用于自動(dòng)識(shí)別遙感圖像路網(wǎng)信息的改進(jìn)模糊連接度方法
doi: 10.11999/JEIT150563
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1.
(湖南大學(xué)建筑學(xué)院 長(zhǎng)沙 410082) ②(湖南大學(xué)電氣與信息工程學(xué)院 長(zhǎng)沙 410082)
An Improved Fuzzy Connectedness Method to Recognize Automatically the Road Network Information from Remote Sensing Image
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1.
(College of Architecture, Hunan University, Changsha 410082, China)
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2.
(College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)
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摘要: 針對(duì)遙感圖像中路網(wǎng)信息的自動(dòng)識(shí)別問(wèn)題，該文將小波模極大值邊緣檢測(cè)方法和模糊連接度方法結(jié)合，提出一種改進(jìn)的模糊連接度方法。采用小波模極大值邊緣檢測(cè)方法進(jìn)行模糊連接度種子點(diǎn)的自動(dòng)選擇，解決傳統(tǒng)模糊連接度理論中種子點(diǎn)難以自動(dòng)選擇的問(wèn)題。在此基礎(chǔ)上，對(duì)傳統(tǒng)的模糊相似度計(jì)算公式進(jìn)行簡(jiǎn)化，在保證路網(wǎng)識(shí)別準(zhǔn)確性的同時(shí)，大大減少了計(jì)算量。采用來(lái)自Quickbird高分辨商業(yè)遙感衛(wèi)星的3組影像進(jìn)行實(shí)驗(yàn)，驗(yàn)證了該文提出的路網(wǎng)識(shí)別方法具有較高的準(zhǔn)確性和計(jì)算速度。
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關(guān)鍵詞:
- 遙感圖像 /
- 路網(wǎng)信息識(shí)別 /
- 模糊連接度 /
- 小波模極大值 /
- 圖像邊緣檢測(cè)
Abstract: To recognize automatically road network from remote sensing image, an improved fuzzy connectedness method is proposed by combining traditional fuzzy connectedness theory with wavelet modulus maximum algorithm. The wavelet modulus maximum image edge detection algorithm is used to solve the problem of selecting seed points automatically in traditional fuzzy connectedness theory. On this basis, traditional fuzzy similarity computational formula is simplified. This can reduce the cost of calculation greatly without reducing the recognition accuracy. Three high-resolution remote sensing images from the satellite Quickbird are processed in the experiments to prove the effectiveness of the proposed method. The results show that the proposed road network recognition method has high accuracy and rapid computation speed. -
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