本文選題：高光譜圖像 + 圖像壓縮�。� 參考：《復(fù)旦大學(xué)》2014年碩士論文

【摘要】：高光譜遙感圖像成像光譜技術(shù)能夠在電磁波譜的紫外、可見光、近紅外與中紅外區(qū)域獲取很多非常窄但光譜連續(xù)的圖像數(shù)據(jù)。這些高光譜數(shù)據(jù)與傳統(tǒng)的遙感數(shù)據(jù)相比,其空間與譜間分辨率都大幅增加,從而使其在目標(biāo)檢測(cè)與識(shí)別,土地類型分析,農(nóng)業(yè)以及環(huán)境監(jiān)測(cè)研究等方面的應(yīng)用更為廣泛。但分辨率增加的同時(shí),其擁有的數(shù)據(jù)量也急劇增加,造成這些數(shù)據(jù)的保存和傳輸非常不便,因此對(duì)高光譜圖像進(jìn)行有效的壓縮顯得尤為必要。傳統(tǒng)的高光譜遙感圖像壓縮算法主要分為無(wú)損壓縮與有損壓縮。但由于對(duì)高光譜遙感圖像傳輸具有高壓縮比與實(shí)時(shí)性要求等苛刻條件,無(wú)損壓縮很難滿足要求,因而尋找高保真的有損壓縮方法成為當(dāng)前研究高光譜圖像技術(shù)人員的急切目標(biāo)。有損壓縮主要有基于離散小波變換的方法。離散小波變換能提供高壓縮比和高保真度,但其提供的解相關(guān)性能并非最好的。除了離散小波外,目前兩維情況下最好的解相關(guān)方法是基于主元分析的。但一般的主元分析只考慮到兩維情況,而對(duì)高維數(shù)據(jù),比如高光譜遙感圖像,其并不能很好地利用數(shù)據(jù)整體的信息。本文提出一種基于張量分解和小波包變換的高光譜圖像壓縮算法。首先,該算法能利用張量分解的性質(zhì),充分提取高光譜圖像中各個(gè)模式的信息,并利用其中包含有空間信息的光譜模式對(duì)高光譜圖像的光譜維進(jìn)行解相關(guān)。之后,運(yùn)用比經(jīng)典Mallat小波分解更為有效的小波包變換對(duì)光譜去相關(guān)后保留下來(lái)的高階主成分進(jìn)行JPEG2000壓縮。針對(duì)采用張量分解算法壓縮圖像耗時(shí)過(guò)長(zhǎng)的問(wèn)題,我們根據(jù)基于JPEG2000標(biāo)準(zhǔn)的圖像壓縮算法的特點(diǎn),采用改進(jìn)的二分搜索法,簡(jiǎn)單有效地降低了提出算法的時(shí)間復(fù)雜度。實(shí)驗(yàn)結(jié)果表明,提出的算法壓縮性能遠(yuǎn)遠(yuǎn)好于經(jīng)典的基于三維小波的算法,并且由于張量分解的應(yīng)用,不論在碼率失真表現(xiàn)還是信息保真度上,提出的算法均比基于二維主元分析的高光譜圖像壓縮算法更具優(yōu)勢(shì)。由于當(dāng)今高光譜圖像的用途越來(lái)越廣泛,因此特別針對(duì)高光譜圖像中的異常點(diǎn)信息的保持問(wèn)題,我們采用了另一種解決方案。在壓縮過(guò)程中先采用異常點(diǎn)檢測(cè)算法將異常點(diǎn)進(jìn)行移除,并將原異常點(diǎn)位置進(jìn)行鄰域插值后再進(jìn)行壓縮,同時(shí)把提取出來(lái)的異常點(diǎn)矢量進(jìn)行無(wú)損壓縮來(lái)提高壓縮算法對(duì)細(xì)節(jié)信息的保持能力。從實(shí)驗(yàn)結(jié)果中可以看出,此算法能更進(jìn)一步提高本文基于張量壓縮算法的壓縮性能。最后,在一般張量分解的基礎(chǔ)上引入分解時(shí)的非負(fù)約束,即利用非負(fù)張量分解,對(duì)高光譜遙感圖像進(jìn)行空間和光譜方向的分塊壓縮。通過(guò)實(shí)驗(yàn)結(jié)果可以看出,引入的非負(fù)分解約束更符合自然圖像的意義,能較好地提升分解效果,并且分塊壓縮能很好地降低整個(gè)算法的運(yùn)行復(fù)雜度,使本文算法對(duì)實(shí)際應(yīng)用更有意義。
[Abstract]:Hyperspectral remote sensing image imaging spectroscopy can obtain a lot of very narrow but continuous spectral image data in the ultraviolet, visible, near infrared and mid-infrared regions of the electromagnetic spectrum.Compared with the traditional remote sensing data, the spatial and spectral resolution of these hyperspectral data are greatly increased, which makes them more widely used in target detection and recognition, land type analysis, agriculture and environmental monitoring research.However, with the increase of resolution, the amount of data has increased dramatically, which makes the preservation and transmission of these data very inconvenient. Therefore, it is necessary to compress hyperspectral images effectively.The traditional hyperspectral remote sensing image compression algorithm is mainly divided into lossless compression and lossy compression.However, because of the harsh conditions such as high compression ratio and real time requirement for hyperspectral remote sensing image transmission, lossless compression is very difficult to meet the requirements, so it is urgent for researchers to find a high fidelity lossy compression method.Lossy compression is mainly based on discrete wavelet transform (DWT).Discrete wavelet transform (DWT) can provide high compression ratio and high fidelity, but its decorrelation performance is not the best.In addition to discrete wavelet, the best method of decorrelation in two dimensional case is based on principal component analysis.But the general principal component analysis only takes into account the two-dimensional situation, but for high-dimensional data, such as hyperspectral remote sensing images, it can not make good use of the overall information of the data.This paper presents a hyperspectral image compression algorithm based on Zhang Liang decomposition and wavelet packet transform.Firstly, the algorithm can fully extract the information of each mode in hyperspectral image by using the character of Zhang Liang decomposition, and decorrelate the spectral dimension of hyperspectral image with spectral mode containing spatial information.Then, the wavelet packet transform, which is more efficient than the classical Mallat wavelet decomposition, is used to compress the higher-order principal components of the spectrum which remain after de-correlation.According to the characteristics of the image compression algorithm based on JPEG2000 standard, we adopt the improved binary search method to reduce the time complexity of the proposed algorithm.The experimental results show that the compression performance of the proposed algorithm is much better than that of the classical algorithm based on 3D wavelet, and because of the application of Zhang Liang decomposition, whether in the performance of rate distortion or the fidelity of information,The proposed algorithm is superior to the hyperspectral image compression algorithm based on two-dimensional principal component analysis.As the applications of hyperspectral images are becoming more and more extensive, we have adopted another solution to the problem of maintaining outliers in hyperspectral images.In the process of compression, the outliers are removed by outlier detection algorithm, and the original outliers are interpolated by neighborhood interpolation, and then compressed.At the same time, the extracted outlier vectors are compressed losslessly to improve the ability of the compression algorithm to preserve the detail information.The experimental results show that this algorithm can further improve the compression performance based on Zhang Liang compression algorithm.Finally, based on the general Zhang Liang decomposition, the non-negative constraint is introduced, that is, the hyperspectral remote sensing image is compressed in space and spectral direction by using non-negative Zhang Liang decomposition.The experimental results show that the non-negative decomposition constraint is more suitable to the meaning of the natural image, and can improve the decomposition effect better, and the block compression can reduce the running complexity of the whole algorithm.The algorithm in this paper is more meaningful to practical application.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751

【共引文獻(xiàn)】

相關(guān)期刊論文 前2條

1 陳宏銘;王遠(yuǎn)大;程玉華;;基于結(jié)合小波變換與FastICA算法的腦電信號(hào)降噪(英文)[J];生物醫(yī)學(xué)工程學(xué)進(jìn)展;2014年03期

2 謝力;王忠;;基于小波包變換的圖像壓縮算法研究[J];通信與信息技術(shù);2015年03期

相關(guān)博士學(xué)位論文 前1條

1 Taha Mohammed Hasan;自適應(yīng)分形圖像壓縮[D];哈爾濱工業(yè)大學(xué);2013年

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本文編號(hào)：1755467