Geometric Parameters Extraction of Building Based on Shape Prior
Funds:
The National Natural Science Foundation of China (61302170)
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摘要: 基于單幅高分辨率遙感圖像的建筑物幾何參數(shù)提取結(jié)果的準(zhǔn)確性，容易受圖像背景、圖像噪聲以及灰度分布相似性的干擾，形成錯(cuò)誤的提取結(jié)果。針對(duì)這一問(wèn)題，該文提出一種新的基于形狀先驗(yàn)的變分水平集提取方法，該方法同時(shí)使用圖像邊緣信息、區(qū)域灰度信息以及包含屋頂和側(cè)立面的形狀先驗(yàn)信息，實(shí)現(xiàn)單幅遙感圖像中建筑物的幾何參數(shù)的提取。實(shí)驗(yàn)結(jié)果表明，該方法能夠更加準(zhǔn)確地提取建筑物，最后得到的幾何參數(shù)比較接近真值，并且由于更加充分地使用了全局形狀信息該方法能更好地抵制側(cè)立面的干擾，具有很強(qiáng)的魯棒性。
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關(guān)鍵詞:
- 遙感圖像處理 /
- 建筑物幾何參數(shù)提取 /
- 變分水平集模型 /
- 曲線(xiàn)演化 /
- 形狀先驗(yàn)
Abstract: Due to algorithms for buildings geometric parameters extraction from single high remote sensing image usually suffer from disturbances of background, noise and intensity similarity, resulting in wrong extraction results. In this paper, a novel variational level set model is proposed based on shape prior which integrates edge, gray, and shape prior including roof and facade, to extract buildings geometric parameters from a single high resolution remote sensing image. Experimental results show that the proposed method can extract buildings geometric parameters accurately. Moreover, it has strong robustness with respect to the disturbance of facade due to the more sufficient utilization of the whole shape prior. -
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