Lossless Compression Of Hyperspectral Images Research Articles

Visualization, Band Ordering and Compression of Hyperspectral Images

Air-borne and space-borne acquired hyperspectral images are used to recognize objects and to classify materials on the surface of the earth. The state of the art compressor for lossless compression of hyperspectral images is the Spectral oriented Least SQuares (SLSQ) compressor (see [1–7]). In this paper we discuss hyperspectral image compression: we show how to visualize each band of a hyperspectral image and how this visualization suggests that an appropriate band ordering can lead to improvements in the compression process. In particular, we consider two important distance measures for band ordering: Pearson’s Correlation and Bhattacharyya distance, and report on experimental results achieved by a Java-based implementation of SLSQ.

Lossless Compression of Hyperspectral Images Based on Classification and Coset Coding

摘要: 在基于陪集码的高光谱图像压缩算法中，由于按照编码块的最大残差确定整块无损压缩所需的码率存在较大冗余，该文提出了基于分类和陪集码的高光谱图像压缩算法。首先利用前一波段对应位置的预测噪声对当前波段编码块的像素进行分类，将具有相似相关性的像素归于一类，然后对每一类像素分别进行陪集码编码。实验表明分类可以有效地降低码率。和基于陪集码的算法相比，该文算法无损压缩的平均码率降低了大约0.4 bpp。 关键词: 高光谱图像 / 无损压缩 / 分布式信源编码 / 陪集码 / 分类

Band regrouping-based lossless compression of hyperspectral images

Hyperspectral remote sensing has been widely utilized in high-resolution climate observation, environment monitoring, resource mapping, etc. However, it brings undesirable difficulties for transmission and storage due to the huge amount of the data. Lossless compression has been demonstrated to be an efficient strategy to solve these problems. In this paper, a novel Band Regrouping based Lossless Compression (BRLlC) algorithm is proposed for lossless compression of hyperspectral images. The affinity propagation clustering algorithm, which can achieve adaptive clustering with high efficiency, is firstly applied to classify all of the hyperspectral bands into several groups based on the inter-band correlation matrix of hyperspectral images. Consequently, hyperspectral bands with high correlation are clustered into one group so that the prediction efficiency in each group can be greatly enhanced. In addition, a linear prediction algorithm based on context prediction is applied to the hyperspectral images in each group followed by arithmetic coding. Experimental results demonstrate that the proposed algorithm outperforms some classic lossless compression algorithms in terms of bit per pixel per band and in terms of processing performance.

Error-Resilient and Low-Complexity Onboard Lossless Compression of Hyperspectral Images by Means of Distributed Source Coding

In this paper, we propose a lossless compression algorithm for hyperspectral images inspired by the distributed-source-coding (DSC) principle. DSC refers to separate compression and joint decoding of correlated sources, which are taken as adjacent bands of a hyperspectral image. This concept is used to design a compression scheme that provides error resilience, very low complexity, and good compression performance. These features are obtained employing scalar coset codes to encode the current band at a rate that depends on its correlation with the previous band, without encoding the prediction error. Iterative decoding employs the decoded version of the previous band as side information and uses a cyclic redundancy code to verify correct reconstruction. We develop three algorithms based on this paradigm, which provide different tradeoffs between compression performance, error resilience, and complexity. Their performance is evaluated on raw and calibrated AVIRIS images and compared with several existing algorithms. Preliminary results of a field-programmable gate array implementation are also provided, which show that the proposed algorithms can sustain an extremely high throughput.

Lossless Compression of Hyperspectral Images Using a Quantized Index to Lookup Tables

We propose an enhancement to the algorithm for lossless compression of hyperspectral images using lookup tables (LUTs). The original LUT method searched the previous band for a pixel of equal value to the pixel colocalized with the one to be predicted. The pixel in the same position as the obtained pixel in the current band is used as a predictor. LUTs were used to speed up the search. The LUT method has also been extended into a method called Locally Averaged Interband Scaling (LAIS)-LUT that uses two LUTs per band. One of the two LUT predictors that is the closest one to the LAIS estimate is chosen as the predictor for the current pixel. We propose the uniform quantization of the colocated pixels before using them for indexing the LUTs. The use of quantization reduces the size of the LUTs by an order of magnitude. The results show that the proposed method outperforms previous methods; a 3% increase in compression efficiency was observed compared to the current state-of-the-art method, LAIS-LUT.

Lossless Hyperspectral-Image Compression Using Context-Based Conditional Average

In this paper, a new algorithm for lossless compression of hyperspectral images is proposed. The spectral redundancy in hyperspectral images is exploited using a context-match method driven by the correlation between adjacent bands. This method is suitable for hyperspectral images in the band-sequential format. Moreover, this method compares favorably with the recent proposed lossless compression algorithms in terms of compression, with a lower complexity.

Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images

This paper deals with the application of distributed source coding (DSC) theory to remote sensing image compression. Although DSC exhibits a significant potential in many application fields, up till now the results obtained on real signals fall short of the theoretical bounds, and often impose additional system-level constraints. The objective of this paper is to assess the potential of DSC for lossless image compression carried out onboard a remote platform. We first provide a brief overview of DSC of correlated information sources. We then focus on onboard lossless image compression, and apply DSC techniques in order to reduce the complexity of the onboard encoder, at the expense of the decoder's, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and is compared with other state-of-the-art 2D and 3D coders. Both schemes turn out to achieve competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main issues that are still to be solved to further improve the performance of DSC-based remote sensing systems.

Lossless Compression of Hyperspectral Images Based on Single Neighbor Multi-Bands Prediction

Lossless Compression of Hyperspectral Images Based on Single Neighbor Multi-Bands Prediction

Lossless compression of hyperspectral images using lookup tables

In this letter, we propose a new algorithm for lossless compression of hyperspectral images. The proposed method searches the previous band for a pixel of equal value to the pixel co-located to the one to be coded. The pixel in the same position as the obtained pixel in the current band is used as the predictor. Lookup tables are used to speed up the search. The algorithm is suitable for compression of hyperspectral image data in the band-interleaved-by-line (BIL) format. The method outperforms other state-of-the-art compression algorithms for the BIL data, at a lower time complexity level. Moreover, its compression ratios for the band sequential format data are within a few percentage points of the current state-of-the-art methods.

Parallel implementation of linear prediction model for lossless compression of hyperspectral airborne visible infrared imaging spectrometer images

We present the implementation of a lossless hyperspectral image compression method for novel parallel environments. The method is an interband version of a linear prediction approach for hyperspectral images. The interband linear prediction method consists of two stages: predictive decorrelation that produces residuals and the entropy coding of the residuals. The compression part is embarrassingly parallel, while the decompression part uses pipelining to parallelize the method. The results and comparisons with other methods are discussed. The speedup of the thread version is almost linear with respect to the number of processors.

Linear prediction in lossless compression of hyperspectral images

This study proposes an interband version of the linear prediction approach for hyperspectral images. Linear prediction represents one of the best performing and most practical and general purpose lossless image compression techniques known today. The interband linear prediction method consists of two stages: predictive decorrelation producing residuals, and entropy coding of these residuals. Our method achieved an average compression ratio of 3.23 using 13 airborne visible/infrared imaging spectrometer (AVIRIS) images.