Convolution Neural Network based lossy compression of hyperspectral images

The large size of hyperspectral imaging poses a significant threat to its potential use in real life due to the abundant information stored in it. The use of deep learning for such data processing is visible in recent applications. In this work, we propose a lossy hyperspectral image compression algorithm based on the concept of autoencoders. It uses a combination of the convolution layer and max-pooling layer to reduce the dimensions of the input image and generate a compressed image. The original image with some loss of information is reconstructed using transpose convolution layer that uses reverse of the procedure used by the encoder. The compressed image has been entropy coded using an adaptive arithmetic coder for transmission or storage application. The method provides an improvement of 28% in PSNR with 21 times increment in the compression ratio. The effect of compression on classification has also been evaluated in the experiment using state of art classification algorithm. Negligible difference in classification accuracy was obtained that proves the effectiveness of the proposed algorithm.     

基于空谱特性的高光谱图像压缩感知重构

针对现有的高光谱图像压缩感知重构算法对图像的空谱特性利用不够充分,导致重构图像质量不够高的问题,提出了一种高光谱图像变投影率分块压缩感知结合优化谱间预测重构方案。编码端以频段聚类方式将高光谱图像的所有频段分成参考频段和普通频段,对不同频段单独采用不同精度分块压缩感知以获取高光谱数据。在解码端,参考频段直接采用稀疏度自适应匹配追踪( SAMP)算法重构,对于普通频段,则设计了一种优化谱间预测结合SAMP算法的新模型进行重构：首先通过重构的参考频段双向预测普通频段,并对其进行压缩投影,然后计算预测前后普通频段投影值的残差,最后利用SAMP算法重构该残差,以此修正预测值。实验表明,相比同类算法,该算法充分考虑了高光谱图像的空谱特性,有效改善了重构图像质量,且编码复杂度低,易于硬件实现。     

基于双估计值的查找表高光谱图像无损压缩

提出一种基于双估计值的查找表预测高光谱图像无损压缩算法。首先,在高光谱图像的第1谱段图像采用JPEG-LS中值预测器进行谱段内预测,其他谱段图像采用谱间预测。谱间预测采用以下步骤,利用3个LUT预测值求出第一个估计值;其次用当前谱段内和前一谱段内特定的8个像素点计算出第二个估计值,将谱段内预测和谱间预测有效地结合,去除了高光谱图像的谱间相关性。然后,用3个LUT预测值和最终的预测估计值比较,选出最终的预测值。最后,将预测残差进行算术编码。实验结果表明,针对NASA的AVIRIS高光谱图像,用本文算法比LAIS-LUT的压缩比平均提高了0.03~0.11,针对国内OIMS-I高光谱图像,比LAIS-LUT压缩比平均提高了0.01~0.09,有效的提高了压缩比。     

A wavelet-based two-stage near-lossless coder.

In this paper, we present a two-stage near-lossless compression scheme. It belongs to the class of "lossy plus residual coding" and consists of a wavelet-based lossy layer followed by arithmetic coding of the quantized residual to guarantee a given L(infinity) error bound in the pixel domain. We focus on the selection of the optimum bit rate for the lossy layer to achieve the minimum total bit rate. Unlike other similar lossy plus lossless approaches using a wavelet-based lossy layer, the proposed method does not require iteration of decoding and inverse discrete wavelet transform in succession to locate the optimum bit rate. We propose a simple method to estimate the optimal bit rate, with a theoretical justification based on the critical rate argument from the rate-distortion theory and the independence of the residual error.     

Scalable image coding using reversible integer wavelet transforms

Reversible integer wavelet transforms allow both lossless and lossy decoding using a single bitstream. We present a new fully scalable image coder and investigate the lossless and lossy performance of these transforms in the proposed coder. The lossless compression performance of the presented method is comparable to JPEG-LS. The lossy performance is quite competitive with other efficient lossy compression methods.     

基于压缩感知理论的波段重构方法

针对高光谱图像数据量大、信息冗余多、传输难度大等问题,从波段压缩采样入手,通过采样数据重构出原始波段,提出一种基于压缩感知理论的波段重构方法。压缩感知理论是一种在不遵循奈奎斯特采样定理的情况下,能够高精度重构出原始信号的新型压缩采样理论。由于高光谱图像谱间相关性高,具有很强的稀疏性,故可将压缩感知理论用于高光谱数据的波段重构,仅选择少量波段,便能够重构得到原始高光谱数据。实验结果表明,压缩感知理论能够对高光谱图像波段维进行压缩与重构,并可达到较高的重构比例,同时获得较高的重构效率,且重构数据光谱曲线与原始数据光谱曲线的波形一致度高。     

基于L∞最小搜索和陪集码的高光谱图像无损及近无损压缩

 分布式信源编码(DSC)由于其较低的编码复杂度及较高的抗误码性被应用于高光谱图像压缩.在典型的基于陪集码的分布式高光谱图像无损压缩算法s-DSC(scalar coset DSC)框架下,本文指出最优的预测准则应为无穷范数最小,提出了基于L_∞最小搜索的预测方法来逼近最优准则,并将框架推广到近无损压缩.实验表明,和原有的s-DSC相比,本文算法无损压缩的平均码率降低了大约0.25bpp,近无损性能也明显优于JPEG-LS,本文算法具有较低的计算复杂度、较高的压缩性能,且具有一定的抗误码能力,适用于星上压缩.     

Compression of colour images by inter-band compensated prediction

A new technique, called inter-band compensated prediction, for coding colour and multispectral images is presented. It is suitable to use for coding any spectral domain and can code colour and multispectral images with any number of bands. This technique is based on the same principles as the very efficient motion compensated prediction largely used in video coding. Thus, each band is predicted in the spectral direction by compensating the differences in the neighbouring bands and then coding the prediction error spatially by another method. This is a forward adaptive prediction and the information used for compensation is coded as side information with prediction error. The comparison of the coding results with the state-of-the-art coding algorithms, based on spectral transformations, proves that this technique is very efficient and can even outperform them. In addition, compensation can be combined with any spatial coder that allows lossless, lossy and scalable coding of any spectral content of the image. It has also the advantages of being simple to implement and to use with parallel architectures.     

基于自适应波段聚类主成分分析和反向传播神经网络的高光谱图像压缩

高光谱遥感图像具有丰富的光谱信息,数据量大。为了能够有效地利用高光谱图像数据,促进高光谱遥感技术的发展,该文提出一种基于自适应波段聚类主成分分析(PCA)与反向传播(BP)神经网络相结合的高光谱图像压缩算法。算法利用近邻传播(AP)聚类算法对波段进行自适应聚类,对聚类后的各个分组分别进行PCA运算,最后利用BP神经网络对所有主成分进行编码压缩。该文的创新点在于BP神经网络压缩图像时,在训练步骤过程中,误差反向传播是用原图与输出作差值,再反向调整各层的权值、阈值。对高光谱图像进行波段聚类,不仅能够有效地利用谱间相关性,提高压缩性能,还可以降低PCA的运算量。实验结果表明,该文算法与其它现有算法比较,在相同压缩比下,其光谱角更小,信噪比更高。     

基于预测与JPEG2000的高光谱图像无损压缩方法

随着成像光谱仪向着高光谱分辨率、高空间分辨率方向发展,高光谱图像的数据量呈几何级数增长。由于数据传输和存储能力的限制,必须对高光谱图像进行有效压缩。首先,对高光谱图像的相关性进行了深入分析,得知其具有一定的空间相关性和极强的谱间相关性,从而具有较强的可压缩性。其次,结合JPEG2000对DPCM进行了修改,提出了基于一阶线性预测与JPEG2000相结合的无损压缩方案。最后,在软件平台上实现了该方案,并取得了较好的压缩效果。结果表明,该方案可以有效的实现高光谱图像无损压缩,验证了方案的可行性,为硬件平台上实现该方案提供了理论依据。     

基于子空间中主成分最优线性预测的高光谱波段选择

针对高光谱遥感图像的异常检测问题,为了使高光谱降维数据能更完整地保留其光谱信息,提出了基于子空间中主成分最优线性预测的波段选择方法.采用改进相关性度量的谱聚类方法将高光谱波段划分为不同的子空间,并对各子空间中的波段进行主成分分析(PCA),选择主要分量作为重构目标;以子空间追踪法为搜索策略,从各子空间中选择数个波段对其重构目标进行联合最优线性预测;合并各子空间中的所选波段得到最佳波段子集.实验结果表明,该方法选择的波段子集可以较完整地重构原始数据,与原始数据以及自适应波段选择(ABS)方法、线性预测(LP)方法、最大方差主成分分析(MVPCA)方法、自相关矩阵波段选择(ACMBS)方法、组合因子最优波段选择(OCFBS)方法得到的波段子集相比,其波段子集具有更好的异常检测性能.     

基于残差偏置和查找表的高光谱图像无损压缩

为了提高高光谱遥感图像的压缩比,提出一种基于残差偏置和查找表的高光谱图像无损压缩方法。在高光谱图像的第一谱段图像采用了无损压缩标准中值预测器方法进行谱段内预测,其它谱段图像采用谱间预测方法。首先,在多级查找表（LAIS-LUT）预测方法的基础上搜索当前预测值,用当前预测值周围特定的5个像素点和当前像素值周围相同位置的5个像素点进行比较,通过比较结果,得出一个偏置值;然后在预测残差上加上偏置值;最后,将最终预测残差进行算术编码,并进行了理论分析和实验验证。结果表明,针对美国航空航天局的高光谱图像,所提出的方法比LAIS-LUT压缩比平均提高0.05;针对国内高光谱图像,该方法比LAIS-LUT压缩比平均提高0.07。这一结果对提高高光谱图像压缩效率是有帮助的。     

基于K-均值聚类和传统递归最小二乘法的高光谱图像无损压缩

针对基于预测的高光谱图像无损压缩算法压缩比低的问题,该文将聚类算法与高光谱图像预测压缩算法相结合,提出一种基于K-均值聚类和传统递归最小二乘法的高光谱图像无损压缩算法。首先,对高光谱图像按光谱矢量进行K-均值聚类以提升同类光谱矢量间的相似度。然后,对每一聚类群分别使用传统递归最小二乘法进行预测,消除高光谱图像的空间冗余和谱间冗余。最后,对预测误差图像进行算术编码,完成高光谱图像压缩过程。对AVIRIS 2006高光谱数据进行仿真实验,所提算法对16位校正图像、16位未校正图像和12位未校正图像分别取得了4.63倍,2.82倍和4.77倍的压缩比,优于同类型已报道的各种算法。     

基于压缩感知的高光谱遥感影像重构方法研究

高光谱遥感影像包含丰富的空间、辐射以及光谱信息,同时海量的数据也引发了高光谱成像技术在传输和存储方面的诸多问题。针对这一问题,根据高光谱遥感影像谱间相关性强的特性,提出了一种结合谱间多向预测的基于压缩感知的高光谱遥感影像重构方法。首先,根据高光谱遥感影像的谱间相关性对高光谱遥感影像的波段进行分组,每组确定一个参考波段,使用平滑l_0范数算法重构每组的参考波段。其次,根据重构恢复的相邻组内的参考波段,建立了一个非参考波段预测模型,用来计算非参考波段的预测测量值;然后,计算实际测量值与预测测量值的差值,使用SL0算法重构该差值得到差值向量;最后,利用得到的差值向量迭代更新预测测量值,直到恢复该波段原始图像。仿真实验结果表明,该方法提高了高光谱遥感影像的重构效果。     

Lossy compression of ultraspectral images: integrating preprocessing and compression stages

In this paper we focus on lossy compression of Atmospheric Infrared Sounder images that include around 40 MB of data distributed over more than two thousand bands. We present a novel architecture that integrates both preprocessing and compression stages providing efficient lossy compression. As part of preprocessing the spectral bands are normalized and reordered such that the bands of the transformed cube are spatially segmented and scanned to generate a unidimensional signal. This signal is then modeled as an autoregressive process and subjected to linear prediction (LP) for which a valid filter order is obtained by analyzing the prediction gain of the filter. The outcomes of this procedure are LP coefficients and an error signal that, when encoded, results in a compressed version of the original image. Performance of this novel architecture is mathematically justified by means of rate-distortion analysis and compared against other well-known compression techniques.     

MODIS多光谱图像压缩研究

为有效存储MODIS多光谱图像数据,该文提出一种基于谱间预测和整数小波变换的多光谱图像压缩算法.首先通过构造谱间最优预测器去除谱间冗余,再利用整数小波变换和SPIHT算法对预测误差图像去除空间冗余,最后进行自适应算术编码.该方法可实现MODIS多光谱图像的无损、近无损和有损压缩,取得了满意的实验结果;在不同小波基条件下与3D-SPIHT算法比较,表明了该方法的有效性.     

A compressive sensing and unmixing scheme for hyperspectral data processing

Hyperspectral data processing typically demands enormous computational resources in terms of storage, computation, and input/output throughputs, particularly when real-time processing is desired. In this paper, a proof-of-concept study is conducted on compressive sensing (CS) and unmixing for hyperspectral imaging. Specifically, we investigate a low-complexity scheme for hyperspectral data compression and reconstruction. In this scheme, compressed hyperspectral data are acquired directly by a device similar to the single-pixel camera based on the principle of CS. To decode the compressed data, we propose a numerical procedure to compute directly the unmixed abundance fractions of given endmembers, completely bypassing high-complexity tasks involving the hyperspectral data cube itself. The reconstruction model is to minimize the total variation of the abundance fractions subject to a preprocessed fidelity equation with a significantly reduced size and other side constraints. An augmented Lagrangian-type algorithm is developed to solve this model. We conduct extensive numerical experiments to demonstrate the feasibility and efficiency of the proposed approach, using both synthetic data and hardware-measured data. Experimental and computational evidences obtained from this paper indicate that the proposed scheme has a high potential in real-world applications.     

高光谱图像压缩技术研究进展

高光谱遥感已经成为遥感领域的前沿科技,在军事侦察以及国民经济中发挥着重要作用。高光谱遥感的光谱通道数达到上百个,光谱分辨率的不断提高使得高光谱图像的数据量急剧膨胀。对于星载成像光谱仪获取的高光谱图像,庞大的数据量已经给数据的存储与传输带来巨大压力,严重制约着高光谱图像的后续应用,因此,必须利用有效的压缩技术对高光谱图像进行压缩。高光谱图像压缩技术可分为无损压缩与有损压缩,在实际应用中,需要根据具体的应用需求选取不同的压缩方式。本文首先对高光谱遥感的基本概念进行了简介,然后从无损压缩与有损压缩两个方面对高光谱图像压缩技术的研究进展进行了综述,最后,指出了高光谱图像压缩技术的发展方向。      

Practical transform coding of multispectral imagery

We present an implementable three dimensional terrain adaptive transform based bandwidth compression technique for multispectral imagery. The algorithm exploits the inherent spectral and spatial correlations in the data. The compression technique is based on Karhunen-Loeve transformation for spectral decorrelation followed by the standard JPEG algorithm for coding the resulting spectrally decorrelated eigen images. The algorithm is conveniently parameterized to accommodate reconstructed image fidelities ranging from near-lossless at about 5:1 CR to visually lossy beginning at about 30:1 CR. The novelty of this technique lies in its unique capability to adaptively vary the characteristics of the spectral correlation transformation as a function of the variation of the local terrain. The spectral and spatial modularity of the algorithm architecture allows the JPEG to be replaced by a alternate spatial coding procedure. The significant practical advantage of this proposed approach is that it is based on the standard and highly developed JPEG compression technology