本文選題：高光譜圖像分類 + 特征選擇��； 參考：《西安電子科技大學(xué)》2014年碩士論文

【摘要】：高光譜圖像是成像光譜儀在數(shù)十甚至數(shù)百個以上的連續(xù)的光譜通道上對地物持續(xù)遙感成像所成的圖像。由于高光譜圖像具有很高的光譜分辨率和豐富的波段信息，所以高光譜圖像在很多領(lǐng)域都應(yīng)用廣泛，，例如植被、生態(tài)、大氣、以及海洋等研究領(lǐng)域。但是正因為高光譜圖像的波段多，所以它的相鄰波段的相關(guān)性高，造成了波段冗余，信息重復(fù)等問題。所以對于高光譜圖像來說，特征選擇和分類技術(shù)已經(jīng)成為高光譜圖像研究的熱點。本文在高光譜圖像特征選擇和分類上做了以下三個工作： (1)針對傳統(tǒng)的基于像素的高光譜圖像分類的方法中，只是利用了譜段信息進(jìn)行分類，沒有考慮圖像的空間的相關(guān)性，本文提出了一種基于均值漂移和稀疏表示分類的高光譜圖像空譜分類方法。采用融合機制融合由基于稀疏表示分類得到的高光譜圖像分類圖和利用均值漂移聚類得到的包含不同像素點的封閉區(qū)域的分割圖，得到最終的分類結(jié)果圖。通過該策略，將自適應(yīng)空間信息融入了分類結(jié)果中，增強了區(qū)域一致性，大大提高了分類正確性。 (2)提出了基于多目標(biāo)免疫克隆算法實現(xiàn)高光譜圖像同時波段選擇和分割，在聚類數(shù)未知的情況下，分割部分采用多目標(biāo)免疫克隆聚類算法同時結(jié)合無監(jiān)督的特征選擇，在實現(xiàn)分割的同時實現(xiàn)高光譜圖像的波段選擇。并采用上一章的融合策略，將基于稀疏表示分類器得到的像素級分類圖與得到的分割圖進(jìn)行融合得到最終的分類結(jié)果圖。通過同時波段選擇和多目標(biāo)分類，在多幅高光譜圖像上可實現(xiàn)使用盡可能少的特征維數(shù)和在少量訓(xùn)練樣本得到較好的分類結(jié)果。 (3)提出了一種多目標(biāo)粒子群分類算法。粒子群分類是一種收斂快的全局優(yōu)化算法，這里我們在經(jīng)典的PSO分類的目標(biāo)函數(shù)上增加了另外兩個目標(biāo)函數(shù)，類內(nèi)判據(jù)和類間判據(jù)，我們把多目標(biāo)粒子群優(yōu)化算法用于UCI數(shù)據(jù)集和高光譜圖像上進(jìn)行了分類，期望能在結(jié)合PSO和多目標(biāo)的優(yōu)勢得到比較好的分類結(jié)果。 本文的工作得到了國家自然科學(xué)基金(61272282)，“教育部新世紀(jì)優(yōu)秀人才支持計劃”(NCET-13-0948)和中央高�；A(chǔ)科研業(yè)務(wù)費(K50511020011)等項目的資助。
[Abstract]:Hyperspectral image is the image of continuous remote sensing imaging of ground objects in dozens or even more than hundreds of continuous spectral channels by imaging spectrometer. Because hyperspectral images have high spectral resolution and rich band information, hyperspectral images are widely used in many fields, such as vegetation, ecology, atmosphere and ocean. But because the hyperspectral image has many bands, the correlation of its adjacent bands is high, which leads to the redundancy of the band and the repetition of information. Therefore, for hyperspectral images, feature selection and classification techniques have become the focus of hyperspectral image research. In this paper, the following three works have been done on the feature selection and classification of hyperspectral images: In the traditional method of hyperspectral image classification based on pixels, only the spectral segment information is used to classify the image, and the spatial correlation of the image is not considered. In this paper, a space-spectrum classification method for hyperspectral images based on mean shift and sparse representation is proposed. The fusion mechanism is used to fuse the hyperspectral image classification map based on sparse representation and the segmentation map with different pixel points by means of mean shift clustering to obtain the final classification results. Through this strategy, adaptive spatial information is incorporated into the classification results, which enhances the regional consistency and greatly improves the classification accuracy. (2) A multi-objective immune clone algorithm is proposed to realize the simultaneous band selection and segmentation of hyperspectral images. In the case of unknown clustering number, the segmentation part adopts multi-objective immune clone clustering algorithm combined with unsupervised feature selection. At the same time, the band selection of hyperspectral image is realized. Using the fusion strategy in the previous chapter, the pixel level classification map based on sparse representation classifier and the segmentation graph are fused to obtain the final classification result graph. By simultaneous band selection and multi-target classification, it is possible to use as few feature dimensions as possible on multiple hyperspectral images and obtain better classification results in a small number of training samples. A multi-objective particle swarm optimization algorithm is proposed. Particle swarm optimization (PSO) is a fast convergent global optimization algorithm. Here we add two other objective functions to the objective function of the classical PSO classification, the intra-class criterion and the inter-class criterion. We apply the multi-objective particle swarm optimization algorithm to the classification of UCI data sets and hyperspectral images in order to obtain a better classification result by combining the advantages of PSO and multi-targets. The work of this paper is supported by the National Natural Science Foundation of China 61272282, NCET-13-0948) and K50511020011) of the National Natural Science Foundation of China (NCET-130948) and the National Natural Science Foundation of China (NCET-130948).
【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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