出了一种从多光谱图像检测水体上桥梁的方法。首先使用基于多种子监督分类技术的多数必须授予逻辑将多光谱图像分为八种土地覆盖类型。然后将分类图像分类为三级图像：水、混凝土和背景。然后，通过使用基于知识的方法在该三级图像中识别桥梁，该方法使用五步法利用桥梁及其周围环境的空间排列。河流提取模块使用递归扫描技术和几何约束来识别河流。使用邻域算子和典型桥梁的空间尺寸知识，我们识别可能的桥梁像素。然后，根据这些潜在的桥像素的连通性和几何特性，将其分组为可能的桥段。最后，根据不同方向的定向水指数及其与路段的连通性对这些桥段进行验证。本文提出的方法已通过空间分辨率为 23.5 x 23.5 m 的 IRS-1C/1-D 卫星图像进行实施和测试。结果表明，该方法在提取桥梁方面既高效又有效。
Extraction of features from images has been a goal of researchers since the early days of remote sensing. While significant progress has been made in several applications, much remains to be done in the area of accurate identification of high-level features such as buildings and roads. This paper presents an approach for detecting bridges over water bodies from multispectral imagery. The multispectral image is first classified into eight land-cover types using a majority-must-be-granted logic based on the multiseed supervised classification technique. The classified image is then categorized into a trilevel image: water, concrete., and background. Bridges are then recognized in this trilevel image by using a knowledge-based approach that exploits the spatial arrangement of bridges and their surroundings using a five-step approach. A river extraction module identifies the rivers using a recursive scanning technique and geometric constraints. Using a neighborhood operator and the knowledge of the spatial dimensions of a typical bridge, we identify the possible bridge pixels. These potential bridge pixels are then grouped into possible bridge segments based on their connectivity and geometric properties. Finally, these bridge segments are verified on the basis of directional water index along different directions and their connectivity with the road segments. The approach proposed in this paper has been implemented and tested with images from the IRS-1C/1-D satellite that has a spatial resolution of 23.5 x 23.5 m. The results show that this approach is both efficient and effective in extracting bridges.