基于紋理和地形輔助的山區(qū)土地利用信息提取研究
發(fā)布時間：2018-05-01 05:32
本文選題：遙感 + 面向?qū)ο蠓诸?/strong>�。� 參考：《四川農(nóng)業(yè)大學》2014年碩士論文
【摘要】：隨著遙感傳感器的飛速發(fā)展,遙感影像的空間分辨率得到大幅度的提升,地表成像細節(jié)變得更加清晰,但傳統(tǒng)的遙感分類技術(shù)基于像元,不能充分利用高分辨影像豐富的圖像特征,故其分類精度和準確性還不能滿足土地調(diào)查的需要。山區(qū)土地利用類型多樣,地形地貌復雜,土地利用分散,信息提取困難,如何采用合理有效的方法實現(xiàn)山區(qū)高分辨率遙感影像土地利用信息的準確、快速提取已成為土地利用調(diào)查中迫切需要解決的問題。本文選擇位于川西南攀枝花市西區(qū)的典型農(nóng)村山區(qū)為研究區(qū),以全色波段分辨率為1m,多光譜波段分辨率為4m的IKONOS高分辨率遙感影像為數(shù)據(jù)源,基于ERDAS 9.2、ENVI4.8、eCognition8.0和ArcGIS 9.3等軟件平臺,采用面向?qū)ο蠓诸惙椒?輔以紋理和地形因子的參與,最終實現(xiàn)研究區(qū)土地利用信息的準確、快速提取。主要研究結(jié)論如下：(1)通過最優(yōu)指數(shù)法(Optimum Index Factor, OIF)對研究區(qū)IKONOS高分辨遙感影像最佳波段組合方式進行研究。結(jié)果表明,研究區(qū)遙感影像最優(yōu)波段組合方式為2、3、4(GRN、RED、NIR)波段組合,其OIF值最大,達到102.88,該組合影像-地物色調(diào)反差較明顯,較好的反映了研究區(qū)土地利用信息。(2)本文設(shè)定了三個分割層次(level1、Ievel2、level3)對研究區(qū)遙感影像進行多尺度分割,并通過影像特征分析法和均值方差法對每個層次的最優(yōu)分割尺度進行了探討。結(jié)果表明,level1層的最佳尺度為20,可較好的對山區(qū)居民點和裸地進行分割；level2層的最佳尺度為45,可較好的對山區(qū)坑塘、有作物耕地、無作物耕地、其他林地和園地進行分割；level3層的最佳尺度為80,可較好的對山區(qū)水庫、河流、有林地、荒草地、道路進行分割。(3)依據(jù)香農(nóng)信息熵(Shannon entropy)原理篩選出了Homo(同質(zhì)性)Con(對比度)、Ent(熵)、Asm(二階距)四個紋理指標輔助本文山區(qū)土地利用信息的提取研究,并對紋理輔助分割的效果進行了對比分析。結(jié)果表明,紋理參與分割過程很好的改善了圖像分割的效果,紋理參與分割后的影像,同一類地物內(nèi)部其分割后多邊形破碎度明顯降低,分割過程對大面積地物的邊界信息予以充分考慮,地物的整體性得到體現(xiàn)。此外,多邊形的數(shù)目明顯減少,減少幅度為44.27%,無形中大大提高了影像解譯的效率。(4)基于地形和紋理輔助的面向?qū)ο蠓诸惛骶仍u價指標較傳統(tǒng)監(jiān)督分類都有較大的提升,充分體現(xiàn)了該分類方法在山區(qū)土地利用信息提取中的優(yōu)越性。其中,分類總體精度達到90.57%,較傳統(tǒng)監(jiān)督分類提高17.92%；Kappa系數(shù)值達到0.8892,較傳統(tǒng)分類提高0.1879；各地類的生產(chǎn)者精度和用戶精度較傳統(tǒng)分類都有不同程度的提高；面向?qū)ο蠓诸惤Y(jié)果中各地類面積與實地調(diào)研地類面積更為接近,也較好的反映了該分類的準確性。
[Abstract]:With the rapid development of remote sensing sensors, the spatial resolution of remote sensing images has been greatly improved, and the surface imaging details become clearer, but the traditional remote sensing classification technology is based on pixels. The classification accuracy and accuracy of high resolution images can not meet the needs of land survey because they can not make full use of the rich image features of high resolution images. There are various types of land use, complex topography and geomorphology, scattered land use and difficult information extraction in mountainous areas. How to use reasonable and effective methods to realize the accuracy of land use information of high resolution remote sensing images in mountainous areas, Rapid extraction has become an urgent problem in land use survey. In this paper, a typical rural mountainous area located in the western part of Panzhihua City, Southwest Sichuan is selected as the research area. The IKONOS high-resolution remote sensing image with a panchromatic band resolution of 1m and a multi-spectral band resolution of 4m is used as the data source, and based on the software platforms of ERDAS 9.2 ENVI4.8e Cognition 8.0 and ArcGIS 9.3, etc. The method of object oriented classification, with the participation of texture and terrain factors, is used to realize the accurate and rapid extraction of land use information in the study area. The main conclusions are as follows: (1) the optimal band combination of IKONOS high-resolution remote sensing images in the study area is studied by the optimal exponential method of Optimum Index Factor (OIF-1). The results show that the optimal band combination mode of remote sensing image in the study area is the band combination of 2 ~ 3 ~ 3 ~ 4G ~ (?) r ~ (?), and its OIF value is the highest, reaching 102.88. The contrast between the image and the ground color is obvious. In this paper, we set up three levels of segmentation: level 1 and I level 2 level 3) to segment the remote sensing image of the study area by multi-scale. The optimal segmentation scale of each level is discussed by means of image feature analysis and mean variance method. The results show that the optimal scale of level 1 layer is 20, and the optimal scale of dividing level 2 layer of residential area and bare land in mountainous area is 45, and the optimal scale is 45 for pothole, cropland and no cropland in mountain area. The optimal scale of dividing three layers of forest land and garden land is 80, which can be better for mountain reservoirs, rivers, woodlands, and grasslands. According to Shannon's information entropy (Shannon entropyy) principle, four texture indexes of Homo (Ent) Ent (second order distance) were selected to help the extraction of land use information in mountain areas. The effect of texture aided segmentation is compared and analyzed. The results show that the texture involved in the segmentation process can improve the effect of image segmentation. After the texture is involved in the segmentation of the image, the degree of polygon fragmentation in the same kind of ground objects is obviously reduced after segmentation. In the process of segmentation, the boundary information of large-area objects is fully considered, and the integrity of objects is reflected. In addition, the number of polygons is obviously reduced by 44.27, and the efficiency of image interpretation is greatly improved.) the accuracy evaluation indexes of object oriented classification based on terrain and texture assistance are greatly improved than those of traditional supervised classification. The advantages of the classification method in the extraction of land use information in mountainous areas are fully demonstrated. Among them, the overall accuracy of classification reached 90.57, the value of Kappa coefficient increased 17.92% than that of traditional supervised classification, and the value of Kappa coefficient was 0.889 2, 0.1879 higher than that of traditional classification. In the result of object-oriented classification, the area of each area is closer to that of field investigation, which also reflects the accuracy of the classification.
【學位授予單位】：四川農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F301.2;S127
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