无人机载光谱仪超光谱图像压缩技术研究
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摘要
超光谱遥感是自二十世纪八十年代起迅速发展起来的一种遥感技术,它可以在可见光、近红外、中红外和热红外区域获取许多波段非常窄的光谱连续的图像数据[1],能够分辨出多光谱遥感无法探测到的地表物质。无人机载遥感系统的运行成本低、实时性高、执行任务灵活性高,具有高分辨率遥感影像数据获取能力,获取的超光谱遥感数据在地质勘测、农业、植被遥感、海洋遥感、环境监测等方面都发挥着重要作用。但是超光谱图像庞大的数据量却使得存储、传输和处理的效率大大降低,因此研究适合硬件实现的、低复杂度、高性能的超光谱图像压缩技术具有重要的应用价值。本文以863项目为背景,围绕超光谱图像的特性,以超光谱图像压缩应用为主题展开论述,主要的研究内容和成果包括以下几个方面:
(1)超光谱图像的数据相关性分析。
对超光谱图像和普通图像进行了相关性对比实验,结果表明超光谱图像各个谱段的空间相关性基本一致,所以在去除空间冗余时可以对各波段采用相同的方法;空间相关系数普遍较低,因此如果仅对超光谱图像去除空间冗余,难以取得理想的压缩效果;超光谱图像的谱间相关性要远远大于空间相关性,这种相关性随着光谱分辨率的提高越来越强,因此多谱段图像的信息冗余度极高,压缩时的首要任务就是去除谱间相关。
(2)基于位平面的编码方案。
对超光谱图像进行了位平面分析,结果表明高位的谱间冗余度要大于低位,因此压缩时应该对高、低位平面采取不同的去相关措施。针对不同的应用需求,文中提出了基于位平面的无损和近无损压缩模式。
无损模式中采用DPCM去除谱间相关,针对高位的残差图像设计了静态编码方案,并在低位压缩时引入了四叉树划分技术,同谱间位平面和三维位平面算法相比,本文算法的的编码时间分别降低了13.1%和6.0%,解码时间降低了8.2%和2.8%,而压缩比则有12.7%和6.3%的提高。
近无损模式中对高位采用改进的JPEG-LS方法压缩,压缩比达到1:8左右的时候,重建图像的PSNR大约为35。在压缩比控制方面提出了有效的方案,预设压缩比和实际压缩比的平均误差约为3.7%。低位压缩时应用四叉树划分后统一去均值并均匀量化,得到的均值图像轮廓与原图像接近。
针对低位压缩比不高的情况,又提出了三角划分方法,压缩解压时间比四叉树划分分别降低了16.54%和10.58%,重建图像相似度约有0.07%的提高,压缩效果比较理想。
(3)二阶差分预测编码方案
分析了常见预测压缩算法,针对低复杂度高压缩比的目标提出了超光谱图像的二阶差分预测压缩方案,算法在空间和谱间分别去相关,最后把结果融合得到统一的预测式。同二阶线性最优预测相比,二阶差分预测的残差图像熵值大约降低了3%,编解码时间降低了5%和16%。同谱间LOCO-I算法相比,残差图像熵值大约降低了4%。
Hyper-spectral remote sensing developed rapidly since the 80's of the 20th century. It can get the image data of many bands with continuous spectrum and narrow spectral regions in visible and near infrared, short waveinfrared and thermal infrared range. With hyper-spectral image, many object that can’t be recognized in multispectral image can be detectd .The unmanned aerial vehical remote sensing system can get high resolution image in real time with lower cost. So it is widely used in military reconnaissance, resource investigation, agriculture, meteorology and environmental assessment. But the increased data volume of hyper-spectral image present special challenges in the acquisition, transmission, analysis and storage process. So it has applicable value to design a compression scheme that suitable for realization in VLSI, with low complexity and good performance. In this paper, with the background of the 863 project , compression of hyper-spectral image is researched, the main contents and results are:
1. The analysis of the correlation in hyperspectral remote sensing images.
Contrast Experiment on correlation of hyper-spectral image and ordinary image is tesred at first.The results show the correlation is almost equal among different bands,so we can use the same method to get rid of the correlation for every band; The spatial correlation coefficient of hyperspectral remote sensing images is lower than ordinary image ,so spatial decorrelate can’t get ideal compression effect ;The spectral correlation of hyperspectral remote sensing images is much larger than the ordinary image’s, the higer of Spectral Resolution,the stronger of spectral correlation. So the most important task in compression is spectral decorrelation.
2. The coding scheme based on bit-plane.
The results of bit-plane analysis shows the spectral correlation is larger in higher bit-plane.To improve the comprssion effect; we can use different measures for higher and lower bit-plane.Two schemes are propsed in this paper.
In lossless scheme, DPCM was used in spectral decorrelation of higher bit-plane, then an static code strategy was applied to residual image.Lower bit-planeis are divided with quadtree. Compared with the inter-spectral bit-plane and 3-D bit-plane algorithm, the encode time is decreased by 13.1% and 6.0%, the decode time is decreased by 12.7% and 6.3%.
In lossy scheme, improved JPEG-LS was used in spectral decorrelation of higher bit-plane, the PSNR reached up to 35 DB when the compression ratio is about 1:8.An effective strategy is designed in comprssion ratio control, the error is abobt 3.7% between expectation ratio and actual ratio. Lower bit-planeis is removing mean after divided with quadtree.
In view of the poor compression ratio of lower bit-plane, a triangle division method is proposed. By this method, the encode and decode time is decreased by 16.54% and 10.58% respectively, similarity of reconstructed image is increased bu 0.08%.
3. The coding scheme based on second order different predictive.
After analyse the performance of common predictive coding scheme,a novel compression scheme that based on second order difference predictive was proposed, It use MED predictor to remove the spatial correlation and second order difference predictor to remove spectral correlation. Then a Unified predictor is designed based on the weight of predictive error. Compared with the optimal linear prediction, the entropy of residual image is decreased by 3%, the encode and decode time are decreased by 5% and 16%. Compared with the inter-spectral LOCO-I, the entropy of residual image is decreased by 4%.
(1)超光谱图像的数据相关性分析。
对超光谱图像和普通图像进行了相关性对比实验,结果表明超光谱图像各个谱段的空间相关性基本一致,所以在去除空间冗余时可以对各波段采用相同的方法;空间相关系数普遍较低,因此如果仅对超光谱图像去除空间冗余,难以取得理想的压缩效果;超光谱图像的谱间相关性要远远大于空间相关性,这种相关性随着光谱分辨率的提高越来越强,因此多谱段图像的信息冗余度极高,压缩时的首要任务就是去除谱间相关。
(2)基于位平面的编码方案。
对超光谱图像进行了位平面分析,结果表明高位的谱间冗余度要大于低位,因此压缩时应该对高、低位平面采取不同的去相关措施。针对不同的应用需求,文中提出了基于位平面的无损和近无损压缩模式。
无损模式中采用DPCM去除谱间相关,针对高位的残差图像设计了静态编码方案,并在低位压缩时引入了四叉树划分技术,同谱间位平面和三维位平面算法相比,本文算法的的编码时间分别降低了13.1%和6.0%,解码时间降低了8.2%和2.8%,而压缩比则有12.7%和6.3%的提高。
近无损模式中对高位采用改进的JPEG-LS方法压缩,压缩比达到1:8左右的时候,重建图像的PSNR大约为35。在压缩比控制方面提出了有效的方案,预设压缩比和实际压缩比的平均误差约为3.7%。低位压缩时应用四叉树划分后统一去均值并均匀量化,得到的均值图像轮廓与原图像接近。
针对低位压缩比不高的情况,又提出了三角划分方法,压缩解压时间比四叉树划分分别降低了16.54%和10.58%,重建图像相似度约有0.07%的提高,压缩效果比较理想。
(3)二阶差分预测编码方案
分析了常见预测压缩算法,针对低复杂度高压缩比的目标提出了超光谱图像的二阶差分预测压缩方案,算法在空间和谱间分别去相关,最后把结果融合得到统一的预测式。同二阶线性最优预测相比,二阶差分预测的残差图像熵值大约降低了3%,编解码时间降低了5%和16%。同谱间LOCO-I算法相比,残差图像熵值大约降低了4%。
Hyper-spectral remote sensing developed rapidly since the 80's of the 20th century. It can get the image data of many bands with continuous spectrum and narrow spectral regions in visible and near infrared, short waveinfrared and thermal infrared range. With hyper-spectral image, many object that can’t be recognized in multispectral image can be detectd .The unmanned aerial vehical remote sensing system can get high resolution image in real time with lower cost. So it is widely used in military reconnaissance, resource investigation, agriculture, meteorology and environmental assessment. But the increased data volume of hyper-spectral image present special challenges in the acquisition, transmission, analysis and storage process. So it has applicable value to design a compression scheme that suitable for realization in VLSI, with low complexity and good performance. In this paper, with the background of the 863 project , compression of hyper-spectral image is researched, the main contents and results are:
1. The analysis of the correlation in hyperspectral remote sensing images.
Contrast Experiment on correlation of hyper-spectral image and ordinary image is tesred at first.The results show the correlation is almost equal among different bands,so we can use the same method to get rid of the correlation for every band; The spatial correlation coefficient of hyperspectral remote sensing images is lower than ordinary image ,so spatial decorrelate can’t get ideal compression effect ;The spectral correlation of hyperspectral remote sensing images is much larger than the ordinary image’s, the higer of Spectral Resolution,the stronger of spectral correlation. So the most important task in compression is spectral decorrelation.
2. The coding scheme based on bit-plane.
The results of bit-plane analysis shows the spectral correlation is larger in higher bit-plane.To improve the comprssion effect; we can use different measures for higher and lower bit-plane.Two schemes are propsed in this paper.
In lossless scheme, DPCM was used in spectral decorrelation of higher bit-plane, then an static code strategy was applied to residual image.Lower bit-planeis are divided with quadtree. Compared with the inter-spectral bit-plane and 3-D bit-plane algorithm, the encode time is decreased by 13.1% and 6.0%, the decode time is decreased by 12.7% and 6.3%.
In lossy scheme, improved JPEG-LS was used in spectral decorrelation of higher bit-plane, the PSNR reached up to 35 DB when the compression ratio is about 1:8.An effective strategy is designed in comprssion ratio control, the error is abobt 3.7% between expectation ratio and actual ratio. Lower bit-planeis is removing mean after divided with quadtree.
In view of the poor compression ratio of lower bit-plane, a triangle division method is proposed. By this method, the encode and decode time is decreased by 16.54% and 10.58% respectively, similarity of reconstructed image is increased bu 0.08%.
3. The coding scheme based on second order different predictive.
After analyse the performance of common predictive coding scheme,a novel compression scheme that based on second order difference predictive was proposed, It use MED predictor to remove the spatial correlation and second order difference predictor to remove spectral correlation. Then a Unified predictor is designed based on the weight of predictive error. Compared with the optimal linear prediction, the entropy of residual image is decreased by 3%, the encode and decode time are decreased by 5% and 16%. Compared with the inter-spectral LOCO-I, the entropy of residual image is decreased by 4%.
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