本文選題：近海養(yǎng)殖區(qū)提取 + 基于正交子空間投影的約束能量最小化(OWCEM)�。� 參考：《中國(guó)科學(xué)院大學(xué)(中國(guó)科學(xué)院遙感與數(shù)字地球研究所)》2017年碩士論文

【摘要】：隨著近海水產(chǎn)養(yǎng)殖業(yè)的發(fā)展,近海岸環(huán)境正面臨著水體富營(yíng)養(yǎng)化、原始生態(tài)群落被破壞等威脅。日益增長(zhǎng)的水產(chǎn)養(yǎng)殖活動(dòng)在追求經(jīng)濟(jì)利益中如何考慮到當(dāng)?shù)丨h(huán)境的承載力是養(yǎng)殖戶(hù)和當(dāng)?shù)毓芾聿块T(mén)需要解決的問(wèn)題。而利用遙感衛(wèi)星影像對(duì)近海養(yǎng)殖區(qū)進(jìn)行觀測(cè),是規(guī)劃和管理近海水產(chǎn)養(yǎng)殖區(qū)的有效方式之一。不過(guò)傳統(tǒng)的水產(chǎn)檢測(cè)算法通常利用專(zhuān)業(yè)的解譯人員配合遙感軟件對(duì)影像進(jìn)行人工解譯,提取效率低,且人工成本較高,難以滿(mǎn)足近海水產(chǎn)養(yǎng)殖區(qū)快速觀測(cè)的要求。本文在研究近海水產(chǎn)養(yǎng)殖區(qū)在遙感影像上的光譜和空間特征,討論了利用短重返周期、低數(shù)據(jù)成本的中等分辨率衛(wèi)星提取近海水產(chǎn)養(yǎng)殖區(qū)的可行性,此外還對(duì)現(xiàn)有的水產(chǎn)提取算法進(jìn)行了總結(jié),并結(jié)合算法原理分析其優(yōu)缺點(diǎn)和適用范圍,對(duì)已有算法中存在的問(wèn)題進(jìn)行總結(jié),并提出了基于改進(jìn)約束能量最小化的水產(chǎn)自適應(yīng)分割提取算法。算法利用Landsat 8 OLI中等分辨率衛(wèi)星數(shù)據(jù)來(lái)進(jìn)行水產(chǎn)提取方法的研究,不僅在最終的提取精度上高于傳統(tǒng)的檢測(cè)算法,而且整個(gè)提取流程的自動(dòng)化程度高,減輕了傳統(tǒng)影像解譯人員的工作負(fù)擔(dān)。相比于以往的水產(chǎn)檢測(cè)方法僅能夠識(shí)別出水產(chǎn)養(yǎng)殖區(qū)的大致范圍,本文充分利用水產(chǎn)養(yǎng)殖區(qū)的光譜和空間特征,將養(yǎng)殖水域養(yǎng)殖帶之間的非養(yǎng)殖像元進(jìn)行剔除,大大降低了算法錯(cuò)分概率,提高了整體算法的提取精度,能有向有關(guān)部門(mén)和專(zhuān)家提供更為準(zhǔn)確、有效的近海養(yǎng)殖信息。主要的研究工作如下:1、本章對(duì)現(xiàn)階段國(guó)內(nèi)外水產(chǎn)提取的方法進(jìn)行了歸納總結(jié),分別是目視解譯法,特征指數(shù)提取法,面向?qū)ο蠓指钐崛》?無(wú)人機(jī)影像解譯法和SAR影像解譯法。通過(guò)分析不同提取方法間的算法原理,說(shuō)明了各方法在水產(chǎn)養(yǎng)殖提取中的優(yōu)缺點(diǎn)和主要解決的問(wèn)題。除了介紹算法各自的原理步驟之外,還對(duì)常用幾種算法的適用性和局限性進(jìn)行了總結(jié)評(píng)述。2、針對(duì)以往水產(chǎn)提取算法誤檢率高、普適性較低等問(wèn)題,本文提出了一種基于改進(jìn)約束能量最小化的水產(chǎn)自適應(yīng)分割提取算法(OWCEM-SDAS)。本文算法利通過(guò)多個(gè)波段組合的方式,構(gòu)建了包含常用的水體指數(shù)MNDWI、MAWEI在內(nèi)的多種指數(shù),并結(jié)合葉綠素濃度和懸浮泥沙濃度指數(shù)使用正交加權(quán)約束能量最小化算法(OWCEM)對(duì)養(yǎng)殖區(qū)進(jìn)行增強(qiáng),然后通過(guò)原始影像分塊,在每個(gè)子區(qū)域(塊)中使用自適應(yīng)閾值對(duì)水產(chǎn)養(yǎng)殖區(qū)進(jìn)行分割提取,最后將各子區(qū)域(塊)的提取結(jié)果綜合,從而得到近海養(yǎng)殖區(qū)最終的水產(chǎn)提取。3、通過(guò)不同近海養(yǎng)殖區(qū)不同提取方法間的對(duì)比,利用混淆矩陣和kappa系數(shù)等指標(biāo)量化提取結(jié)果,證明了本文算法在不同實(shí)驗(yàn)區(qū)復(fù)雜背景下的提取精度和穩(wěn)定性都好于其他常用提取算法。
[Abstract]:With the development of offshore aquaculture, the coastal environment is facing the threat of eutrophication and destruction of primitive ecological communities. How to consider the bearing capacity of local environment in the pursuit of economic benefits in the growing aquaculture activities is a problem that farmers and local management departments need to solve. It is one of the effective ways to plan and manage the offshore aquaculture area by using remote sensing satellite image. However, the traditional fish detection algorithms usually use professional interpreters with remote sensing software for artificial interpretation of images, the extraction efficiency is low, and the labor cost is high, it is difficult to meet the requirements of rapid observation in offshore aquaculture areas. In this paper, the spectral and spatial characteristics of remote sensing images of offshore aquaculture area are studied, and the feasibility of extracting offshore aquaculture area by using medium resolution satellite with short re-entry period and low data cost is discussed. In addition, the existing aquatic extraction algorithms are summarized, and the advantages, disadvantages and applicable scope of the algorithms are analyzed, and the existing problems in the existing algorithms are summarized. An adaptive extraction algorithm based on improved constrained energy minimization is proposed. The algorithm uses Landsat 8 Oli medium resolution satellite data to carry on the aquatic product extraction method research, not only in the final extraction precision is higher than the traditional detection algorithm, but also the whole extraction process automation degree is high. It lightens the workload of the traditional image interpreter. Compared with the previous methods of aquaculture detection, we can only identify the approximate range of aquaculture areas. In this paper, we take full advantage of the spectral and spatial characteristics of aquaculture zones, and eliminate the non-culture pixels between the aquaculture zones. The algorithm greatly reduces the probability of misdivision, improves the accuracy of the whole algorithm, and can provide more accurate and effective offshore aquaculture information to the relevant departments and experts. The main research work is as follows: 1. In this chapter, the methods of aquatic extraction at home and abroad are summarized, including visual interpretation, feature index extraction, object oriented segmentation, UAV image interpretation and SAR image interpretation. By analyzing the principle of different extraction methods, the advantages and disadvantages of each method in aquiculture extraction and the main problems solved were explained. In addition to introducing the principle and steps of the algorithms, the applicability and limitation of several commonly used algorithms are summarized and commented. In view of the problems of high misdetection rate and low universality of the previous aquatic extraction algorithms, In this paper, an improved constrained energy minimization algorithm (OWCEM-SDAS) is proposed. In this paper, we construct a variety of indexes including the commonly used water index MNDWI and MAWEI by means of multi-band combination. Combined with chlorophyll concentration and suspended sediment concentration index, orthogonal weighted constrained energy minimization algorithm (OWCEM) is used to enhance the culture area, and then the original image is divided into blocks. In each sub-region (block), the adaptive threshold is used to segment and extract the aquaculture area. Finally, the extraction results of each sub-region (block) are synthesized. In this way, the final aquatic extraction. 3 in the offshore culture area was obtained. By comparing the different extraction methods in different offshore culture areas, the results were quantified by using the confusion matrix and kappa coefficient, and so on. It is proved that the extraction accuracy and stability of this algorithm are better than those of other common algorithms in different experimental areas.
【學(xué)位授予單位】：中國(guó)科學(xué)院大學(xué)(中國(guó)科學(xué)院遙感與數(shù)字地球研究所)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：S951.4;TP751

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