發(fā)布時(shí)間：2018-03-14 06:19

  本文選題：高分辨率　切入點(diǎn)：變化檢測　出處：《北京師范大學(xué)》2014年碩士論文　論文類型：學(xué)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(zhǔn)、地物內(nèi)部異質(zhì)性大，這些特點(diǎn)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(shù)據(jù)的大量冗余和資源的浪費(fèi)，不能夠很好的適應(yīng)高分辨率影像的特點(diǎn)。因此研究高分辨率影像的變化檢測方法具有較強(qiáng)的現(xiàn)實(shí)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?qū)ο蟮姆椒�，對高分辨率遙感影像的變化檢測方法進(jìn)行了相關(guān)的試驗(yàn)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(jìn)行了驗(yàn)證。然后，利用分割結(jié)果，生成面向?qū)ο蟮南嚓P(guān)關(guān)系影像（Object Correlation Images,OCIs），并分別采用多變量自動(dòng)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(jié)果表明，RMAS方法可以比較準(zhǔn)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(dòng)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(shí)， OCIs影像多變量二值化的變化檢測結(jié)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(shù)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(gè)波段合起來的變化檢測精度的KAPPA系數(shù)達(dá)0.87；對于OCI影像，單個(gè)相關(guān)系數(shù)波段的變化檢測精度的KAPPA系數(shù)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(shù)為0.89�；贠CIs特征影像的決策樹分類方法與其他方法相比，分類結(jié)果要好。其中，基于象元的決策樹分類總體精度為81%，KAPPA系數(shù)為0.71；面向?qū)ο蟮臎Q策樹分類總體精度為88%，KAPPA系數(shù)為0.75；而基于OCIs的最鄰近分類的總體精度為87%，KAPPA系數(shù)為0.81；基于OCIs的決策樹分類的總體精度為88%，KAPPA系數(shù)為0.82�；谕ㄟ^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?qū)ο蟮姆椒ǹ梢杂行У谋苊饨符}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(xué)位授予單位】：北京師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：P237

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