本文選題：GF-2 + 影像��； 參考：《成都理工大學(xué)》2017年碩士論文

【摘要】：耕地資源是人類生存與發(fā)展不可或缺的物質(zhì)基礎(chǔ),是區(qū)域內(nèi)糧食自給能力的根本所在,是國家糧食安全的可靠保障,我國必須守住18億畝耕地紅線。在城市不斷發(fā)展過程中,土地資源的消耗越來越大,耕地資源在這一過程中被逐步侵占的問題十分嚴(yán)重。耕地面積銳減是這一時(shí)期土地利用/覆被(LUCC)變化的最顯著特征,在一定時(shí)空程度上而言,建設(shè)用地與耕地資源之間的關(guān)系是此消彼長。因此,如何快速、準(zhǔn)確地獲取耕地信息對耕地動(dòng)態(tài)監(jiān)測、耕地保護(hù)以及土地資源利用程度分析,協(xié)調(diào)好人地關(guān)系具有重要意義,也可以為黨和國家制定相關(guān)政策提供依據(jù)。而遙感影像中普遍存在的同物異譜、異物同譜現(xiàn)象使得地物分離精度大大降低。并且由于耕地與相鄰建設(shè)用地、林地等地物交織混雜,形成不易區(qū)分的混合體,且分布破碎無規(guī)律,加之土壤特性、作物以及耕作方式的不同,地類間光譜特征的差異,這使得基于中低分辨率衛(wèi)星影像的耕地信息提取效果不如人意。隨著遙感衛(wèi)星技術(shù)的迅猛發(fā)展,尤其是高空間分辨率衛(wèi)星的發(fā)展,使得高精度地物分類成為可能。目前,有關(guān)利用高分辨率遙感影像提取耕地信息已漸成研究熱點(diǎn)。這為進(jìn)行大區(qū)域、高精度的遙感信息尤其是耕地信息的快速有效提取提供了新思路。本文以地處川東丘陵地區(qū)的隆昌市龍市鎮(zhèn)為研究區(qū)進(jìn)行耕地信息提取,龍市鎮(zhèn)是隆昌市基本農(nóng)田保護(hù)示范鄉(xiāng)鎮(zhèn),鎮(zhèn)內(nèi)耕地面積大,具有代表性。研究采用基于像元的最小距離法、最大似然法、K均值法、ISODATA法與面向?qū)ο蟮碾`屬度函數(shù)分類法、CART決策樹法、支持向量機(jī)法對研究區(qū)GF-2衛(wèi)星影像數(shù)據(jù)進(jìn)行耕地信息提取。主要取得以下成果:(1)研究區(qū)GF-2影像數(shù)據(jù),分別采用Gram-Schmidt、PCA變換與NNDiffuse四種方法進(jìn)行影像融合,通過對比亮度、對比度、紋理等特征得出該區(qū)域影像的最佳融合方法為NNDiffuse融合。(2)基于影像的光譜特征對研究區(qū)融合影像進(jìn)行非監(jiān)督分類實(shí)驗(yàn)和監(jiān)督分類實(shí)驗(yàn)。在易康(eCognition)軟件平臺上進(jìn)行多尺度分割,確定適合研究區(qū)影像的分割尺度與相關(guān)參數(shù),建立分類體系,通過特征優(yōu)化選擇優(yōu)化特征集,結(jié)合研究區(qū)數(shù)字高程模型(DEM)進(jìn)行耕地信息提取,結(jié)果表明基于優(yōu)化特征的分類效果優(yōu)于單純基于光譜特征的分類效果。(3)結(jié)合野外實(shí)地驗(yàn)證,評價(jià)不同方法提取耕地信息的精度,結(jié)果表明,面向?qū)ο蟮姆诸惙椒芨焖�、�?zhǔn)確地提取丘陵地區(qū)耕地信息。(4)分析研究區(qū)耕地分布與區(qū)內(nèi)地形、坡度之間的關(guān)系,并對耕地保護(hù)與基本農(nóng)田保護(hù)區(qū)劃定給出相關(guān)建議。
[Abstract]:The cultivated land resource is the indispensable material foundation for the human survival and development, is the basic of the grain self-sufficiency ability in the region, is the reliable guarantee of the national food security, our country must keep the 1.8 billion mu cultivated land red line. In the process of continuous development of cities, the consumption of land resources is increasing, and the problem of cultivated land resources being gradually encroached on in this process is very serious. The sharp decrease of cultivated land area is the most significant feature of LUCCs change in this period. In a certain time and space, the relationship between construction land and cultivated land resources is the same. Therefore, how to obtain cultivated land information quickly and accurately is of great significance to the dynamic monitoring of cultivated land, the analysis of cultivated land protection and the degree of land resource utilization, and the coordination of human-land relations, which can also provide the basis for the Party and the state to formulate relevant policies. In remote sensing images, the common isospectral and foreign body isospectral phenomena reduce the precision of ground object separation greatly. And because cultivated land, adjacent construction land, woodland and other ground objects are intertwined and mixed, the mixture is not easy to distinguish, and the distribution is irregular, in addition, the characteristics of soil, the difference of crop and tillage methods, the difference of spectral characteristics among land types. This makes the effect of farmland information extraction based on low-resolution satellite image unsatisfactory. With the rapid development of remote sensing satellite technology, especially the development of high spatial resolution satellite, it is possible to classify ground objects with high precision. At present, the use of high-resolution remote sensing images to extract cultivated land information has become a research hotspot. This provides a new idea for fast and effective extraction of remote sensing information, especially cultivated land information. This paper takes Longchang City, located in the hilly region of East Sichuan, as the research area to extract cultivated land information. Longcheng Town is the demonstration town of basic farmland protection in Longchang City. The cultivated land area in the town is large and representative. In this paper, the method of minimum distance based on pixel, the method of maximum likelihood and the method of ISODATA and the method of object oriented membership function classification are used to extract cultivated land information from GF-2 satellite image data in the study area by support vector machine (SVM). The main achievements are as follows: 1) the GF-2 image data in the research area are fused by Gram-SchmidtPCA transform and NNDiffuse respectively, and the brightness and contrast are compared. Texture and other features show that the best fusion method for the region is NNDiffuse fusion. (2) based on the spectral features of the image, the unsupervised classification experiment and supervised classification experiment are carried out for the fusion image in the study area. Multi-scale segmentation is carried out on the software platform of Accom Cognition to determine the segmentation scale and relevant parameters suitable for the image in the study area, to establish a classification system, and to select the optimized feature set through feature optimization. The result shows that the classification effect based on the optimized feature is better than the classification effect based solely on the spectral feature. Combined with field verification, the accuracy of different methods for extracting cultivated land information is evaluated. The results show that the object-oriented classification method can extract the cultivated land information of hilly area more quickly and accurately. Some suggestions on farmland protection and basic farmland protection are also given.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S127

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