【摘要】：近年來，隨著高光譜成像技術(shù)的發(fā)展，高光譜影像的目標(biāo)檢測算法日益受到關(guān)注。高光譜影像光譜分辨率較高，且具有圖譜合一的特點，每一個波段形成一幅圖像，同一像元的不同波段連起來又形成一條連續(xù)的光譜曲線，這使得其在目標(biāo)檢測中有獨特的優(yōu)勢。另一方面，由于傳感器空間分辨率有限，加上地物分布復(fù)雜，有時會出現(xiàn)目標(biāo)的大小小于一個像元，和多種地物混合在一起的情況，這種情況下的目標(biāo)稱為亞像元目標(biāo)�；旌舷裨墓庾V與目標(biāo)差別較大，難以識別。這使得亞像元目標(biāo)檢測成為高光譜信息處理的難點問題，加上大氣、光照等條件的影響，更加大了目標(biāo)檢測的難度。 在高光譜影像亞像元檢測時，現(xiàn)有的方法多假設(shè)影像的噪聲服從多元正態(tài)分布。然而，該假設(shè)并不完全符合高光譜影像中的實際情況。在對高光譜圖像中噪聲分布模型進(jìn)行了仔細(xì)研究后，我們發(fā)現(xiàn)噪聲梯度的分布同樣符合多元正態(tài)分布。為了提高亞像元檢測的檢測效果，有必要將該先驗引入目標(biāo)檢測算法。本文針對高光譜亞像元檢測問題展開研究，提出了新的目標(biāo)檢測算法。本文的主要研究內(nèi)容如下：(1)根據(jù)噪聲梯度分布的先驗，對現(xiàn)有的線性混合模型進(jìn)行改進(jìn)，提出了混合梯度模型；(2)將本文提出的混合梯度模型引入統(tǒng)計假設(shè)檢驗框架，提出兩種混合梯度探測器，分別對應(yīng)結(jié)構(gòu)背景假設(shè)和非結(jié)構(gòu)背景假設(shè)。與傳統(tǒng)的高光譜亞像元目標(biāo)檢測算法相比，，本文提出的算法有如下優(yōu)點：(1)對噪聲分布的描述更符合實際情況，因此抗干擾性較強；(2)改進(jìn)了像元混合模型，對亞像元目標(biāo)有更好的檢測效果。
[Abstract]:In recent years, with the development of hyperspectral imaging technology, the target detection algorithm of hyperspectral image has been paid more and more attention. The spectral resolution of hyperspectral image is high, and it has the characteristic of unifying the spectrum. Each band forms an image, and the different bands of the same pixel form a continuous spectral curve. This makes it have a unique advantage in target detection. On the other hand, due to the limited spatial resolution of sensors and the complex distribution of ground objects, sometimes the target size is smaller than one pixel and mixed with a variety of ground objects. In this case, the target is called sub-pixel target. The spectrum of the mixed pixel is different from that of the target, so it is difficult to recognize. This makes subpixel target detection become a difficult problem in hyperspectral information processing, combined with the influence of atmosphere, illumination and other conditions, which increases the difficulty of target detection. In hyperspectral image subpixel detection, the existing methods assume that the noise of the image is multivariate normal distribution. However, this assumption does not fully conform to the actual situation in hyperspectral images. After a careful study of the noise distribution model in hyperspectral images, we find that the noise gradient distribution also accords with the multivariate normal distribution. In order to improve the detection effect of subpixel detection, it is necessary to introduce the priori into the target detection algorithm. In this paper, a new target detection algorithm is proposed to solve the problem of hyperspectral subpixel detection. The main contents of this paper are as follows: (1) according to the prior noise gradient distribution, the existing linear mixed model is improved and the mixed gradient model is proposed. (2) the mixed gradient model proposed in this paper is introduced into the statistical hypothesis test framework, and two kinds of hybrid gradient detectors are proposed, which correspond to the structural background hypothesis and the non-structural background hypothesis, respectively. Compared with the traditional hyperspectral sub-pixel target detection algorithm, the proposed algorithm has the following advantages: (1) the description of the noise distribution is more in line with the actual situation, so the anti-jamming is stronger; (2) the mixed pixel model is improved, and the detection effect of sub-pixel target is better.
【學(xué)位授予單位】：中國科學(xué)院研究生院（西安光學(xué)精密機械研究所）
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP391.41

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本文編號：2317342