基于稀疏性約束的高光譜圖像處理方法研究
發(fā)布時間：2018-11-21 08:09
【摘要】：隨著傳感器技術(shù)的發(fā)展,光譜成像技術(shù)得到了空前的發(fā)展。高光譜遙感除了獲取圖像的空間信息外,還可以得到精細的光譜信息,在軍事偵察和國民經(jīng)濟等各個領(lǐng)域的應(yīng)用越來越廣泛。但是,隨著高光譜圖像的分辨率不斷提高,成像光譜儀獲取的圖像數(shù)據(jù)已經(jīng)遠遠超出了數(shù)據(jù)傳輸和處理能力。基于信號稀疏性約束的處理方法近年來廣泛應(yīng)用于信號處理、模式識別和計算機視覺等方面。如何有效的利用光譜圖像的稀疏性,已經(jīng)成為遙感信息處理領(lǐng)域重要的研究方向之一。針對高光譜圖像數(shù)據(jù)處理難題,本文主要分析了高光譜圖像的稀疏性,在此基礎(chǔ)上研究了基于稀疏性約束的高光譜圖像分類和目標檢測,主要工作如下:首先,論文分析與驗證了高光譜圖像的稀疏性。分析高光譜圖像數(shù)據(jù)的典型特性,并利用無監(jiān)督的學(xué)習(xí)方法構(gòu)建字典對高光譜圖像數(shù)據(jù)進行稀疏分解,將圖像中實際包含的物理材料的光譜曲線與字典原子進行比對,證明學(xué)習(xí)的字典原子可以很好地與材料的光譜曲線擬合,驗證了高光譜圖像的稀疏性。其次,論文提出了基于稀疏嵌入的高光譜圖像分類方法。針對高光譜圖像的高維特性,利用稀疏嵌入的方法對高光譜圖像進行特征提取,通過保持類內(nèi)緊湊性的條件下進行類內(nèi)稀疏重建,同時最大限度地增大類間距離,以增強高光譜數(shù)據(jù)在特征空間投影的離散度。通過對真實數(shù)據(jù)進行測試表明,本文方法在分類時間和分類精度上比起其它方法都有一定的提高。最后,論文研究了高光譜圖像異常檢測問題,提出了一種基于金字塔空-譜協(xié)同編碼的高光譜圖像異常檢測方法。首先在優(yōu)化樣本-特征分布的行稀疏性、列稀疏性和行分布統(tǒng)計相似性的基礎(chǔ)上,采用無監(jiān)督的學(xué)習(xí)方法提取低維區(qū)分性特征;其次,利用空間金字塔思想在多個空間尺度上對局部像素進行空-譜協(xié)同編碼;最后統(tǒng)計編碼差異性,定位異常。在實測數(shù)據(jù)集的實驗結(jié)果驗證了方法的有效性和魯棒性。
[Abstract]:With the development of sensor technology, spectral imaging technology has been unprecedented development. Hyperspectral remote sensing not only can obtain spatial information of images, but also can obtain fine spectral information. It is more and more widely used in military reconnaissance and national economy and other fields. However, with the improvement of the resolution of hyperspectral images, the image data obtained by the imaging spectrometer is far beyond the ability of data transmission and processing. In recent years, signal sparsity constraint based processing methods have been widely used in signal processing, pattern recognition and computer vision. How to make effective use of spectral image sparsity has become one of the important research directions in remote sensing information processing field. Aiming at the difficult problem of hyperspectral image data processing, this paper mainly analyzes the sparsity of hyperspectral image, and then studies the classification and target detection of hyperspectral image based on sparse constraint. The main work is as follows: first, The sparsity of hyperspectral images is analyzed and verified. This paper analyzes the typical characteristics of hyperspectral image data, constructs a dictionary to sparse decompose the hyperspectral image data by using unsupervised learning method, and compares the spectral curve of the physical material actually contained in the image with the dictionary atom. It is proved that the dictionary atoms can fit well with the spectral curves of the materials, and the sparsity of hyperspectral images is verified. Secondly, a method of hyperspectral image classification based on sparse embedding is proposed. In view of the high dimensional characteristics of hyperspectral images, the method of sparse embedding is used to extract the features of hyperspectral images, and the intra-class sparse reconstruction is carried out under the condition of keeping intra-class compactness, and the distance between classes is maximized. In order to enhance the dispersion of hyperspectral data projection in feature space. By testing the real data, it is shown that the classification time and accuracy of this method are better than those of other methods. Finally, the problem of hyperspectral image anomaly detection is studied, and a hyperspectral image anomaly detection method based on pyramid space-spectrum cooperative coding is proposed. Firstly, on the basis of optimizing the row sparsity, column sparsity and the statistical similarity of row distribution, the unsupervised learning method is used to extract the low-dimensional distinguishing features. Secondly, spatial pyramid is used to cocode the local pixels on multiple spatial scales. Finally, the differences of coding are statistically analyzed, and the anomalies are located. The effectiveness and robustness of the method are verified by the experimental results of the measured data sets.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP751
本文編號：2346402
[Abstract]:With the development of sensor technology, spectral imaging technology has been unprecedented development. Hyperspectral remote sensing not only can obtain spatial information of images, but also can obtain fine spectral information. It is more and more widely used in military reconnaissance and national economy and other fields. However, with the improvement of the resolution of hyperspectral images, the image data obtained by the imaging spectrometer is far beyond the ability of data transmission and processing. In recent years, signal sparsity constraint based processing methods have been widely used in signal processing, pattern recognition and computer vision. How to make effective use of spectral image sparsity has become one of the important research directions in remote sensing information processing field. Aiming at the difficult problem of hyperspectral image data processing, this paper mainly analyzes the sparsity of hyperspectral image, and then studies the classification and target detection of hyperspectral image based on sparse constraint. The main work is as follows: first, The sparsity of hyperspectral images is analyzed and verified. This paper analyzes the typical characteristics of hyperspectral image data, constructs a dictionary to sparse decompose the hyperspectral image data by using unsupervised learning method, and compares the spectral curve of the physical material actually contained in the image with the dictionary atom. It is proved that the dictionary atoms can fit well with the spectral curves of the materials, and the sparsity of hyperspectral images is verified. Secondly, a method of hyperspectral image classification based on sparse embedding is proposed. In view of the high dimensional characteristics of hyperspectral images, the method of sparse embedding is used to extract the features of hyperspectral images, and the intra-class sparse reconstruction is carried out under the condition of keeping intra-class compactness, and the distance between classes is maximized. In order to enhance the dispersion of hyperspectral data projection in feature space. By testing the real data, it is shown that the classification time and accuracy of this method are better than those of other methods. Finally, the problem of hyperspectral image anomaly detection is studied, and a hyperspectral image anomaly detection method based on pyramid space-spectrum cooperative coding is proposed. Firstly, on the basis of optimizing the row sparsity, column sparsity and the statistical similarity of row distribution, the unsupervised learning method is used to extract the low-dimensional distinguishing features. Secondly, spatial pyramid is used to cocode the local pixels on multiple spatial scales. Finally, the differences of coding are statistically analyzed, and the anomalies are located. The effectiveness and robustness of the method are verified by the experimental results of the measured data sets.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP751
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