本文選題：不透水面 + 自動提取��； 參考：《地球信息科學學報》2017年10期

【摘要】：利用多源遙感數(shù)據(jù)提取不透水面信息是一個重要的研究方向。針對以往研究中多需要人工選取不透水面樣本進行模型訓練的問題,本文通過整合夜間燈光遙感與Landsat TM影像中的空間和光譜信息實現(xiàn)了不透水面覆蓋范圍(Impervious Surface Area,ISA)的自動提取。首先根據(jù)夜間燈光的分布來定位ISA聚集的城市區(qū)域的位置,分別在城市區(qū)域內(nèi)部和外部自動提取可靠性高的ISA及非ISA樣本,然后通過迭代分類提取城市區(qū)域的ISA,再以此為樣本對城市區(qū)域外部進行分類,最后將分類結果整合完成整幅影像的ISA提取流程。應用本方法對美國雪城地區(qū)的DMSP/OLS夜間燈光影像上提取了84個城市區(qū)域,提取精度大于95%。從中分別選擇高ISA密度和低ISA密度的2個城市區(qū)域作為ISA提取的測試區(qū),本文方法在城市區(qū)域內(nèi)的ISA提取總體精度與kappa系數(shù)分別為88.23%和0.63;在城市區(qū)域外部為78.6%和0.54,均優(yōu)于人工樣本選取方法的提取精度,表明該方法能夠實現(xiàn)精度穩(wěn)定且高效的ISA自動提取。
[Abstract]:Extracting impermeable surface information from multi-source remote sensing data is an important research direction. In order to solve the problem of artificial selection of impermeable surface samples for model training in previous studies, this paper realizes automatic extraction of impervious Surface area by integrating spatial and spectral information from night light remote sensing and Landsat TM images. Firstly, according to the distribution of night lights, the location of the urban area where ISA is gathered is located, and the highly reliable ISA and non- samples are automatically extracted from the inner and outer parts of the urban area, respectively. Then the ISAs of the urban area are extracted by iterative classification, and then the ISA extraction process of the whole image is completed by using the ISAs as samples to classify the exterior of the urban area. This method is used to extract 84 urban areas from the DMSP/OLS night light image of the American Snow City area, and the extraction accuracy is more than 95%. Two urban areas with high ISA density and low ISA density were selected as test areas for ISA extraction. The total precision and kappa coefficient of ISA extraction in urban areas were 88.23% and 0.63 respectively, and those outside urban areas were 78.6% and 0.54 respectively, which were better than those of artificial sample selection method. It shows that this method can achieve stable precision and efficient automatic ISA extraction.
【作者單位】： 成都理工大學地球物理學院;浙江工業(yè)大學計算機與技術學院;中國科學院遙感與數(shù)字地球研究所;中國科學院大學;
【基金】：國家重點研發(fā)項目(2017YFB0504204、2016YFB0502502) 國家自然科學基金項目(41301488)
【分類號】：P237
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本文編號：1810339