Detecting Designated Building Areas From Remote Sensing Images Using Hierarchical Structural Constraints
Detecting Designated Building Areas From Remote Sensing Images Using Hierarchical Structural Constraints
复制标题
使用分层结构约束从遥感图像中检测指定建筑区域
DOI：
10.1007/s13320-019-0558-5
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发表时间：
2019-05
期刊：
影响因子：
--
通讯作者：
Yanyan Qin
中科院分区：
文献类型：
--
作者：
Fukun Bi;Mingyang Lei;Zhihua Yang;Jinyuan Hou;Yanyan Qin
建筑物区域的自动检测是遥感图像目标检测领域的一个研究热点。目标检测在诸如违法建筑监测、动态土地利用监测、反恐工作和军事侦察等任务中是迫切需要的。由于遥感场景的庞大和复杂性，现有的检测方法普遍存在效率低、检测精度差的问题。针对现有检测方法存在的问题，提出了一种基于层次结构约束的遥感图像DBA检测方法。我们的方法分两个主要阶段进行。(1)在关键点生成过程中，提出了一种基于结构模式描述子的关键点筛选方法。首先用多层局部模式直方图（MLPH）特征描述初始关键点的局部模式特征，然后用单类支持向量机（OC-SVM）对建筑物属性关键点进行筛选。(2)为了匹配筛选的关键点，我们提出了一种可靠的DBA检测方法的基础上匹配筛选的关键点的局部结构相似性。我们通过计算粗略匹配的关键点周围相邻区域的局部骨架结构的相似度来实现精确的关键点匹配，从而实现DBA检测。我们测试了所提出的方法对不同类型的建筑面积集，并在不同的时间阶段。实验结果表明，该方法具有较高的精度和计算效率。
Automatic detection of a designated building area (DBA) is a research hotspot in the field of target detection using remote sensing images. Target detection is urgently needed for tasks such as illegal building monitoring, dynamic land use monitoring, antiterrorism efforts, and military reconnaissance. The existing detection methods generally have low efficiency and poor detection accuracy due to the large size and complexity of remote sensing scenes. To address the problems of the current detection methods, this paper presents a DBA detection method that uses hierarchical structural constraints in remote sensing images. Our method was conducted in two main stages. (1) During keypoint generation, we proposed a screening method based on structural pattern descriptors. The local pattern feature of the initial keypoints was described by a multilevel local pattern histogram (MLPH) feature; then, we used one-class support vector machine (OC-SVM) merely to screen those building attribute keypoints. (2) To match the screened keypoints, we proposed a reliable DBA detection method based on matching the local structural similarities of the screened keypoints. We achieved precise keypoint matching by calculating the similarities of the local skeletal structures in the neighboring areas around the roughly matched keypoints to achieve DBA detection. We tested the proposed method on building area sets of different types and at different time phases. The experimental results show that the proposed method is both highly accurate and computationally efficient.
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DOI：
10.1007/978-1-4899-7687-1_810
发表时间：
2017
期刊：
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影响因子：
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作者：
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DOI：
10.1201/9781003139041-11
发表时间：
2021-03
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影响因子：
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作者：
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影响因子：
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DOI：
10.3390/s18020374
发表时间：
2018-01-27
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Velazquez-Pupo R;Sierra-Romero A;Torres-Roman D;Shkvarko YV;Santiago-Paz J;Gómez-Gutiérrez D;Robles-Valdez D;Hermosillo-Reynoso F;Romero-Delgado M
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DOI：
10.1023/b:visi.0000029664.99615.94
发表时间：
2004-11-01
影响因子：
19.5
作者：
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