【摘要】：高光譜遙感是一種多維信息獲取技術(shù),它不僅可以獲得描述地物分布的二維空間信息,而且可以獲得對(duì)應(yīng)地物的一維光譜信息。高光譜遙感圖像的光譜分辨率很高,隨著光譜分辨率的提高,其對(duì)地物的認(rèn)知能力也不斷的提升,但是高光譜遙感圖像的空間分辨率仍然很低,混合像素普遍的存在于高光譜遙感圖像中。針對(duì)混合像素的處理,硬分類方法會(huì)導(dǎo)致地物信息的大量丟失,正因?yàn)槿绱?提出軟分類方法,具體來說包括端元提取,豐度反演和亞像元定位三個(gè)部分,端元提取算法提取高光譜圖像中所含有的端元,豐度反演是計(jì)算各端元在混合像素中所含有的豐度,亞像元定位技術(shù)預(yù)測各端元在混合像素中的分布。本文針對(duì)高光譜遙感圖像亞像元定位方法中一些關(guān)鍵問題進(jìn)行了研究。具體工作如下:首先,對(duì)亞像素和像素之間的吸引力模型(SPSAM),像元交換算法(PSA)等基礎(chǔ)的亞像元定位算法進(jìn)行了研究。SPSAM直接對(duì)亞像素進(jìn)行賦值,而且吸引力值計(jì)算方法極為粗糙,使其亞像元定位結(jié)果中出現(xiàn)很多獨(dú)立的像素。PSA具有高效的迭代速度,但是其缺點(diǎn)是對(duì)噪聲和亞像元的初始分布非常敏感。其次,對(duì)遺傳算法在亞像元定位中的應(yīng)用進(jìn)行了分析,由于遺傳算法中交叉算子選擇所交換基因的隨機(jī)性,使得其迭代效率很低,最終的亞像元定位結(jié)果精度也不高。本文提出了一種基于改進(jìn)的遺傳算法(MGA)的亞像元定位算法,該算法既結(jié)合了遺傳算法(GA)中種群思想的優(yōu)點(diǎn),又結(jié)合了像元交換算法(PSA)中高效的迭代速率的優(yōu)點(diǎn),使其迭代效率進(jìn)一步增強(qiáng)。最后,上述亞像元定位算法是以光譜解混所得的豐度圖像作為輸入,由于現(xiàn)有的光譜解混算法很難達(dá)到其精度要求,使得最終亞像元定位結(jié)果存在誤差的疊加,精度無法進(jìn)一步提高。針對(duì)這些算法,本文首先描述了馬爾科夫隨機(jī)場(MRF)在亞像元定位中的應(yīng)用,由于MRF可以結(jié)合空間和光譜信息,進(jìn)一步描述了基于多光譜約束MRF的亞像元定位算法,雖然基于多光譜約束的MRF亞像元定位算法可以進(jìn)一步提高SPM精度,但是由于其沒有考慮亞像素平移圖像(SSRSI)的空間信息,使得其精度有限,針對(duì)此缺點(diǎn),本文提出了基于多空間約束和多光譜約束的MRF亞像元定位算法,進(jìn)一步提高了亞像元定位的精度。
[Abstract]:Hyperspectral remote sensing is a multidimensional information acquisition technique, which can not only obtain two-dimensional spatial information to describe the distribution of ground objects, but also obtain one-dimensional spectral information of corresponding objects. The spectral resolution of hyperspectral remote sensing image is very high. With the improvement of spectral resolution, the cognitive ability of hyperspectral remote sensing image is improved, but the spatial resolution of hyperspectral remote sensing image is still very low. Mixed pixels are common in hyperspectral remote sensing images. For the processing of mixed pixels, the hard classification method will lead to a lot of loss of ground object information. Because of this, a soft classification method is proposed, which includes End-element extraction, abundance inversion and sub-pixel location. End-element extraction algorithm extracts endelements from hyperspectral images. Abundance inversion is used to calculate the abundance of each endelement in the mixed pixel, and sub-pixel localization technique is used to predict the distribution of each endelement in the mixed pixel. In this paper, some key problems in sub-pixel localization of hyperspectral remote sensing images are studied. The main work is as follows: firstly, the subpixel location algorithm based on the attraction model between sub-pixel and pixel, such as (SPSAM), pixel exchange algorithm (PSA), is studied. SPSAM directly assign sub-pixel value, and the calculation method of attraction value is very rough. Many independent pixels. PSA in the sub-pixel localization results have high iterative speed, but their disadvantages are that they are very sensitive to the initial distribution of noise and sub-pixel. Secondly, the application of genetic algorithm in sub-pixel location is analyzed. Because of the randomness of crossover operator selection, the iterative efficiency of genetic algorithm is very low, and the precision of final sub-pixel localization is not high. In this paper, a sub-pixel localization algorithm based on improved genetic algorithm (MGA) is proposed. This algorithm combines the advantages of population idea in genetic algorithm (GA) and the efficient iterative rate in pixel exchange algorithm (PSA). The iteration efficiency is further enhanced. Finally, the above sub-pixel localization algorithm is based on the abundance image obtained by spectral unmixing. Because the existing spectral de-mixing algorithms are difficult to achieve its precision requirements, the final sub-pixel localization results have a superposition of errors. The precision cannot be further improved. In view of these algorithms, this paper first describes the application of Markov random field (MRF) in sub-pixel localization. Because MRF can combine spatial and spectral information, the sub-pixel localization algorithm based on multi-spectral constraint MRF is further described. Although the MRF sub-pixel localization algorithm based on multi-spectral constraints can further improve the accuracy of SPM, but because it does not consider the spatial information of subpixel translation image (SSRSI), its accuracy is limited. In this paper, a MRF sub-pixel localization algorithm based on multi-spatial constraints and multi-spectral constraints is proposed, which further improves the accuracy of sub-pixel localization.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751

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