本文選題：圖像分割 + 圖像分類��； 參考：《電子科技大學》2017年碩士論文

【摘要】：目前高分辨率遙感圖像的應用呈現(xiàn)兩個增長趨勢,一個是應用領域的增加,一個是應用復雜度的增加。高分辨率遙感圖像在城市土地使用情況統(tǒng)計、城市生態(tài)評估、災害評估、農業(yè)灌溉等方面均有重要應用。遙感圖像空間分辨率的增加,一方面使得圖像中的地物細節(jié)更清晰,另一方面也增加了圖像信息分析的難度。為了提高分類的正確率,本文結合了空間特征和顏色特征等多種特征。同時,為了處理高分辨率圖像的大量數(shù)據并減少計算量,在圖像分割和分類中應用了面向對象的思想。本文主要工作如下:1.將面向對象的思想引入遙感圖像分割。面向對象的分析方法不僅具有良好的抗噪聲性,且在降低計算量的同時能夠保證分割結果的準確性。為限制計算復雜度,本文通過適當?shù)姆炙畮X變換得到圖像的超像素表示。本文采用區(qū)域鄰接圖(region-adjacency graph,RAG)度量初始超像素塊的相似性,將圖像分割問題轉化為圖割問題。多組實驗表明,使用基于超像素的分割方法所得分割結果幾乎不存在過分割現(xiàn)象,并且分割結果的邊界正確率得到較好保證。2.建立包含多種特征的特征集合,用于高分辨率遙感圖像的分類。傳統(tǒng)分類算法中,依靠光譜特征和紋理特征實現(xiàn)遙感圖像的分類。然而高分辨率圖像中大量增加的地物細節(jié)對特征提出了新的要求。為了有效描述圖像的空間信息,增加形態(tài)學特征APs(morphological attribute profiles)。APs特征可以根據選擇的屬性類型生成不同的特征。與常規(guī)的基于預定義的結構元的形態(tài)濾波器相比,APs可以提供一個多層次的圖像分析,從而得到更精確的空間信息。本文通過大量實驗驗證了APs特征用于高分辨率圖像分類的有效性。顏色特征的引入,豐富了特征集合,增強了不同類別之間的區(qū)分度。本文實驗表明,顏色特征的增加能夠改善圖像中陰影等地物的分類情況。由于不同特征在提取圖像信息時各有側重,因而如何選擇合適的特征是圖像分類的關鍵問題之一。本文研究了不同特征組合的分類結果,利用SVM分類算法實現(xiàn)面向對象的分類。分類以結合圖論的基于超像素的分割算法所得分割結果為基礎進行。統(tǒng)計6種特征組合的分類結果并分析,發(fā)現(xiàn)聯(lián)合光譜特征和空間特征以及顏色特征的特征集合可以獲得較為理想的分類結果。
[Abstract]:At present, the application of high resolution remote sensing image shows two increasing trends, one is the increase of application field and the other is the increase of application complexity. High-resolution remote sensing images have important applications in urban land use statistics, urban ecological assessment, disaster assessment, agricultural irrigation and so on. The increase of spatial resolution of remote sensing image makes the details of ground objects more clear on the one hand and makes the analysis of image information more difficult on the other hand. In order to improve the accuracy of classification, this paper combines spatial features and color features. At the same time, in order to deal with a large number of high-resolution image data and reduce the amount of computation, the object-oriented idea is applied in image segmentation and classification. The main work of this paper is as follows: 1. The object-oriented idea is introduced into remote sensing image segmentation. The object-oriented analysis method not only has good anti-noise performance, but also can ensure the accuracy of the segmentation results while reducing the computational complexity. In order to limit the computational complexity, the super-pixel representation of the image is obtained by appropriate watershed transformation. In this paper, region-adjacency graph rag is used to measure the similarity of initial superpixel blocks, and the image segmentation problem is transformed into graph cutting problem. Multi-group experiments show that there is almost no over-segmentation phenomenon in the segmentation results obtained by using the hyperpixel segmentation method, and the boundary accuracy of the segmentation results is better guaranteed. 2. A feature set consisting of multiple features is established for the classification of high resolution remote sensing images. In the traditional classification algorithm, remote sensing image classification is realized by spectral feature and texture feature. However, a large number of feature details in high resolution images require new features. In order to effectively describe the spatial information of the image, the addition of morphological features APs(morphological attribute profiles).APs features can generate different features according to the selected attribute types. Compared with the conventional morphological filter based on predefined structure elements, the APs can provide a multi-level image analysis to obtain more accurate spatial information. The effectiveness of APs feature in high resolution image classification is verified by a large number of experiments in this paper. The introduction of color features enriches feature sets and enhances the differentiation between different categories. Experiments show that the increase of color features can improve the classification of objects such as shadows in images. Because different features have different emphases in extracting image information, how to select suitable features is one of the key problems in image classification. In this paper, the classification results of different feature combinations are studied, and the object-oriented classification is realized by using SVM classification algorithm. The classification is based on the segmentation results obtained from the hyperpixel segmentation algorithm combined with graph theory. The classification results of six kinds of feature combinations are analyzed and it is found that the combination of spectral features and spatial features and the feature sets of color features can obtain more ideal classification results.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP751

【參考文獻】

相關期刊論文 前1條

1 ;Review of Remotely Sensed Imagery Classification Patterns Based on Object-oriented Image Analysis[J];Chinese Geographical Science;2006年03期

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本文編號：1934642