發(fā)布時間：2018-08-26 14:08

【摘要】：土地覆蓋變化檢測是根據(jù)對同一物體或現(xiàn)象不同時間的觀測來確定地表覆蓋變化的處理過程,是根據(jù)不同時相的遙感圖像及其它多時相GIS數(shù)據(jù)來提取、描述目標(biāo)隨時間產(chǎn)生的變化,并定量分析、確定其變化內(nèi)容的理論和方法。它以GIS和RS為主要工具從多時相數(shù)據(jù)中提取人類活動和地球環(huán)境的動態(tài)信息,其結(jié)果常用于GIS空間數(shù)據(jù)庫的快速更新和空間數(shù)據(jù)挖掘。 隨著地理信息系統(tǒng)和趨于“三多”(多角度、多傳感器和多平臺)以及“三高”(高光譜分辨率、高空間分辨率和高時間分辨率)的遙感技術(shù)的快速發(fā)展,土地覆蓋變化檢測已成為空間信息科學(xué)中一個較為活躍的研究方向。面向?qū)ο笥跋穹治黾夹g(shù)(Object-Oriented Analysis, OOA)以對象為基本操作單元,是結(jié)合RS和GIS的一種新型的地學(xué)分析工具,廣泛且成功的應(yīng)用于以遙感圖像分類為代表的遙感影像分析處理之中,越來越多的受到土地覆蓋應(yīng)用研究者的青睞。 本文在全面分析、總結(jié)國內(nèi)外研究現(xiàn)狀的基礎(chǔ)上,介紹了變化檢測的基本概念、模型和處理步驟；總結(jié)了現(xiàn)有的土地覆蓋變化檢測方法,闡述了常用方法的原理,并對它們的優(yōu)缺點進(jìn)行了詳細(xì)的分析；研究了面向?qū)ο笥跋穹治龅南嚓P(guān)概念和理論以及多尺度圖像分割技術(shù),并以面向?qū)ο筮b感影像分析軟件eCognition(易康)中的多尺度圖像分割為例探討了基于多光譜遙感圖像的多尺度圖像分割中參與分割的圖像波段和各波段權(quán)重這兩組重要參數(shù)的確定。在此基礎(chǔ)上將面向?qū)ο筮b感影像分析技術(shù)引入以Landsat5TM中分辨率多光譜遙感影像為數(shù)據(jù)源的土地覆蓋變化檢測中,詳細(xì)對比了對象級變化檢測與傳統(tǒng)像元級變化檢測的性能指標(biāo)；分析探討了三種常用變化檢測方法的優(yōu)劣以及面向?qū)ο笸恋馗采w變化檢測的優(yōu)勢；結(jié)合變化檢測的發(fā)展趨勢,將LBV變換與傳統(tǒng)的變化矢量分析(CVA)相結(jié)合,提出了一種基于面向?qū)ο蟮腖BV-CVA變化檢測法。通過實驗,證明該方法能有效提高土地覆蓋變化檢測的精度,降低檢測誤差。最后對本文的研究成果進(jìn)行了總結(jié),指出了在研究中有待改進(jìn)的地方,對以后的研究工作進(jìn)行了展望。
[Abstract]:Land cover change detection is a processing process to determine land cover change based on the observation of the same object or phenomenon at different times. It is extracted from remote sensing images and other multitemporal GIS data. Describe the change of target over time, and determine the theory and method of change content. It uses GIS and RS as the main tools to extract the dynamic information of human activities and the earth environment from the multitemporal data. The results are often used in the rapid updating of GIS spatial database and spatial data mining. With the rapid development of geographic information systems and remote sensing technologies that tend to be "multi-angle, multi-sensor and multi-platform" and "three high" (hyperspectral resolution, high spatial resolution and high temporal resolution), Land cover change detection has become an active research direction in spatial information science. Object oriented image analysis (Object-Oriented Analysis, OOA) is a new geoscience analysis tool combined with RS and GIS. It is widely and successfully applied to remote sensing image analysis and processing represented by remote sensing image classification. More and more researchers are interested in land cover application. On the basis of comprehensive analysis and summarization of current research situation at home and abroad, this paper introduces the basic concepts, models and processing steps of change detection, summarizes the existing methods of land cover change detection, and expounds the principles of common methods. The advantages and disadvantages of them are analyzed in detail, and the related concepts and theories of object oriented image analysis and multi-scale image segmentation technology are studied. Taking the multi-scale image segmentation in object oriented remote sensing image analysis software eCognition as an example, the determination of the two important parameters of image band and the weight of each band in multi-scale image segmentation based on multispectral remote sensing image is discussed. On this basis, the object oriented remote sensing image analysis technology is introduced into the land cover change detection based on Landsat5TM resolution multispectral remote sensing image, and the performance indexes of object level change detection and traditional pixel level change detection are compared in detail. The advantages and disadvantages of three commonly used change detection methods and the advantages of object-oriented land cover change detection are analyzed and discussed, and the LBV transform is combined with the traditional change vector analysis (CVA) according to the development trend of change detection. An object-oriented LBV-CVA change detection method is proposed. The experimental results show that the method can effectively improve the accuracy of land cover change detection and reduce the detection error. Finally, this paper summarizes the research results, points out the areas to be improved in the research, and looks forward to the future research work.
【學(xué)位授予單位】：河南工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：P208;P237

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