本文選題：工業(yè)固體廢物 + 遙感影像分類　； 參考：《北方民族大學(xué)》2014年碩士論文

【摘要】：工業(yè)固體廢物污染問(wèn)題在國(guó)際已經(jīng)被認(rèn)為是十大環(huán)境問(wèn)題中的一個(gè)。隨著我國(guó)工業(yè)化進(jìn)程的加快，工業(yè)固體廢物的排放量和堆存量也在大幅度增加。盡管環(huán)保技術(shù)的發(fā)展已使部分工業(yè)固體廢物能夠被回收，甚至被利用，但人們處理固體廢物的方法更多的是采用集中堆放。工業(yè)固體廢物的堆放會(huì)妨礙景觀，而且工業(yè)固體廢物中含有的有害物質(zhì)會(huì)通過(guò)降雨和滲透作用進(jìn)入土壤，造成地下水的污染，甚至將土壤中的微生物殺死，破壞生態(tài)平衡，形成工業(yè)固體廢物的二次污染。因此采用科學(xué)有效的方法對(duì)其進(jìn)行監(jiān)測(cè)和管理，降低工業(yè)固體廢物對(duì)環(huán)境的污染是非常必要的。在此，遙感監(jiān)測(cè)起到了重要的作用。 工業(yè)固體遙感影像分類作為工業(yè)固體遙感監(jiān)測(cè)的有效途徑，其分類精度的高低直接影響到監(jiān)測(cè)結(jié)果的準(zhǔn)確度。然而傳統(tǒng)的遙感影像分類大多是基于像素級(jí)別的分類方法，沒(méi)有綜合考慮影像的多個(gè)特征信息，這導(dǎo)致分類精度不是很高。針對(duì)此，本文提出了一種面向?qū)ο蟮墓I(yè)固體廢物遙感影像分類方法，該方法綜合考慮了影像的光譜、形狀和紋理信息，，采用圖論與支持向量機(jī)（SVM）相結(jié)合的方法對(duì)遙感影像進(jìn)行了分類處理。 本文的面向?qū)ο蟮墓I(yè)固體廢物遙感影像分類方法包括以下幾個(gè)步驟： （1)在遙感影像預(yù)處理的基礎(chǔ)上對(duì)其進(jìn)行四叉樹(shù)預(yù)分割； （2)分別計(jì)算每個(gè)分割塊之間的光譜相似度、像素之間的匹配度、紋理相似度，從而獲得相應(yīng)的權(quán)值分量； （3)運(yùn)用圖論中的R-cut割集準(zhǔn)則對(duì)影像做進(jìn)一步的多特征分割； （4)使用SVM對(duì)上述分割結(jié)果做分類處理，得到最終的分類結(jié)果。 本文方法綜合遙感影像的多個(gè)特征，并且把圖論與SVM相結(jié)合對(duì)工業(yè)固體廢物遙感影像進(jìn)行分類。實(shí)驗(yàn)證明：與傳統(tǒng)的分類方法：馬氏距離法、光譜角制圖、SVM等分類方法進(jìn)行比較，本文方法所得的總體精度和kappa系數(shù)都要比它們高，因此，本文方法可以有效地用于工業(yè)固體廢物遙感影像分類處理。
[Abstract]:Industrial solid waste pollution has been regarded as one of the top ten environmental problems in the world. With the acceleration of industrialization in our country, the discharge of industrial solid waste and the storage of industrial solid waste are also increasing by a large margin. Although the development of environmental protection technology has enabled some industrial solid waste to be recycled or even used, the method of disposal of solid waste is more often centralized stacking. The stowage of industrial solid waste can interfere with the landscape, and the harmful substances contained in industrial solid waste can enter the soil through rainfall and infiltration, causing groundwater pollution, even killing microorganisms in the soil and destroying the ecological balance. Secondary pollution of industrial solid waste. Therefore, it is necessary to use scientific and effective methods to monitor and manage industrial solid waste to reduce environmental pollution. Here, remote sensing monitoring plays an important role. The classification of industrial solid remote sensing image is an effective way of industrial solid remote sensing monitoring. The accuracy of the classification directly affects the accuracy of the monitoring results. However, the traditional classification of remote sensing images is mostly based on pixel level classification methods, without comprehensive consideration of multiple features of the image, which leads to the classification accuracy is not very high. In this paper, an object oriented classification method of industrial solid waste remote sensing image is proposed, which considers the spectral, shape and texture information of the image. The method of combining graph theory with support vector machine (SVM) is used to classify remote sensing images. The object oriented classification method of industrial solid waste remote sensing image includes the following steps: 1) presegmentation of remote sensing image based on quadtree preprocessing; Secondly, the spectral similarity, the matching degree between pixels and the texture similarity of each partition block are calculated respectively, and the corresponding weight components are obtained. 3) using the R-cut cut set criterion in graph theory to further multi-feature segmentation of image; Finally, SVM is used to classify the segmentation results and the final classification results are obtained. In this paper, several features of remote sensing images are synthesized, and the classification of industrial solid waste remote sensing images is carried out by combining graph theory with SVM. The experimental results show that compared with the traditional classification methods, such as Markov distance method and spectral angle mapping method, the overall accuracy and kappa coefficient of this method are higher than theirs. This method can be used to classify and treat industrial solid waste image effectively.
【學(xué)位授予單位】：北方民族大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751

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