An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From High-Resolution Satellite Images in Urban Areas
An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From High-Resolution Satellite Images in Urban Areas
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DOI：
10.1109/lgrs.2017.2704120
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发表时间：
2017-06
影响因子：
4.8
通讯作者：
Z. Lv;Yonghong Jia;Qian Zhang;Yifu Chen
中科院分区：
文献类型：
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作者：
Z. Lv;Yonghong Jia;Qian Zhang;Yifu Chen
尽管它有能力处理遮挡和噪声，但稀疏跟踪可能不足以描述复杂的噪声损坏，例如，在城市道路跟踪中，在高分辨率（HR）卫星图像中，道路表面经常被存在的遮挡和噪声严重破坏。为了解决这个问题，本文提出了一种从HR卫星图像中提取道路的半自动方法。首先，引入多特征稀疏模型来表示道路目标的外观；其次，采用一种新的稀疏约束正则化均值偏移算法来支持道路跟踪。此外，采用一种新的可靠度度量，通过加权多个特征的贡献来组合多个特征，以区分目标和背景。实验证实，该方法在可靠性、鲁棒性和准确性方面优于当前最先进的从HR图像中提取道路的方法。
Despite its ability to handle occlusions and noise, sparse tracking may be inadequate to describe complex noise corruption, for instance, in urban road tracking, where road surfaces are often significantly disrupted by the existence of occlusions and noise in high-resolution (HR) satellite imagery. To address this issue, this letter presents a semiautomatic approach for road extraction from HR satellite images. Firstly, a multifeature sparse model is introduced to represent the road target appearance. Next, a novel sparse constraint regularized mean-shift algorithm is used to support the road tracking. Furthermore, multiple features are combined by weighting their contributions using a novel reliability measure derived to distinguish target from background. The experiments confirm that the proposed method performs better than the current state-of-the-art methods for the extraction of roads from HR imagery, in terms of reliability, robustness, and accuracy.