發(fā)布時(shí)間：2018-03-09 10:07

  本文選題：高光譜圖像　切入點(diǎn)：壓縮感知　出處：《西北工業(yè)大學(xué)》2015年博士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著高光譜圖像分辨率的不斷提高,成像光譜儀獲取的高光譜數(shù)據(jù)急劇增加,海量的高光譜數(shù)據(jù)給機(jī)載或星載遙感成像系統(tǒng)的數(shù)據(jù)存儲(chǔ)、傳輸和處理帶來(lái)巨大壓力。傳統(tǒng)的高光譜遙感成像面臨高速率采樣、海量數(shù)據(jù)的存儲(chǔ)和傳輸?shù)入y以突破的難題。壓縮感知理論作為一種新穎的數(shù)據(jù)采集理論將數(shù)據(jù)的采樣和壓縮過(guò)程巧妙地結(jié)合起來(lái),實(shí)現(xiàn)采樣率低于傳統(tǒng)奈奎斯特率的數(shù)據(jù)采集和少量觀測(cè)數(shù)據(jù)的精確重構(gòu),降低了對(duì)傳感器的要求,有效避免了追求高分辨率帶來(lái)的軟硬件成本問(wèn)題。壓縮感知理論的誕生為解決傳統(tǒng)高光譜遙感成像面臨的瓶頸問(wèn)題提供了有效的解決途徑,將壓縮感知技術(shù)應(yīng)用于高光譜遙感成像的研究已成為當(dāng)前高光譜數(shù)據(jù)采集研究的熱點(diǎn)。本文以高光譜數(shù)據(jù)的壓縮采樣模式和圖像的重建算法為研究對(duì)象,以高光譜圖像的線性混合模型為契機(jī),致力于尋求高效的高光譜壓縮采樣方式和快速、高精度的壓縮感知重建算法,并針對(duì)當(dāng)前成熟的推掃式和擺掃式高光譜數(shù)據(jù)采集模式研究了壓縮感知成像的實(shí)現(xiàn)方案。論文的主要工作及取得的創(chuàng)新性成果如下:1.針對(duì)高光譜圖像空間或光譜維壓縮采樣形式單一、采樣效率低的問(wèn)題,在分析研究空間壓縮采樣域數(shù)據(jù)特性的基礎(chǔ)上,提出了一種高光譜圖像的空譜壓縮采樣方案。高光譜數(shù)據(jù)采樣先進(jìn)行空間壓縮采樣,再對(duì)壓縮后的數(shù)據(jù)進(jìn)行光譜維壓縮采樣。實(shí)驗(yàn)結(jié)果表明,空譜壓縮采樣有助于提高采樣效率,改善重構(gòu)質(zhì)量。2.針對(duì)傳統(tǒng)高光譜壓縮感知直接重構(gòu)原始圖像數(shù)據(jù)量大的問(wèn)題,在光譜維壓縮采樣數(shù)據(jù)的重構(gòu)過(guò)程中應(yīng)用線性混合模型,提出了已知端元譜和未知端元譜的壓縮感知重構(gòu)算法,以及基于線性光譜庫(kù)混合模型的壓縮感知重構(gòu)算法。應(yīng)用線性混合模型將高光譜圖像分離成豐度系數(shù)和端元特征兩個(gè)小數(shù)據(jù)量的子集,利用混合像元分解算法估計(jì)豐度,稀疏優(yōu)化算法提取端元,再通過(guò)估計(jì)的豐度和提取的端元來(lái)合成原圖像。實(shí)驗(yàn)結(jié)果表明所提的重構(gòu)算法由于估計(jì)的兩子集數(shù)據(jù)量小,運(yùn)行速度上有較大幅度的提升,同時(shí)能獲得更高的峰值信噪比和更好的重建效果。3.針對(duì)高光譜圖像壓縮感知重構(gòu)精度低、計(jì)算復(fù)雜度高的問(wèn)題,提出了基于波段的分布式壓縮感知和基于像元的分布式壓縮感知方案。采樣時(shí)將高光譜圖像分成關(guān)鍵波段圖像與壓縮感知波段圖像或者關(guān)鍵像元與壓縮感知像元,并對(duì)不同類(lèi)別數(shù)據(jù)采用不同的采樣方式。重構(gòu)時(shí)利用線性混合模型分離不同類(lèi)別的觀測(cè)數(shù)據(jù),進(jìn)行端元提取和豐度估計(jì),線性譜解混算法的應(yīng)用使得欠定問(wèn)題的優(yōu)化轉(zhuǎn)化成超定方程的求解,大幅提高了重構(gòu)算法的運(yùn)行速度和精度。在基于波段的分布式壓縮采樣基礎(chǔ)上,提出基于迭代預(yù)測(cè)的分布式壓縮感知重構(gòu)算法,進(jìn)一步提高了高光譜壓縮感知的重構(gòu)精度。多個(gè)高光譜數(shù)據(jù)集的實(shí)驗(yàn)結(jié)果表明,所提的兩種分布式壓縮感知方案重構(gòu)的平均信噪比遠(yuǎn)遠(yuǎn)高于壓縮投影主成分分析算法和三維壓縮采樣算法,且重構(gòu)速度比三維壓縮采樣算法有數(shù)量級(jí)的提升。4.針對(duì)高光譜圖像壓縮測(cè)量矩陣難以光學(xué)實(shí)現(xiàn)的問(wèn)題,構(gòu)造了實(shí)現(xiàn)空譜聯(lián)合壓縮采樣的測(cè)量矩陣,提出了高光譜圖像的空譜聯(lián)合壓縮感知方案。通過(guò)分析空間壓縮采樣的豐度系數(shù)的數(shù)據(jù)特性,構(gòu)造了便于光學(xué)實(shí)現(xiàn)的、具有特殊結(jié)構(gòu)的空間測(cè)量矩陣。重構(gòu)時(shí)采用線性混合模型通過(guò)提取端元和估計(jì)豐度恢復(fù)原圖像。實(shí)驗(yàn)結(jié)果表明,高光譜圖像采用空譜聯(lián)合的壓縮采樣,重構(gòu)時(shí)利用線性混合模型進(jìn)行分離重構(gòu),能實(shí)現(xiàn)快速、高精度的數(shù)據(jù)采集與重建的目標(biāo)。5.基于擺掃式和推掃式高光譜遙感數(shù)據(jù)采集模式,研究了壓縮感知成像的實(shí)現(xiàn)方案,設(shè)計(jì)了實(shí)現(xiàn)擺掃式基于像元的分布式壓縮采樣成像系統(tǒng)模型、推掃式光譜維和空譜聯(lián)合壓縮采樣成像系統(tǒng)模型。采用棱鏡、柱面透鏡、DMD等光電器件,能使采集的數(shù)據(jù)量大幅減少而不需要進(jìn)行其它壓縮處理,既節(jié)省了存儲(chǔ)空間和傳輸資源,又降低了數(shù)據(jù)壓縮算法帶來(lái)的功耗,有利于機(jī)載或星載高光譜成像的實(shí)現(xiàn)。仿真所設(shè)計(jì)成像系統(tǒng)模型壓縮觀測(cè)數(shù)據(jù),采用本文所設(shè)計(jì)的重構(gòu)算法進(jìn)行重構(gòu),雖然重構(gòu)性能稍有下降,但仍能高精度的恢復(fù)原數(shù)據(jù)。
[Abstract]:With the continuous improvement of image resolution hyperspectral imaging spectrometer, hyperspectral data acquisition has increased dramatically, the hyperspectral data for massive airborne or satellite remote sensing imaging system data storage, transmission and processing of huge pressure. The traditional hyperspectral remote sensing imaging at high sampling rate, the problem of massive data storage and transmission is difficult to break the compressed sensing theory. As a kind of novel data acquisition theory data sampling and compression process combined skillfully, achieve the sampling rate is lower than the accurate reconstruction of data acquisition and a small amount of observation data of the traditional Nyquist rate, reduce the requirement of sensor, effectively avoid the pursuit of cost of software and hardware problems brought by high resolution. Compressed sensing theory has provided the effective way to solve the bottleneck problem of traditional hyperspectral remote sensing imaging, the compressed sensing technology Study on technique applied to hyperspectral remote sensing imaging has become a hotspot of current research. The hyperspectral data acquisition based on compression sampling mode and image reconstruction algorithm for hyperspectral data as the research object, using linear mixed models of hyperspectral image as an opportunity to seek efficient compression of hyperspectral sampling and fast, compressed sensing the reconstruction algorithm with high precision, and in view of the current mature pushbroom scanning and high spectral data acquisition mode of implementation of compressed sensing imaging. The main work and innovative results are as follows: 1. for hyperspectral image space or spectral compression sampling in single form, the problem of low efficiency of sampling. Based on the analysis of spatial domain data compression sampling characteristics, presents a high spectral image space spectral compression sampling scheme. The hyperspectral data sampling to space compression Sampling of compressed data in spectral compression sampling. The experimental results show that the spectrum of air compressed sampling is helpful to improve the sampling efficiency, improve the quality of reconstructed.2. in traditional hyperspectral compressed sensing to reconstruct the original image directly to large amount of data compression using linear mixed model reconstruction process of sampling data in the spectral dimension put forward, known and unknown endmember spectral element end of compressed sensing reconstruction algorithm in spectrum, and compressed sensing reconstruction algorithm of linear spectral library based on the hybrid model. The separation of application of linear mixed model of hyperspectral image into subsets abundance coefficient and endmember features two small amount of data, using mixed pixel decomposition algorithm to estimate the abundance. The optimization algorithm of endmember extraction by sparse, endmember abundance and extraction estimation to the synthesis of the original image. Experimental results show that the proposed reconstruction algorithm due to estimation of the two data sets Small, greatly enhance the speed, also can get higher PSNR and better reconstruction effect of.3. for hyperspectral image compressed sensing reconstruction precision is low, the computational complexity of the problem, put forward the distributed compressed sensing band and pixel distributed compressed sensing scheme based on the sampling. Hyperspectral image is divided into key band image and compressed sensing image band or key pixel and compressed sensing pixels, and using different sampling methods for different categories of data. Data reconstruction using linear mixed model from different categories, endmember extraction and abundance estimation, linear spectral unmixing algorithm makes the optimal solution using the underdetermined problem into solving over determined equations, greatly improve the reconstruction speed and accuracy. Based on the band distributed compressed sampling based on the proposed base In the iterative prediction of distributed compressed sensing reconstruction algorithm, further improve the reconstruction accuracy of hyperspectral compressed sensing. A plurality of hyperspectral data sets. The experimental results show that the average signal reconstruction of two kinds of distributed compressed sensing scheme of the proposed noise ratio is much higher than the compression projection principal component analysis algorithm and three-dimensional reconstruction and compression sampling algorithm. Faster than the 3D compressive sampling algorithm of magnitude increase.4. for hyperspectral image compression measurement matrix is difficult to optical implementation problems, constructed to realize spatial spectral joint compression sampling measurement matrix, the hyperspectral image compressed sensing scheme combined with spatial spectral data characteristics. Through the analysis of space compression abundance coefficient sampling, construct for optical implementation, space measurement matrix with special structure. The reconstruction using a linear mixed model by extracting and recovering the original image endmember abundance estimation Like. The experimental results show that the hyperspectral image compression using spatial spectral joint sampling, reconstruction using linear mixed model for separation of reconstruction, to achieve rapid,.5. data acquisition and reconstruction of high precision sweeping and pushbroom hyperspectral remote sensing data acquisition mode based on the research, the realization scheme of compressed sensing imaging that is designed to achieve the scanning type pixel distributed compressed sampling imaging system based on the model of pushbroom spectral and spatial spectral compression joint sampling imaging system model. Using the cylindrical lens, prism, DMD and other optoelectronic devices, can make the collection of data without the need for a substantial reduction in the amount of other compression processing, can save storage space and transmission resources, and reduce the power data compression algorithm, is beneficial to the realization of airborne or spaceborne hyperspectral imaging. The imaging system design model using the data compression. The reconstructed algorithm is reconstructed, although the reconfiguration performance is slightly reduced, it can still restore the original data with high precision.

【學(xué)位授予單位】：西北工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TP751

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5 王U喺，

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