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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From High-Resolution Satellite Images in Urban Areas
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An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From High-Resolution Satellite Images in Urban Areas

机译：基于自适应多特征稀疏性的城市高分辨率卫星图像半自动道路提取模型

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摘要

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.
机译：尽管它具有处理遮挡和噪声的能力，但稀疏跟踪可能不足以描述复杂的噪声破坏，例如，在城市道路跟踪中，高分辨率（HR）卫星中的遮挡和噪声的存在经常严重干扰路面图像。为了解决这个问题，这封信提出了一种从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外观。接下来，使用一种新颖的稀疏约束正则化均值漂移算法来支持道路跟踪。此外，多个特征通过使用新颖的可靠性度量权衡它们的贡献来组合，该新颖的可靠性度量被导出以区分目标与背景。实验证实，该方法在可靠性，鲁棒性和准确性方面，比从HR图像中提取道路的方法要好于当前的最新方法。

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