基于改进预测树的超光谱遥感图像无损压缩方法

夏豪; 张荣

doi:10.3724/SP.J.1146.2007.01933

基于改进预测树的超光谱遥感图像无损压缩方法

doi: 10.3724/SP.J.1146.2007.01933

夏豪,
张荣

计量
- 文章访问数: 3063
- HTML全文浏览量: 78
- PDF下载量: 731
- 被引次数: 0
出版历程
- 收稿日期: 2007-12-20
- 修回日期: 2008-06-17
- 刊出日期: 2009-04-19

The Lossless Compression Method for Hyperspectral Images Based on Optimized Prediction Tree

Xia Hao,
Zhang Rong

摘要

摘要: 该文在传统预测树方法的基础上提出一种改进方法，该方法定义一个幅度拉伸因子来表达相邻波段的局部灰度变化，通过比较局部上下文梯度来估算该幅度因子，并用它对当前的预测值进行修正。此外，还结合AVIRIS超光谱遥感图像的相关性特性提出一种谱间预测和空间预测相结合的综合预测无损压缩方案，在不同波段范围内采用可选的预测方式进行预测。在AVIRIS遥感图像数据集上的实验结果表明，该方案在计算复杂度较低的情况下，能够更好地消除冗余信息，具有良好的压缩性能。
- 遥感;超光谱;无损压缩;预测树
Abstract: Prediction tree is a traditional and efficient method for lossless compression of hyperspectral image. In this paper an optimized method based on prediction tree is presented. To express the variation of local context of two neighboring bands, a partial extending factor is introduced to compensate the predicted value of current pixel so as to reduce the prediction error. Furthermore, a synthetical prediction based lossless compression scheme for AVIRIS hyperspectral images is proposed. Experimental results demonstrate that the proposed method works efficiently on AVIRIS images with low complexity and limited memory.

HTML全文

参考文献(1)

张晓玲, 沈兰荪. 高光谱图像的无损压缩研究进展[J]. 测控技术, 2004, 23(5): 23-27.Zhang Xiao-ling and Shen Lan-sun. Research advances onlossless compression of hyperspectral image [J]. Measurement Control Technology, 2004, 23(5): 23-27.[2]Zhang Jing and Liu Guizhong. An efficient reorderingprediction-based lossless compression algorithm forhyperspectral images [J].IEEE Geosci. Remote SensLetters.2007, 4(2):283-287[3]Slyz M and Zhang L. A block-based inter-band losslesshyperspectral image compressor [C]. Proc. DCC 2005, Utah,US, 2005: 427-436.[4]Mielikainen J and Toivanen P. Clustered DPCM for thelossless compression of hyperspectral images [J].IEEE Trans.on Geosci. Remote Sens.2003, 41(12):2943-2946[5]Rizzo F, Carpentieri B, and Motta G, et al.. Low-complexitylossless compression of hyperspectral imagery via linearprediction [J].IEEE Signal Process. Lett.2005, 12(2):138-141[6]Wang H, Babacan S D, and Sayood K. Lossless hyperspectralimage compression using context-based conditional averages[C]. Proc. DCC, Snowbird, Utah, US, 2005: 418-426.[7]Jain S K and Adjeroh D A. Edge-based prediction for losslesscompression of hyperspectral images[C]. DCC2007, Utah, US,2007: 153-162.[8]Mielikainen J. Lossless compression of hyperspectral imagesusing lookup tables[J]. IEEE Signal Processing Letter, 2006,3(3): 157-160.[9]Aiazzi B, Alparone L, and Baronti S. Crisp and fuzzyadaptive spectral predictions for lossless and near-losslesscompression of hyperspectral imagery [J].IEEE Gersci. Remote Sens. Letter.2007, 4(4):532-536[10]Memon N D, Sayood K, and Magliveras S. Losslesscompression of multispectral image data [J].IEEE Trans. onGeosci. Remote Sensing.1994, 32(2):282-289[11]Wu X and Memon N. Context-based lossless interbandcompression-extending CALIC [J]. IEEE Trans. on ImageProcess, 2000, 9(6): 994-1001.[12]张荣, 阎青, 刘政凯. 一种基于预测树的多光谱遥感图像无损压缩方法[J]. 遥感学报, 1998, 2(3): 171-175.Zhang Rong, Yan Qing, and Liu Zheng-kai. A predictiontree-based lossless compression technique of multispectralimage data[J]. Journal of Remote Sensing, 1998, 2(3): 171-175.[13]吴铮, 何明一, 冯燕, 等. 基于误差补偿预测树的多光谱遥感图像无损压缩方法[J]. 遥感学报, 2005, 9(2): 143-147.Wu Zheng, He Ming-yi, and Feng Yan, et al.. Losslesscompression of multispectral imagery by error compensatedprediction tree [J]. Journal of Remote Sensing, 2005, 9(2):143-147.

施引文献

资源附件(0)

访问统计