本文選題：高分辨率　切入點：變化檢測　出處：《北京師范大學》2014年碩士論文　論文類型：學位論文

【摘要】：高分辨率遙感影像成像清晰、紋理豐富、定位精準、地物內部異質性大，這些特點都使得傳統(tǒng)的基于像元級別的變化檢測方法存在不能充分利用影像信息、分類精度低、速度慢等局限性，還容易造成空間數據的大量冗余和資源的浪費，不能夠很好的適應高分辨率影像的特點。因此研究高分辨率影像的變化檢測方法具有較強的現(xiàn)實意義。 本文以北京市通州區(qū)2008年10月31日和2010年11月1日的IKONOS影像為例，采用基于面向對象的方法，對高分辨率遙感影像的變化檢測方法進行了相關的試驗研究。首先，針對eCognition軟件的多尺度分割算法，采用與鄰域絕對均值差分方差比方法（Ratio of Mean Difference to Neighbors(ABS) to StandardDeviation，RMAS）方法確定了各類地物的最佳分割尺度，并對其進行了驗證。然后，利用分割結果，生成面向對象的相關關系影像（Object Correlation Images,OCIs），并分別采用多變量自動提取二值化閾值方法和決策樹分類方法，探究該特征影像在變化檢測中的作用。 結果表明，RMAS方法可以比較準確的確定各類地物的分割尺度。與傳統(tǒng)的單變量提取二值化閾值方法相比，多變量自動提取二值化閾值方法的變化檢測精度有了很大的提高。同時， OCIs影像多變量二值化的變化檢測結果也要好于多變量的差值影像。其中，對于差值影像，第一波段的變化檢測精度的KAPPA系數為0.7，第二波段為0.69，第三波段為0.72，第四波段為0.76，而四個波段合起來的變化檢測精度的KAPPA系數達0.87；對于OCI影像，單個相關系數波段的變化檢測精度的KAPPA系數為0.86，，斜率的為0.7，截距的為0.63，而三者合起來的變化檢測精度KAPPA系數為0.89。基于OCIs特征影像的決策樹分類方法與其他方法相比，分類結果要好。其中，基于象元的決策樹分類總體精度為81%，KAPPA系數為0.71；面向對象的決策樹分類總體精度為88%，KAPPA系數為0.75；而基于OCIs的最鄰近分類的總體精度為87%，KAPPA系數為0.81；基于OCIs的決策樹分類的總體精度為88%，KAPPA系數為0.82。基于通過以上研究發(fā)現(xiàn)，基于面向的變化檢測方法的變化檢測精度要高于基于象元的方法，另外，面向對象的方法可以有效的避免椒鹽現(xiàn)象。
[Abstract]:High resolution remote sensing images are characterized by clear image, rich texture, accurate location and large internal heterogeneity of ground objects. These characteristics make traditional pixel level based change detection methods can not make full use of image information, and the classification accuracy is low. Because of the limitation such as slow speed, large amount of redundancy of spatial data and waste of resources, it can not adapt to the characteristics of high-resolution image. Therefore, it is of great practical significance to study the change detection method of high-resolution image. In this paper, the IKONOS images of Tongzhou District in Beijing are taken as an example, and the change detection methods of high-resolution remote sensing images are studied based on object-oriented method. In view of the multi-scale segmentation algorithm of eCognition software, the ratio of Mean Difference to neighborhood absolute difference variance ratio method is used to determine the best segmentation scale of all kinds of ground objects, and it is verified. Then, the segmentation results are used. The object Correlation images are generated, and the binary threshold method and decision tree classification method are used to explore the role of the feature image in change detection. The results show that the RMAS method can accurately determine the segmentation scale of all kinds of ground objects. The change detection accuracy of multivariable automatic binary threshold extraction method has been greatly improved. At the same time, the change detection result of multivariable binarization of OCIs image is better than that of multivariable differential image. The KAPPA coefficient of the first band is 0.7, the second band is 0.69, the third band is 0.72, the 4th band is 0.76, and the KAPPA coefficient of the four bands combined is 0.87. For OCI images, The KAPPA coefficient, slope and intercept of a single correlation coefficient band are 0.86, 0.7, 0.63, respectively, while the KAPPA coefficient of variation detection accuracy is 0.89. The decision tree classification method based on OCIs feature image is compared with other methods. The classification results are good. Among them, The overall accuracy of decision tree classification based on pixel is 81kappa coefficient is 0.71; that of object-oriented decision tree classification is 88kappa coefficient 0.75; and that of nearest neighbor classification based on OCIs is 87kappa coefficient 0.81; decision tree classification based on OCIs is decision tree classification based on OCIs. The overall accuracy of KAPPA is 0.82.Based on the results of the above study, The accuracy of change detection based on object-oriented change detection method is higher than that based on pixel method. In addition, object-oriented method can effectively avoid salt and pepper phenomenon.
【學位授予單位】：北京師范大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：P237

【參考文獻】

相關期刊論文 前9條

1 譚衢霖;劉正軍;沈偉;;一種面向對象的遙感影像多尺度分割方法[J];北京交通大學學報;2007年04期

2 王大鵬;王周龍;李德一;;基于NDVI紋理的山東丘陵地區(qū)SPOT-5影像果園信息識別研究[J];測繪科學;2007年01期

3 周啟鳴;;多時相遙感影像變化檢測綜述[J];地理信息世界;2011年02期

4 陳鑫鏢;;遙感影像變化檢測技術發(fā)展綜述[J];測繪與空間地理信息;2012年09期

5 張曉東;李德仁;龔健雅;秦前清;;遙感影像與GIS分析相結合的變化檢測方法[J];武漢大學學報(信息科學版);2006年03期

6 黃慧萍;吳炳方;;地物大小、對象尺度、影像分辨率的關系分析[J];遙感技術與應用;2006年03期

7 陳晉,何春陽,史培軍,陳云浩,馬楠;基于變化向量分析的土地利用/覆蓋變化動態(tài)監(jiān)測(Ⅰ)——變化閾值的確定方法[J];遙感學報;2001年04期

8 ;Object-oriented Urban Dynamic Monitoring——A Case Study of Haidian District of Beijing[J];Chinese Geographical Science;2007年03期

9 楊勝;李敏;彭振國;馮春;;一種新的多波段遙感影像變化檢測方法[J];中國圖象圖形學報;2009年04期

本文編號：1610007