本文選題：高光譜圖像無損壓縮 + 邊緣檢測(cè)��； 參考：《西安電子科技大學(xué)》2014年碩士論文

【摘要】：高光譜圖像是一種空間和譜間分辨率都很高的三維立體遙感圖像。隨著成像光譜技術(shù)的發(fā)展，高光譜圖像的數(shù)據(jù)量急劇增加，給高光譜數(shù)據(jù)的傳輸和存儲(chǔ)帶來了巨大的壓力，嚴(yán)重制約了高光譜遙感的發(fā)展。因此研究高光譜圖像壓縮算法對(duì)高光譜圖像的廣泛應(yīng)用有著重大的意義。 本文首先介紹了高光譜圖像的特點(diǎn)及應(yīng)用，，分析了高光譜圖像無損壓縮技術(shù)的研究現(xiàn)狀。然后描述了無損壓縮的基本理論，特別說明了無損預(yù)測(cè)壓縮的基本框架及現(xiàn)有技術(shù)。最后在現(xiàn)有的高光譜圖像壓縮算法基礎(chǔ)上，主要做了兩方面的工作。一方面是提出了基于邊緣檢測(cè)的改進(jìn)SLSQ高光譜圖像無損壓縮方法。與SLSQ相比，該方法在已有的譜內(nèi)預(yù)測(cè)模式和譜間預(yù)測(cè)模式上增加了無預(yù)測(cè)模式，在譜內(nèi)中值預(yù)測(cè)的基礎(chǔ)上增加了對(duì)斜邊緣的檢測(cè)，在譜間預(yù)測(cè)中提出了自適應(yīng)邊緣預(yù)測(cè)。測(cè)試結(jié)果表明，對(duì)AVIRIS1997高光譜圖像和CCSDS測(cè)試圖像，該方法得到的壓縮比比SLSQ分別提高了0.04和0.07。另一方面是對(duì)CCSDS多/高光譜圖像無損壓縮標(biāo)準(zhǔn)123.0-B-1（簡(jiǎn)稱為MHDC標(biāo)準(zhǔn)）進(jìn)行了性能分析。該標(biāo)準(zhǔn)中共包括12個(gè)用戶自定義參數(shù)，通過對(duì)這些參數(shù)的測(cè)試及分析得到了一組優(yōu)化參數(shù)，最后對(duì)標(biāo)準(zhǔn)性能進(jìn)行了評(píng)測(cè)。測(cè)試結(jié)果表明，優(yōu)化參數(shù)下的壓縮性能明顯優(yōu)于參考參數(shù)，MHDC標(biāo)準(zhǔn)對(duì)AVIRIS1997高光譜圖像和CCSDS測(cè)試圖像性能居中。
[Abstract]:Hyperspectral image is a three-dimensional remote sensing image with high spatial and interspectral resolution. With the development of imaging spectrum technology, the data volume of hyperspectral images increases dramatically, which brings great pressure to the transmission and storage of hyperspectral data, which seriously restricts the development of hyperspectral remote sensing. Therefore, the study of hyperspectral image compression algorithm is of great significance to the wide application of hyperspectral image. In this paper, the characteristics and applications of hyperspectral image are introduced, and the research status of lossless compression of hyperspectral image is analyzed. Then the basic theory of lossless compression is described, especially the basic frame of lossless prediction compression and the existing techniques. Finally, based on the existing hyperspectral image compression algorithm, two aspects of work are mainly done. On the one hand, an improved SLSQ hyperspectral image lossless compression method based on edge detection is proposed. Compared with SLSQ, this method adds no prediction model to the existing prediction model in spectrum and interspectral prediction model. On the basis of median prediction in spectrum, the detection of oblique edge is added, and adaptive edge prediction is proposed in inter-spectral prediction. The results show that the compression ratio of AVIRIS1997 hyperspectral image and CCSDS image is increased by 0.04 and 0.07 than SLSQ, respectively. On the other hand, the performance of CCSDS multispectral / hyperspectral image lossless compression standard 123.0-B-1 (referred to as MHDC standard) is analyzed. The standard includes 12 user-defined parameters. A set of optimized parameters is obtained by testing and analyzing these parameters. Finally, the standard performance is evaluated. The test results show that the compression performance of the optimized parameters is better than that of the reference parameter MHDC standard for AVIRIS1997 hyperspectral images and CCSDS test images.
【學(xué)位授予單位】：西安電子科技大學(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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本文編號(hào)：2116536