發(fā)布時(shí)間：2018-06-15 10:54

  本文選題：高光譜圖像 + KLT變換　； 參考：《成都理工大學(xué)》2016年碩士論文

【摘要】：高光譜遙感圖像具有光譜分辨率高、譜段窄等特點(diǎn),在為我們提供更為豐富的地物信的同時(shí),它的數(shù)據(jù)量也變得十分龐大,為其傳輸或存儲(chǔ)都帶來(lái)了不便,限制了它在實(shí)際中的應(yīng)用,所以研究高效的壓縮方法是十分有必要的。本文對(duì)高光譜圖像的壓縮技術(shù)作了研究,分析了高光譜圖像的兩種冗余,本文采用KLT變換去除譜間冗余,小波變換去除空間冗余,對(duì)變換后的系數(shù)采用兩種SPHIT編碼方案:一是使用二維SPHIT算法對(duì)各個(gè)波段進(jìn)行編碼;二是采用3D-SPHIT算法對(duì)整體進(jìn)行編碼;在二維SPHIT編碼中,采用了基于KLT變換特征向量的非均勻碼率分配方法,實(shí)驗(yàn)結(jié)果表明,該方法獲得的PSNR要比均勻分配碼率的方法高出約3db。在3D-SPHIT編碼中,分析了KLT變換結(jié)合小波變換與直接采用三維非對(duì)稱(chēng)小波變換對(duì)高光譜圖像冗余去除的性能,結(jié)果表明,總的來(lái)說(shuō),采用KLT變換結(jié)合小波變換的方案獲得的SNR要高于采用三維小波變換的方案,在低碼率時(shí),兩者相差不大,但隨著碼率的增加,采用KLT變換結(jié)合小波變換的方案可以獲得更好的SNR。
[Abstract]:Hyperspectral remote sensing images have the characteristics of high spectral resolution and narrow spectral bands. While providing us with more abundant information of ground objects, the amount of data in hyperspectral remote sensing images has become very large, which brings inconvenience to its transmission or storage. It limits its application in practice, so it is necessary to study the efficient compression method. In this paper, the compression technology of hyperspectral image is studied, and two kinds of redundancy of hyperspectral image are analyzed. In this paper, the inter-spectral redundancy is removed by KLT transform, and the spatial redundancy is removed by wavelet transform. Two kinds of SPHIT coding schemes are used for the transformed coefficients: one is to encode each band by using two-dimensional SPHIT algorithm, the other is to code the whole by using 3D-SPHIT algorithm. The non-uniform bit rate allocation method based on KLT transform eigenvector is used. The experimental results show that the PSNR obtained by this method is about 3 db. higher than that of the uniform allocation method. In the 3D-SPHIT coding, the performance of combining KLT transform with wavelet transform and directly using 3D asymmetric wavelet transform to remove redundancy of hyperspectral image is analyzed. The results show that, The SNR obtained by using KLT combined with wavelet transform is higher than that with 3D wavelet transform. At low bit rate, there is no difference between the two schemes, but with the increase of code rate, a better SNR can be obtained by using KLT combined with wavelet transform.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP751

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 孫蕾;谷德峰;羅建書(shū);;最佳遞歸雙向預(yù)測(cè)的高光譜圖像無(wú)損壓縮[J];光學(xué)精密工程;2009年11期

2 王朗;郭樹(shù)旭;;基于雙向波段預(yù)測(cè)的超光譜圖像無(wú)損壓縮[J];吉林大學(xué)學(xué)報(bào)(信息科學(xué)版);2009年03期

3 孫蕾;羅建書(shū);谷德峰;;基于譜間預(yù)測(cè)和碼流預(yù)分配的高光譜圖像壓縮算法[J];光學(xué)精密工程;2008年04期

4 孫蕾;羅建書(shū);;基于多波段譜間預(yù)測(cè)的高光譜圖像無(wú)損壓縮算法[J];電子與信息學(xué)報(bào);2007年12期

5 張曉玲 ,沈蘭蓀;高光譜圖像的無(wú)損壓縮研究進(jìn)展[J];測(cè)控技術(shù);2004年05期

6 楊宗凱;小波去噪及其在信號(hào)檢測(cè)中的應(yīng)用[J];華中理工大學(xué)學(xué)報(bào);1997年02期

，

本文編號(hào)：2021767