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  本文關(guān)鍵詞： 高光譜圖像 稀疏表示 空間信息 圖像分類 光譜解混　出處：《西安電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：高光譜圖像分類技術(shù)和光譜解混技術(shù)在高光譜遙感技術(shù)領(lǐng)域占有非常重要的地位,一直是國內(nèi)外學(xué)者的重點研究方向。近年來,隨著稀疏表示在圖像處理方面的廣泛應(yīng)用,一些學(xué)者開始將其應(yīng)用到了高光譜遙感圖像處理方面,尤其是高光譜圖像分類和光譜解混方向,已經(jīng)取得了一些成就。但是現(xiàn)有的基于稀疏表示的圖像分類和光譜解混方法只考慮了高光譜圖像的光譜信息,沒有考慮到圖像的空間信息和高光譜圖像本身的特征信息。在高光譜圖像中,存在這樣一種現(xiàn)象,相鄰像元可能包含相似或相同的物質(zhì),并且物質(zhì)的含量也是相似的,基于上述特征,可以利用中心像元的鄰居像元在分類和解混模型中添加空間約束,將高光譜圖像的空間信息和光譜信息結(jié)合起來,從而提高分類和解混的精度。論文的研究方向主要有以下兩方面:1.在高光譜圖像中,存在這樣一種現(xiàn)象,相鄰像元可能包含相似或相同的物質(zhì),這樣,它們很可能在分類過程中歸為同一類�；�于高光譜圖像的這種特征,本文提出了一階鄰域系統(tǒng)加權(quán)約束,即用中心像元周圍的4個像元在分類模型中去約束該像元,并且與中心像元越相近的鄰居像元在約束中占的比重越大,該約束使得中心像元和周圍的4個像元在分類過程中包含同樣的光譜信息。然后將此約束添加到稀疏分類模型中,提出了一種基于一階鄰域系統(tǒng)加權(quán)約束的新的分類算法。為測試新算法的分類性能,利用常見的AVIRIS和ROSIS傳感器搜集的高光譜圖像進(jìn)行實驗,采用總分類精度、平均分類精度和kappa系數(shù)3種評價標(biāo)準(zhǔn)評價算法性能。實驗結(jié)果表明,一階鄰域系統(tǒng)加權(quán)約束充分利用了空間信息和圖像本身的特征,分類精度有了大幅提高,分類性能優(yōu)于現(xiàn)有分類算法。2.高光譜圖像中的像元是由光譜信息和空間信息共同組成的,光譜信息是獨立的,而空間信息是相關(guān)的。由于馬爾科夫隨機(jī)場是一個模擬空間相關(guān)性的強(qiáng)大工具,它不僅考慮到圖像中相鄰像元的相關(guān)性,同時也考慮到了圖像本身的特征,所以本文采用馬爾科夫隨機(jī)場在稀疏解混模型中添加空間相關(guān)性約束,提出了一種基于自適應(yīng)的馬爾科夫隨機(jī)場的稀疏解混算法。為測試該算法的解混性能,本文提供了模擬圖像數(shù)據(jù)和真實的AVIRIS圖像數(shù)據(jù)進(jìn)行實驗,并利用SRE(信號噪聲比)分析實驗結(jié)果。實驗結(jié)果表明,對于模擬圖像,基于自適應(yīng)的馬爾科夫隨機(jī)場的稀疏解混算法取得了更高的SRE值,對于真實圖像,新算法解混后得到的豐度圖像比現(xiàn)有算法更加光滑,豐度圖像細(xì)節(jié)也展現(xiàn)的更加全面。
[Abstract]:The technology of hyperspectral image classification and spectral deconvolution plays a very important role in the field of hyperspectral remote sensing, and has been a key research direction of domestic and foreign scholars in recent years. With the wide application of sparse representation in image processing, some scholars begin to apply it to hyperspectral remote sensing image processing, especially hyperspectral image classification and spectral deconvolution. Some achievements have been made, but the existing image classification and spectral de-mixing methods based on sparse representation only consider the spectral information of hyperspectral images. The spatial information of the image and the characteristic information of the hyperspectral image are not taken into account. In the hyperspectral image, there is a phenomenon that adjacent pixels may contain similar or identical substances. And the content of matter is similar, based on the above characteristics, we can use the neighbor pixel of the center pixel to add spatial constraints to the classification and the mixed model, and combine the spatial information and spectral information of hyperspectral image. In order to improve the accuracy of classification and mixing. The main research direction of this paper is as follows: 1. In hyperspectral images, there is a phenomenon that adjacent pixels may contain similar or identical substances. It is very likely that they can be classified into the same class in the classification process. Based on this feature of hyperspectral images, a first-order neighborhood system weighted constraint is proposed in this paper. That is to say, four pixels around the center pixel are used to constrain the pixel in the classification model, and the neighbor pixel which is close to the center pixel occupies a larger proportion in the constraint. The constraint makes the central pixel and the surrounding pixel contain the same spectral information in the classification process. Then the constraint is added to the sparse classification model. A new classification algorithm based on weighted constraints of first-order neighborhood system is proposed. In order to test the classification performance of the new algorithm, hyperspectral images collected by common AVIRIS and ROSIS sensors are tested. The performance of the algorithm is evaluated by three evaluation criteria: general classification accuracy, average classification accuracy and kappa coefficient. The experimental results show that the weighted constraints of the first-order neighborhood system make full use of the spatial information and the features of the image itself. Classification accuracy has been greatly improved, classification performance is better than the existing classification algorithm .2.The pixel in hyperspectral image is composed of spectral information and spatial information, and spectral information is independent. Because Markov random field is a powerful tool to simulate spatial correlation, it not only considers the correlation of adjacent pixels in the image, but also takes into account the characteristics of the image itself. In this paper, a sparse demultiplexing algorithm based on adaptive Markov random field is proposed by adding spatial correlation constraints to the sparse demultiplexing model. In this paper, the simulated image data and the real AVIRIS image data are provided, and the experimental results are analyzed by using the SRE (signal-noise ratio). The experimental results show that, for the simulated images. The sparse demultiplexing algorithm based on adaptive Markov random field achieves a higher SRE value. For real images, the abundance images obtained by the new algorithm are more smooth than the existing algorithms. Abundance image details are also more comprehensive.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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本文編號：1457746