基于多特征融合的衛(wèi)星遙感圖像分類研究
發(fā)布時間：2018-11-23 13:23
【摘要】：隨著遙感事業(yè)的蓬勃發(fā)展,衛(wèi)星遙感圖像受到人們越來越多的關注。高光譜圖像作為衛(wèi)星遙感圖像的一個重要分支,其本身具有的高維數(shù)據蘊含了豐富的信息待我們深入挖掘。高光譜圖像分類問題是現(xiàn)階段遙感圖像研究領域的一個熱門問題,該問題涉及計算機圖像學、數(shù)理統(tǒng)計學、矩陣論等多個學科理論。在高光譜圖像分類領域,目前較為流行的分類方法是利用基于統(tǒng)計學習的機器學習分類算法(監(jiān)督學習方法,無監(jiān)督學習方法),通過建立分類模型來預測預測圖像中每個像素點的類別。當使用有監(jiān)督分類算法時,由于高光譜圖像每個像素點對應的特征向量(光譜特征)維度較高,并且一般可供使用的訓練樣本數(shù)量稀少,會導致很嚴重的“Hughes”現(xiàn)象,這種特性導致了高光譜數(shù)據對分類算法的選擇頗為敏感。根據前人的大量實驗可知,支持向量機分類算法(SVM)比較適合高光譜圖像的分類問題,此算法能夠很好地克服訓練集少、特征維度高等問題。在高光譜圖像分類時,現(xiàn)有方法一般僅依賴于光譜特征,從而忽略了高光譜圖像所蘊含的空間特征(即空間地理信息)。因此,為了提高圖像分類精度,在分類過程中如何有效地融合這兩種特征所提供的信息成為亟待解決的問題。本文中使用屬性特征方法(Attribute Profiles,APs)提取空間特征,該方法利用屬性過濾器(Attribute Filters,AFs)掃描高光譜圖像的每個通道,計算AF覆蓋區(qū)域的屬性值,與一系列閾值比較后,得到對應像素點的空間特征;將得到的空間特征和光譜特征加權相加從而實現(xiàn)多特征融合,利用融合后的特征建立SVM分類模型。在特征融合之前,需要對數(shù)據中夾雜的噪聲進行處理。近些年來,基于字典學習的矩陣稀疏表達方法越來越多地被應用于數(shù)據特征處理。在圖像領域比較有代表性的稀疏表達方法是矩陣低秩分解,并結合字典學習后轉換為矩陣低秩表達算法(LRR)。該方法通過矩陣分解還原矩陣的低秩性,使得噪聲數(shù)據與原來的數(shù)據分解開來,從而得到高質量的特征。結合衛(wèi)星遙感圖像的特點,根據相鄰元素具有相同類別的假設,對整幅圖像分塊進行低秩表達,本文提出使用基于區(qū)域劃分的LRR對高光譜圖像的光譜特征和空間特征進行去噪處理,對得到的新特征進行特征融合,最后再利用SVM分類算法建立分類模型;通過實驗證明,本文提出的方法對于高光譜圖像分類問題可以得到較高的分類精度,另與基于核函數(shù)的特征融合方法對比具有明顯的優(yōu)勢。
[Abstract]:With the rapid development of remote sensing, people pay more and more attention to satellite remote sensing image. As an important branch of satellite remote sensing images, hyperspectral images contain abundant information for us to mine deeply. The problem of hyperspectral image classification is a hot issue in the field of remote sensing image research at present. This problem involves many disciplines such as computer graphics mathematical statistics matrix theory and so on. In the field of hyperspectral image classification, the most popular classification methods are machine learning (supervised learning, unsupervised learning), which is based on statistical learning. The classification model is established to predict the classification of each pixel in the image. When the supervised classification algorithm is used, because of the high dimension of the feature vector (spectral feature) corresponding to each pixel of hyperspectral image and the small number of training samples generally available for use, the phenomenon of "Hughes" will be very serious. This property leads to hyperspectral data being sensitive to the selection of classification algorithms. According to a large number of previous experiments, support vector machine (SVM) classification algorithm (SVM) is more suitable for hyperspectral image classification. This algorithm can overcome the problems of less training set and higher feature dimension. In the classification of hyperspectral images, the existing methods generally only depend on spectral features, thus ignoring the spatial features (i.e. spatial geographic information) contained in hyperspectral images. Therefore, in order to improve the accuracy of image classification, how to effectively fuse the information provided by these two features in the process of classification has become an urgent problem. In this paper, attribute feature method (Attribute Profiles,APs) is used to extract spatial features. In this method, attribute filter (Attribute Filters,AFs) is used to scan each channel of hyperspectral image, and the attribute value of AF coverage area is calculated, which is compared with a series of thresholds. The spatial features of the corresponding pixels are obtained. The spatial features and spectral features are weighted together to achieve multi-feature fusion, and the SVM classification model is established by using the fused features. It is necessary to deal with the noise in the data before feature fusion. In recent years, the sparse representation of matrix based on dictionary learning has been applied to data feature processing more and more. The sparse representation method in image field is matrix low rank decomposition, which is converted to matrix low rank representation algorithm (LRR). After dictionary learning. By decomposing the low rank of the matrix, the noise data is decomposed from the original data, and the high quality characteristic is obtained. Considering the characteristics of satellite remote sensing images and the assumption that adjacent elements have the same category, the block representation of the whole image is carried out in low rank. In this paper, LRR based on region division is used to de-noise the spectral and spatial features of hyperspectral images, and the new features are fused. Finally, the classification model is established by using the SVM classification algorithm. The experimental results show that the proposed method can achieve high classification accuracy for hyperspectral image classification and has obvious advantages compared with the kernel-based feature fusion method.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP751
[Abstract]:With the rapid development of remote sensing, people pay more and more attention to satellite remote sensing image. As an important branch of satellite remote sensing images, hyperspectral images contain abundant information for us to mine deeply. The problem of hyperspectral image classification is a hot issue in the field of remote sensing image research at present. This problem involves many disciplines such as computer graphics mathematical statistics matrix theory and so on. In the field of hyperspectral image classification, the most popular classification methods are machine learning (supervised learning, unsupervised learning), which is based on statistical learning. The classification model is established to predict the classification of each pixel in the image. When the supervised classification algorithm is used, because of the high dimension of the feature vector (spectral feature) corresponding to each pixel of hyperspectral image and the small number of training samples generally available for use, the phenomenon of "Hughes" will be very serious. This property leads to hyperspectral data being sensitive to the selection of classification algorithms. According to a large number of previous experiments, support vector machine (SVM) classification algorithm (SVM) is more suitable for hyperspectral image classification. This algorithm can overcome the problems of less training set and higher feature dimension. In the classification of hyperspectral images, the existing methods generally only depend on spectral features, thus ignoring the spatial features (i.e. spatial geographic information) contained in hyperspectral images. Therefore, in order to improve the accuracy of image classification, how to effectively fuse the information provided by these two features in the process of classification has become an urgent problem. In this paper, attribute feature method (Attribute Profiles,APs) is used to extract spatial features. In this method, attribute filter (Attribute Filters,AFs) is used to scan each channel of hyperspectral image, and the attribute value of AF coverage area is calculated, which is compared with a series of thresholds. The spatial features of the corresponding pixels are obtained. The spatial features and spectral features are weighted together to achieve multi-feature fusion, and the SVM classification model is established by using the fused features. It is necessary to deal with the noise in the data before feature fusion. In recent years, the sparse representation of matrix based on dictionary learning has been applied to data feature processing more and more. The sparse representation method in image field is matrix low rank decomposition, which is converted to matrix low rank representation algorithm (LRR). After dictionary learning. By decomposing the low rank of the matrix, the noise data is decomposed from the original data, and the high quality characteristic is obtained. Considering the characteristics of satellite remote sensing images and the assumption that adjacent elements have the same category, the block representation of the whole image is carried out in low rank. In this paper, LRR based on region division is used to de-noise the spectral and spatial features of hyperspectral images, and the new features are fused. Finally, the classification model is established by using the SVM classification algorithm. The experimental results show that the proposed method can achieve high classification accuracy for hyperspectral image classification and has obvious advantages compared with the kernel-based feature fusion method.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP751
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