本文選題：地理國情監(jiān)測 + 影像分割��； 參考：《西南交通大學(xué)》2017年碩士論文

【摘要】：隨著經(jīng)濟(jì)高速、穩(wěn)步的發(fā)展,我國地表自然和人文信息的變化更趨頻繁。為進(jìn)一步滿足政府、社會和大眾對土地利用變化信息實時性掌握的需求,我國全面開展了地理國情監(jiān)測。傳統(tǒng)地理國情監(jiān)測方法工作量大、自動化程度不高、效率低下,從而限制了地理國情數(shù)據(jù)的更新周期的縮短。相反,遙感技術(shù)作為一種非接觸的探測技術(shù),具有探測范圍廣、獲取地表真實信息速度快、周期短等特點,可對地理國情進(jìn)行快速、大范圍的監(jiān)測,從而滿足地理國情對更新周期的需要。但是,由于傳統(tǒng)的遙感技術(shù)變化檢測需要兩幅或兩幅以上遙感影像,而常態(tài)化地理國情監(jiān)測中往往只能獲取前期矢量數(shù)據(jù)和后期遙感影像。因此,為解決這一矢量數(shù)據(jù)預(yù)更新問題,本文在現(xiàn)有遙感影像變化檢測技術(shù)上對矢量數(shù)據(jù)和遙感影像聯(lián)合變化檢測方法做進(jìn)一步研究和探討。本文的主要研究內(nèi)容如下:(1)對基于矢量數(shù)據(jù)與遙感影像的變化檢測技術(shù)的概念及流程進(jìn)行了闡述,并按照在檢測過程中是否需要選取樣本將其分為監(jiān)督法和非監(jiān)督法。對影像分割、特征提取、閾值自動獲取等矢量與遙感影像變化檢測中的三個關(guān)鍵技術(shù)進(jìn)行了歸納和總結(jié)。(2)研究了基于像斑類別異質(zhì)度的矢量數(shù)據(jù)與遙感影像的變化檢測方法。該方法借鑒了經(jīng)典的標(biāo)記分水嶺分割方法,以矢量數(shù)據(jù)作為先驗邊界,對影像進(jìn)行約束分割,獲取同質(zhì)像斑;提取像斑特征,構(gòu)建像斑類別異質(zhì)度,通過自動化閾值獲取技術(shù)獲取每一地物類別異質(zhì)度閾值,最后進(jìn)行變化/未變化像斑的判別。該方法實現(xiàn)了矢量數(shù)據(jù)與遙感影像的自動變化檢測。(3)研發(fā)了地理國情常態(tài)化監(jiān)測數(shù)據(jù)變化檢測系統(tǒng)。以基于像斑類別異質(zhì)度的矢量數(shù)據(jù)與遙感影像變化檢測方法為技術(shù)基礎(chǔ),采用GDAL(Geospatial Data Abstraction Library)與ArcEngine混合編程技術(shù)建設(shè)了地理國情常態(tài)化監(jiān)測數(shù)據(jù)變化檢測系統(tǒng)。系統(tǒng)采用C#語言,在Microsoft Visual Studio 2010平臺上,利用GDAL對柵格數(shù)據(jù)進(jìn)行處理,利用ArcEngine對矢量數(shù)據(jù)進(jìn)行處理。系統(tǒng)實現(xiàn)了柵格影像與矢量數(shù)據(jù)的疊加顯示、矢量數(shù)據(jù)約束下的影像分割、像斑特征提取、特征距離度量、閾值獲取、變化檢測、人工編輯(新增、刪除、修改)、參數(shù)設(shè)定(分割參數(shù))、數(shù)據(jù)輸出(分割結(jié)果、變化檢測結(jié)果)、后期編輯等功能。該系統(tǒng)能夠?qū)崿F(xiàn)自動化的變化檢測,與傳統(tǒng)的目視解譯相比,變化檢測效率得到較大的提高。(4)以新都區(qū)某村2015年地理國情普查矢量數(shù)據(jù)的地表覆蓋層與2016年航空高分遙感影像進(jìn)行變化檢測,變化檢測的正確率、虛檢率、漏檢率分別為84.3%、、15.6%、16.1%,變化檢測結(jié)果驗證了該方法的可行性與有效性,變化檢測精度已達(dá)到地理國情監(jiān)測中的精度要求。該變化檢測同時證明了研發(fā)的系統(tǒng)可有效的輔助地理國情數(shù)據(jù)中變化區(qū)域的發(fā)現(xiàn),提高內(nèi)業(yè)更新的效率。
[Abstract]:With the rapid and steady development of economy, the changes of natural and humanistic information on the surface of our country become more frequent. In order to further meet the needs of the government, society and the public to grasp the real-time information of land use change, our country has carried out the monitoring of geographical conditions in an all-round way. The traditional method of geographical situation monitoring has the advantages of heavy workload, low automation and low efficiency, which limits the shortening of the updating period of geographical national data. On the contrary, as a non-contact detection technology, remote sensing technology has the characteristics of wide range of detection, fast speed of obtaining real information on the surface, short period and so on. In order to meet the geographical conditions of the update cycle needs. However, two or more remote sensing images are needed for the change detection of traditional remote sensing technology. However, in the normal geographical situation monitoring, only the former vector data and the later remote sensing images can be obtained. Therefore, in order to solve the problem of vector data pre-updating, this paper further studies and discusses the joint change detection method of vector data and remote sensing image on the existing remote sensing image change detection technology. The main contents of this paper are as follows: (1) the concept and flow of change detection technology based on vector data and remote sensing image are expounded, and they are divided into supervised method and unsupervised method according to whether it is necessary to select samples in the process of detection. In this paper, three key techniques of image segmentation, feature extraction, threshold automatic acquisition and remote sensing image change detection are summarized and summarized. (2) the change detection method of vector data and remote sensing image based on the heterogeneity of image spot category is studied. This method uses the classical marking watershed segmentation method for reference, uses vector data as the prior boundary, performs constrained segmentation on the image, obtains the homogeneous image spot, extracts the image spot feature, and constructs the heterogeneity degree of the image spot category. The heterogeneity threshold of each feature category is obtained by the automatic threshold acquisition technique. Finally, the variable / unchanged image spots are identified. This method realizes the automatic change detection of vector data and remote sensing image. Based on the change detection method of vector data and remote sensing image based on the heterogeneity of image spot category, the change detection system of the normal monitoring data of geographical national conditions is built by using the mixed programming technology of GDAL spatial data Abstraction library and ArcEngine. The system uses C # language, on the platform of Microsoft Visual Studio 2010, uses GDAL to process raster data and ArcEngine to process vector data. The system realizes the superposition display of raster image and vector data, image segmentation under vector data constraint, image spot feature extraction, feature distance measurement, threshold value acquisition, change detection, manual editing (add, delete, delete). Modification, parameter setting (partition parameters, data output (segmentation results, change detection results, late editing and so on). Compared with traditional visual interpretation, the system can realize automatic change detection. The efficiency of change detection has been greatly improved.) change detection is carried out on the ground overlay of a village's 2015 geographic situation census vector data and 2016 aerial high score remote sensing image. The accuracy and false detection rate of change detection are obtained. The rate of missing detection is 84.3and 15.6and 16.1respectively. The result of change detection verifies the feasibility and validity of the method, and the accuracy of the change detection has reached the precision requirement of geographical situation monitoring. The change detection also proves that the developed system can effectively assist the discovery of the changing regions in the geographical national conditions data and improve the efficiency of the renewal of the internal industry.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P237

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本文編號：2006259