本文選題：高光譜圖像 + 模式分類　； 參考：《重慶大學(xué)》2014年博士論文

【摘要】：高光譜遙感是當(dāng)前遙感技術(shù)發(fā)展的一個(gè)前沿領(lǐng)域，它利用很多很窄的電磁波波段從感興趣的物體獲得有用信息。高光譜圖像作為遙感領(lǐng)域的一項(xiàng)重大突破，在保留較高空間分辨率同時(shí)，其光譜分辨率有極大的提高，達(dá)到了納米的數(shù)量級，可以用來探測和識別傳統(tǒng)全色和多光譜遙感中不可探測的地物類別。與傳統(tǒng)的多光譜遙感圖像相比，高光譜遙感圖像有著信息量大、光譜分辨率高等特點(diǎn)，這使得在描述與區(qū)分地物類別方面的能力有了大幅提高，進(jìn)而為地物光譜信息的精確處理與分析提供了可能。高光譜遙感系統(tǒng)已在全球許多國家的先進(jìn)對地觀察遙感系統(tǒng)中占有重要的位置，己成為地球陸地、海洋、大氣觀察的生力軍。但是由于高光譜圖像具有較高的數(shù)據(jù)維數(shù)，常規(guī)的圖像分類方法在處理高光譜圖像時(shí)有較大的限制，如何從大量的高光譜數(shù)據(jù)中快速而準(zhǔn)確地挖掘出所需要的信息，，實(shí)現(xiàn)高精度的分類，仍是一個(gè)亟待解決的問題。本文從高光譜圖像數(shù)據(jù)的特點(diǎn)入手，在對現(xiàn)有算法進(jìn)行分析的基礎(chǔ)上，針對高光譜遙感圖像分類算法進(jìn)行深入研究。主要的研究工作如下： ①在對高光譜遙感影像進(jìn)行預(yù)處理之后，對所用高光譜圖像做了大氣校正。幾何校正選取為二次多項(xiàng)式模型，重采樣采用的是最近鄰插值法，精度方面的要求得到了充分保證，為下一步的正確分類打下了堅(jiān)實(shí)的基礎(chǔ)。 ②提出了一種基于自適應(yīng)粒子群優(yōu)化算法的RBF神經(jīng)網(wǎng)絡(luò)高光譜遙感圖像分類方法。由于人工神經(jīng)網(wǎng)絡(luò)具有并行處理、模糊識別和非線性映射等優(yōu)點(diǎn)，很適合高光譜圖像分類，但是其參數(shù)難選。采用自適應(yīng)粒子群優(yōu)化算法對RBF神經(jīng)網(wǎng)絡(luò)的參數(shù)進(jìn)行了優(yōu)化，建立了基于粒子群優(yōu)化算法的的RBF神經(jīng)網(wǎng)絡(luò)模型，分類實(shí)驗(yàn)結(jié)果表明了基于粒子群優(yōu)化的RBF神經(jīng)網(wǎng)絡(luò)模型具有很高的分類精度。 ③提出了一種基于自適應(yīng)粒子群優(yōu)化算法的SVR高光譜遙感圖像分類方法。首先分析了支持向量回歸的核函數(shù)的構(gòu)造和模型參數(shù)的優(yōu)選問題。由于本文數(shù)據(jù)樣本較少，模型參數(shù)優(yōu)選的比較復(fù)雜，本文采用了CV估計(jì)模型推廣誤差，并使用自適應(yīng)粒子群優(yōu)化算法來優(yōu)選SVR模型參數(shù)，構(gòu)建了基于粒子群優(yōu)化算法的SVR高光譜遙感圖像分類模型，在一定程度上解決了高光譜數(shù)據(jù)標(biāo)記樣本不足的問題。 ④從稀疏表示的基本理論出發(fā)提出了一種基于自適應(yīng)稀疏表示的高光譜分類方法。利用訓(xùn)練樣本構(gòu)建字典，聚類每一步迭代所產(chǎn)生的余項(xiàng)，將聚類中心作為新的字典原子，然后將測試樣本看成冗余字典中訓(xùn)練樣本的線性組合，令字典能夠更適應(yīng)于樣本的稀疏表示。通過對高光譜圖像的分類實(shí)驗(yàn)，驗(yàn)證了自適應(yīng)稀疏表示算法的有效性。
[Abstract]:Hyperspectral remote sensing is a frontier field in the development of remote sensing technology. It uses a lot of narrow electromagnetic wave bands to obtain useful information from objects of interest. As a major breakthrough in the field of remote sensing, the spectral resolution of hyperspectral images has been greatly improved, reaching the order of magnitude of nanometer, while retaining higher spatial resolution. It can be used to detect and identify undetectable features in traditional panchromatic and multispectral remote sensing. Compared with traditional multispectral remote sensing images, hyperspectral remote sensing images have the characteristics of large amount of information and high spectral resolution. It also provides the possibility for the accurate processing and analysis of the spectral information of ground objects. Hyperspectral remote sensing system has played an important role in the advanced earth observation remote sensing system in many countries all over the world, and has become a new force in the observation of the earth's land, ocean and atmosphere. However, because of the high data dimension of hyperspectral images, the conventional image classification methods have great limitations in processing hyperspectral images. How to quickly and accurately mine the needed information from a large number of hyperspectral data. The realization of high-precision classification is still a problem to be solved. Based on the characteristics of hyperspectral image data and the analysis of existing algorithms, the classification algorithm of hyperspectral remote sensing image is studied in this paper. The main research work is as follows: 1 after preprocessing the hyperspectral remote sensing image, the atmospheric correction of the hyperspectral image is done. The geometric correction is chosen as quadratic polynomial model, and the nearest neighbor interpolation method is used for resampling. It lays a solid foundation for correct classification in the next step. 2 A RBF neural network hyperspectral remote sensing image classification method based on adaptive particle swarm optimization algorithm is proposed. Because of the advantages of parallel processing, fuzzy recognition and nonlinear mapping, artificial neural network is suitable for hyperspectral image classification, but its parameters are difficult to select. The parameters of RBF neural network are optimized by adaptive particle swarm optimization algorithm, and the RBF neural network model based on particle swarm optimization algorithm is established. The classification experiment results show that the RBF neural network model based on particle swarm optimization has high classification accuracy. 3 A SVR hyperspectral remote sensing image classification method based on adaptive particle swarm optimization algorithm is proposed. Firstly, the construction of kernel function of support vector regression and the optimization of model parameters are analyzed. Because of the small number of data samples and the complexity of the optimal selection of the model parameters, the CV estimation model is used to extend the error, and the adaptive particle swarm optimization algorithm is used to optimize the SVR model parameters. The SVR hyperspectral remote sensing image classification model based on particle swarm optimization algorithm is constructed. To some extent, the problem of insufficient samples of hyperspectral data markers is solved. 4 based on the basic theory of sparse representation, a hyperspectral classification method based on adaptive sparse representation is proposed. Using training samples to construct dictionaries, the remainder of each iteration is clustered, the cluster center is regarded as a new dictionary atom, and the test samples are regarded as linear combinations of training samples in redundant dictionaries. The dictionary is more suitable for sparse representation of samples. The effectiveness of the adaptive sparse representation algorithm is verified by the classification experiments of hyperspectral images.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TP751

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本文編號：2018269