發(fā)布時間：2018-07-07 21:01

  本文選題：云檢測 + 陰影檢測��； 參考：《廣西師范大學(xué)》2017年碩士論文

【摘要】：在信息時代的今天,遙感圖像的采集和解譯因計算機科學(xué),電子信息,圖像處理技術(shù)的進(jìn)步而不斷發(fā)展。大量的遙感資源圖像具備高分辨率與廣泛的區(qū)域覆蓋特性,這些數(shù)據(jù)信息可使用在許多領(lǐng)域,如農(nóng)業(yè)生產(chǎn),環(huán)境監(jiān)測,生態(tài)保護(hù),基礎(chǔ)建設(shè)投資,地形建模等。然而,受大氣中不同形態(tài)的云和云陰影干擾,遙感衛(wèi)星經(jīng)常得到模糊的圖像資源,嚴(yán)重影響了信息的時效性和完整性。另外在不同的背景下,亮白的云和較暗的陰影區(qū)域檢測較困難,如果僅僅只使用人工方式檢測云和云陰影,則極大影響信息分析的效率。為克服發(fā)展與效率之間的矛盾,自動化去云和云陰影成為研究遙感圖像的一個很重要的科學(xué)領(lǐng)域。基于多幅不同時相遙感圖像之間的信息補償方式,提高了解譯圖像的清晰度和整體利用率。使用C++程序設(shè)計語言調(diào)用Opencv庫函數(shù),設(shè)計一套操作簡單的圖像處理軟件,并成功處理廣東和道縣地區(qū)的遙感圖像。論文的主要研究方法概括如下:文章采用傳統(tǒng)的閾值、改進(jìn)的分水嶺、小波融合算法檢測厚云區(qū)域。因厚云和薄云所具備物理屬性不同,分別以不同的方式檢測和處理。改進(jìn)的分水嶺算法先通過最大類間方差法標(biāo)記亮白的區(qū)域,然后獲得完備的厚云邊界區(qū)域,避免了圖像過度的分割。小波融合的方式充分利用藍(lán)色通道圖像厚云區(qū)域的完整性與近紅外通道圖像厚云與背景區(qū)域的差異性。多時相圖像經(jīng)3層小波分解,選取合適的小波分解系數(shù),重構(gòu)得到的圖像厚云與背景區(qū)域?qū)Ρ榷让黠@增強,利用傳統(tǒng)閾值的方式對融合后的圖像進(jìn)行分割,能夠得到灰度值不是很高的厚云區(qū)域。最后以上三種算法均通過區(qū)域膨脹的方式獲得厚云邊界上的薄云區(qū)域。在實際的應(yīng)用中,為了排除灰度值相似于云的亮白背景對象和檢測出灰度值相似背景的薄云區(qū)域,提出的掩膜算法,能夠得到準(zhǔn)確的云區(qū)域。遙感圖像中的云陰影嚴(yán)重妨礙了信息提取和變化檢測的精確度,城市建筑、山體、和云都能產(chǎn)生陰影,或者在光譜特性上與陰影相似的水體。本文為了排除水體并檢測出云陰影,根據(jù)光譜間差異性檢測出水體。在近紅外波段下云的陰影比較暗,使用泛洪填充算法來獲得可能的陰影區(qū)域。隨后,通過幾何形態(tài)學(xué)的方式構(gòu)造云與云陰影之間的三維幾何關(guān)系,確定云的陰影。為了減少云與陰影匹配的迭代次數(shù),本文通過云的亮溫值和光譜反射率確定云的高度,提高了陰影檢測算法的有效性和準(zhǔn)確性。為去除檢測得到的云和云陰影區(qū)域,根據(jù)不同時相圖像之間的差異,建立回歸關(guān)系。使用矯正后的不同時相圖像像素區(qū)域替換到被云和云陰影所污染的區(qū)域。從主觀視覺的角度評估結(jié)果所示:上述云檢測算法均能十分精確檢測出云區(qū)域,其中掩膜算法效果最佳,最后得到無邊界差異的去云效果圖,并適合Landsat系列不同時相衛(wèi)星圖像資源。
[Abstract]:In the information age, the acquisition and interpretation of remote sensing images have been continuously developed due to the progress of computer science, electronic information and image processing technology. A large number of remote sensing images have high resolution and extensive regional coverage. These data information can be used in many fields, such as agricultural production, environmental monitoring, ecological protection, infrastructure investment, terrain modeling, and so on. However remote sensing satellites often get blurred image resources which seriously affect the timeliness and integrity of information due to the interference of cloud and cloud shadows in the atmosphere. In addition, it is more difficult to detect bright white clouds and dark shadow regions in different backgrounds. If only the manual detection of cloud and cloud shadow is used, the efficiency of information analysis will be greatly affected. In order to overcome the contradiction between development and efficiency, automatic cloud removal and cloud shadow has become an important scientific field in remote sensing image research. Based on the information compensation method of multi-time remote sensing images, the sharpness and overall utilization rate of the interpreted images are improved. Using C programming language to call Opencv library function, a set of simple image processing software is designed, and the remote sensing images in Guangdong and Daoxian areas are successfully processed. The main research methods are summarized as follows: traditional threshold, improved watershed and wavelet fusion algorithm are used to detect thick cloud region. Because thick cloud and thin cloud have different physical properties, they are detected and processed in different ways. The improved watershed algorithm uses the maximum inter-class variance method to mark the bright white region, and then obtains the complete thick cloud boundary area, which avoids the excessive segmentation of the image. The method of wavelet fusion makes full use of the difference between the integrity of thick cloud region of blue channel image and the difference between thick cloud and background region of near infrared channel image. The multi-temporal image is decomposed by three layers of wavelet, and the suitable wavelet decomposition coefficient is selected. The contrast between the thick cloud and the background area is obviously enhanced, and the fused image is segmented by the traditional threshold method. A thick cloud region with a low gray value can be obtained. Finally, the three algorithms are used to obtain the thin cloud region on the thick cloud boundary by the expansion of the region. In practical applications, in order to eliminate the bright white background objects with similar gray values and detect thin cloud regions with similar gray values, the proposed mask algorithm can obtain accurate cloud regions. Cloud shadows in remote sensing images seriously hinder the accuracy of information extraction and change detection. Urban buildings mountains and clouds can produce shadows or water bodies with spectral characteristics similar to shadows. In order to exclude water body and detect cloud shadow, the water body is detected according to spectral difference. In the near infrared band, the cloud shadow is dark, the flooding fill algorithm is used to obtain the possible shadow region. Then, the geometric relationship between cloud and cloud shadow is constructed by geometric morphology, and the cloud shadow is determined. In order to reduce the number of iterations of cloud and shadow matching, the cloud height is determined by cloud brightness temperature and spectral reflectivity, which improves the effectiveness and accuracy of shadow detection algorithm. In order to remove the detected cloud and cloud shadow regions, a regression relationship was established according to the differences between different phase images. The corrected pixel area of the different phase image is replaced by the area contaminated by cloud and cloud shadow. The evaluation results from subjective vision show that the above cloud detection algorithms can detect cloud region accurately, and the mask algorithm has the best effect. Finally, the de-cloud effect map without boundary difference is obtained. And suitable for Landsat series of different phase satellite image resources.
【學(xué)位授予單位】：廣西師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP751

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