本文關(guān)鍵詞：基于低空遙感影像的建設用地變化檢測研究　出處：《南京大學》2013年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 低空遙感 建設用地 變化檢測 紋理識別 動態(tài)監(jiān)測

【摘要】：本文根據(jù)國土資源部公益性行業(yè)科研專項課題“土地動態(tài)監(jiān)測監(jiān)察技術(shù)研究”(201011015-1)的需求,針對江蘇省金壇市典型地區(qū),研究基于低空遙感影像的建設用地變化檢測方法,快速發(fā)現(xiàn)建設用地變化信息,特別是新增建設用地信息,為提高國土資源動態(tài)監(jiān)測效率提供技術(shù)支撐。 建設用地是工業(yè)化、城市化的核心要素之一,及時掌握建設用地變化情況對提高土地資源利用率、協(xié)調(diào)耕地資源保護與經(jīng)濟發(fā)展之間的矛盾等具有重要意義。無人機、無人飛艇等低空遙感平臺具有機動、實時、低成本的特點,與航空、航天遙感平臺相比,它能有效避免云霧、成像周期等因素的影響。在區(qū)域尺度上利用低空遙感影像快速檢測建設用地變化信息,是一種新型的國土資源動態(tài)監(jiān)測技術(shù)手段。 通過本文研究,提出了一種集成像元變化分析和對象識別的建設用地變化檢測方法。首先根據(jù)低空遙感影像的成像特點,綜合利用低空遙感影像中的光譜與紋理信息,在像元級設計自適應綜合差值方法檢測土地利用變化信息；然后通過數(shù)學形態(tài)學等方式優(yōu)化變化檢測結(jié)果,在提高變化信息精度的同時,提升變化信息的完整度,實現(xiàn)從像元級檢測到對象級識別的過渡；最后,以對象紋理分析為手段,在對象級進一步驗證變化信息,并采用對象識別的方式從所有的變化對象中篩選出建設用地變化對象。本文的主要研究內(nèi)容與結(jié)論如下： (1)結(jié)合光譜與紋理信息的土地利用變化檢測。低空遙感影像空間分辨率高,地物的結(jié)構(gòu)和紋理信息豐富；而波段較少,光譜分辨率相對不足。基于以上特點,在變化檢測過程中充分挖掘影像的紋理信息。在光譜特征的基礎(chǔ)上,選擇適合的紋理特征,經(jīng)逐波段差值計算后,設計自適應的閾值分割方法分別獲得各光譜波段和紋理特征所表征的變化信息。以邏輯合并的方式將不同來源的變化信息綜合,生成土地利用變化像元。結(jié)果表明紋理信息能夠反映變化區(qū)域內(nèi)部的細節(jié)信息,在提升檢測精度的同時,能有效降低檢測結(jié)果的破碎度。所提出的自適應綜合差值方法相比于其他常用的基于直接比較的變化檢測方法,其檢測結(jié)果具有更高的正確率,且有效降低了漏檢率。 (2)土地利用變化檢測結(jié)果優(yōu)化。由于低空遙感影像空間分辨率高,地物內(nèi)部差異較大,加上配準誤差、細部結(jié)構(gòu)和噪聲的影響,像元級變化檢測結(jié)果較破碎,一方面生成了許多無意義的偽變化區(qū)域,另一方面變化信息表達不完整,變化區(qū)域與地物對象之間不具有明顯的對應關(guān)系。依次采用形態(tài)學閉運算、孔洞填充、形態(tài)學開運算、小區(qū)域去除等四個步驟優(yōu)化變化檢測結(jié)果,以去除偽變化像元,提高變化檢測的精度,并生成形狀完整、邊界簡潔的變化區(qū)域,便于直接獲得變化對象,實現(xiàn)從像元級檢測到對象級識別的過渡。 (3)建設用地變化對象識別。由于受局部細微結(jié)構(gòu)差異的影響,在像元級變化檢測結(jié)果中仍存在部分偽變化信息,需要在對象級對變化檢測結(jié)果做進一步篩選。通過區(qū)域標記將連通的變化像元追蹤為單個變化對象,利用變化對象在前后時相的紋理差異濾除偽變化對象。然而由于土地利用變化檢測結(jié)果中包含所有類型的變化信息,變化對象中不僅有建設用地變化對象,還存在非建設用地變化對象。采用對象識別的方式進一步篩選出建設用地變化對象。分別針對均質(zhì)和非均質(zhì)的建設用地對象建立相應的紋理識別標志,在單個時相中識別變化對象是否為建設用地,并輸出最終的建設用地變化對象。 綜上所述,利用低空遙感影像進行建設用地變化檢測時,紋理與光譜信息的綜合應用以及變化檢測結(jié)果的優(yōu)化,能夠有效降低漏檢率；變化對象的篩選與建設用地對象的識別能夠降低誤檢率,提高建設用地變化檢測的精度。研究結(jié)果表明,該方法能夠快速獲得建設用地變化信息的幾何范圍與物理屬性,有助于提高建設用地動態(tài)監(jiān)測的效率。
[Abstract]:According to the Ministry of land and resources public welfare industry research special subject of "land dynamic monitoring monitoring technology research" (201011015-1) requirements, according to the typical area of Jintan city in Jiangsu Province, the construction land use change detection method of low altitude remote sensing images based on fast find the changes of construction land, especially the new construction land information, in order to improve dynamic monitoring of land resources efficiency and provide technical support.
Construction land is one of the core elements of industrialization, the city, to grasp the situation to improve the utilization of land resources construction land use change, has the contradiction between cultivated land protection and economic development coordination is the important significance. The UAV, unmanned airship remote sensing platform with mobile, real-time, low cost airlines and characteristics. Compared, space remote sensing platform, it can effectively avoid the influence of cloud imaging cycle and other factors. In the regional scale using the rapid detection of low altitude remote sensing information construction land change, is a new means of state land resources dynamic monitoring technology.
This paper put forward a detection method of construction land use change an integrated pixel change analysis and object recognition. According to the imaging characteristics of low altitude remote sensing images, the comprehensive utilization of spectrum and texture information of low altitude remote sensing image, value detection method of land use change information in the design of adaptive pixel level comprehensive optimization; and then change detection results the mathematical morphology method, improve the accuracy of information on changes at the same time, enhance the change information integrity, to achieve the transition from pixel level detection to the object level recognition; finally, the object texture analysis as a means to further verify the information in the object level, and changes from all objects of the selected object construction land use change the object recognition way. The main research contents and conclusions are as follows:
(1) combined with the detection of land use change on spectral and texture information. The low altitude remote sensing images of high spatial resolution, feature rich texture and structure information; while the band is less, the relative lack of spectral resolution. Based on the above characteristics, changes in the detection process to fully tap the image texture information. Based on the spectral feature, texture feature, the spectral difference calculation, an adaptive threshold segmentation method is used to obtain the change information of each spectral band and texture feature representation. Combined with logical methods will change the letter of different sources of comprehensive information, land use change to generate pixel. The results show that the texture information can reflect the change of details within the region. While improving the detection accuracy, can effectively reduce the detection results of fragmentation. The integrated adaptive difference method proposed compared to other commonly used based on the straight Compared with the change detection method, the detection result has a higher correct rate, and the leakage rate is effectively reduced.
(2) land use change detection result optimization. Because of low altitude remote sensing images of high spatial resolution, internal differences of larger features, plus the registration error, influence of detail and noise, the pixel level change detection result is broken, on the one hand, generating a lot of meaningless pseudo change regions, on the other hand, changes in expression of the incomplete information, with no obvious corresponding relationship between the object and the ground. The change area followed by morphological operations, hole filling, morphological open operation, small area to remove the four steps optimization of change detection results, to remove the pseudo pixel changes, improve the accuracy of change detection, and generate a complete shape, simple changes in regional boundaries, to facilitate direct access changes in the object, to achieve the transition from pixel level detection to the object level recognition.
(3) the object recognition of construction land use change. Due to the local subtle structural differences, there are still some false change information at the pixel level change detection results, the need for further screening at the object level on the change detection results. Through region labeling will change pixel connectivity for a single object tracking changes, changes in the object before and after use when the texture difference phase filtering false change object. However due to include all types of information change detection results of land use change, change the object not only the object of construction land use change, the existence of non construction land use change object. Object recognition further screened object construction land use change for homogeneous and respectively. The heterogeneity of construction land to establish the corresponding object texture recognition marks recognition change whether the object is construction land in a single phase, and the output end of the construction land Changing objects.
To sum up, to detect the changes of construction land use of low altitude remote sensing image, optimize the comprehensive application of texture and spectral information and change detection results, can effectively reduce the missing rate; screening and construction object identified by the object changes can reduce the error rate and improve the construction land change detection accuracy. The results show that and this method can quickly obtain geometric and physical attributes of the construction land use change, help to improve the efficiency of dynamic monitoring of construction land.

【學位授予單位】：南京大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TU984.113;P237

【參考文獻】

相關(guān)期刊論文 前10條

1 耿忠;;面向單波段高分辨率遙感影像的人工目標變化檢測技術(shù)研究[J];地理信息世界;2007年06期

2 劉直芳,張繼平;變化檢測方法及其在城市中的應用[J];測繪通報;2002年09期

3 張友水;馮學智;周成虎;;多時相TM影像相對輻射校正研究[J];測繪學報;2006年02期

4 李磊,李小娟,崔偉宏;基于GIS和RS的縣級土地利用動態(tài)監(jiān)測系統(tǒng)研究[J];地理學與國土研究;2001年02期

5 龍玄耀;李培軍;;基于圖像分割的城市變化檢測[J];地球信息科學;2008年01期

6 馬建偉;趙忠明;;基于影像區(qū)域特征人工目標變化檢測方法[J];計算機工程;2007年17期

7 邸鳳萍;朱重光;丁玲;;方向矢量法在城市土地利用變化檢測中的應用[J];計算機工程;2008年02期

8 蘇娟;王貴錦;林行剛;劉代志;;基于多時相遙感圖像的人造目標變化檢測算法[J];自動化學報;2008年09期

9 葉明,楊曉平,蔣剛毅;基于TM遙感圖像的寧波土地利用動態(tài)監(jiān)測[J];寧波大學學報(理工版);2003年01期

10 張發(fā)旺;程彥培;韓旭;馮仲科;董華;;基于NOAA數(shù)據(jù)的北亞凍土變化研究[J];南水北調(diào)與水利科技;2010年06期

相關(guān)會議論文 前1條

1 郝中豫;高光星;王祥;余詠勝;;低空遙感在違法建筑快速監(jiān)測中的應用[A];中國測繪學會九屆四次理事會暨2008年學術(shù)年會論文集[C];2008年

相關(guān)博士學位論文 前3條

1 祝錦霞;高分辨率遙感影像變化檢測的關(guān)鍵技術(shù)研究[D];浙江大學;2011年

2 張路;基于多元統(tǒng)計分析的遙感影像變化檢測方法研究[D];武漢大學;2004年

3 馬建偉;全色波段高空間分辨遙感影像特定目標變化檢測技術(shù)研究[D];中國科學院研究生院（遙感應用研究所）;2006年

相關(guān)碩士學位論文 前2條

1 韓文超;基于POS系統(tǒng)的無人機遙感圖像拼接技術(shù)研究與實現(xiàn)[D];南京大學;2011年

2 劉小洲;高分辨率遙感圖像的變化檢測技術(shù)研究[D];國防科學技術(shù)大學;2007年

，

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