Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of the available HSI-CSR methods consider the sparsity prior in spatial and spectral vector domains via vectorizing hyperspectral cubes along a certain dimension. Besides, in most previous works, little attention has been paid to exploiting the underlying nonlocal structure in spatial domain of the HSI. In this paper, we propose a nonlocal tensor sparse and low-rank regularization (NTSRLR) approach, which can encode essential structured sparsity of an HSI and explore its advantages for HSI-CSR task. Specifically, we study how to utilize reasonably the l1 -based sparsity of core tensor and tensor nuclear norm function as tensor sparse and low-rank regularization, respectively, to describe the nonlocal spatial-spectral correlation hidden in an HSI. To study the minimization problem of the proposed algorithm, we design a fast implementation strategy based on the alternative direction multiplier method (ADMM) technique. Experimental results on various HSI datasets verify that the proposed HSI-CSR algorithm can significantly outperform existing state-of-the-art CSR techniques for HSI recovery.

Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction

Rapid advancement in the development of hyperspectral image analysis techniques has led to specialized hyperspectral missions. It results in the bulk transmission of hyperspectral images from sensors to analysis centers and finally to data centers. Storage of these large size images is a critical issue that is handled by compression techniques. This survey focuses on different hyperspectral image compression algorithms that have been classified into two broad categories based on eight internal and six external parameters. In addition, we identified research challenges and suggested future scope for each technique. The detailed classification used in this paper can categorize other compression algorithms and may help in selecting research objectives.

https://www.spiedigitallibrary.org/journals/optical-engineering/volume-59/issue-9/090902/Comprehensive-review-of-hyperspectral-image-compression-algorithms/10.1117/1.OE.59.9.090902.pdf

Comprehensive review of hyperspectral image compression algorithms

The review of the main methods for lossless images compression that can be used in remote sensing tasks are given. The major advantages and disadvantages of image compression methods in terms of implementation on FPGA are shown. The recommendations are made for the use of compression techniques in the construction of onboard systems for remote sensing.

Lossless image compression in the remote sensing applications

With the development of remote sensing technology, spatial and spectral resolutions of hyperspectral images have become increasingly dense. In order to overcome difficulties in the storage, transmission, and manipulation of hyperspectral images, an effective compression algorithm is requisite. The clustered differential pulse code modulation (C-DPCM), which is a prediction-based hyperspectral image lossless compression algorithm, can achieve a relatively high compression ratio, but its efficiency still requires improvement. This paper presents a parallel implementation of the C-DPCM algorithm on graphics processing units (GPUs) with the compute unified device architecture, which is a parallel computing platform and programming model developed by NVIDIA. Three optimization strategies are utilized to implement the C-DPCM algorithm in parallel, including a version that uses shared memory and registers, a version that employs multistream, and a version that uses multi-GPU. In addition, we studied how to assign all classes to each GPU to minimize the processing time. Finally, we reduced the compression time from approximately half an hour to an hour to several seconds, with almost no loss in accuracy.

GPU Acceleration of Clustered DPCM for Lossless Compression of Hyperspectral Images

Effective compression of hyperspectral (HS) images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy HS image compression method based on enhanced multivariance products representation (EMPR) is proposed. As an efficient data decomposition method, EMPR enables us to represent the given multidimensional data with lower-dimensional entities. EMPR, as a finite expansion with relevant approximations, can be acquired by truncating this expansion at certain levels. Thus, EMPR can be utilized as a highly effective lossy compression algorithm for hyper spectral images. In addition to these, an efficient variety of EMPR is also introduced in this article, in order to increase the compression efficiency. The results are benchmarked with several state-of-the-art lossy compression methods. It is observed that both higher peak signal-to-noise ratio values and improved classification accuracy are achieved from EMPR-based methods.

/pdf/iterative-enhanced-multivariance-products-representation-for-2pd5afh76d.pdf

Iterative Enhanced Multivariance Products Representation for Effective Compression of Hyperspectral Images

Integer-coefficient Discrete Wavelet Transformation (DWT) filters widely used in the literature are implemented and investigated as spectral decorrelator. As the performance of spectral decorrelation step has direct impact on the compression ratio (CR), it is important to employ the most convenient spectral decorrelator in terms of computational complexity and CR. Tests using AVIRIS image data set are carried out and CRs corresponding to various subband decomposition levels are presented within a lossless hyperspectral compression framework. Two-dimensional images corresponding to each band is compressed using JPEG-LS algorithm. Results suggest that Cohen-Daubechies-Feauveau (CDF) 9/7 integer-coefficient wavelet transform with five levels of spectral subband decomposition would be an efficient spectral decorrelator for onboard lossless hyperspectral image compression.

Lossless hyperspectral image compression using wavelet transform based spectral decorrelation

Integer-coefficient Discrete Wavelet Transformation (DWT) filters widely used in the literature are implemented and investigated as spectral decorrelator for on-board lossless hyperspectral image compression. As the performance of spectral decorrelation step has direct impact on the compression ratio (CR), it is important to employ the most convenient spectral decorrelator in terms of low computational complexity and high CR. Extensive tests using AVIRIS image data set are carried out and CRs corresponding to various subband decomposition levels are presented within a lossless hyperspectral compression framework. Results suggest that Cohen-Daubechies-Feauveau (CDF) 9/7 integer-coefficient wavelet transform with five levels of spectral subband decomposition would be an efficient spectral decorrelator for on-board lossless hyperspectral image compression.

Evaluation of on-board integer wavelet transform based spectral decorrelation schemes for lossless compression of hyperspectral images

In this study, a hardware efficient field-programmable-gate-array (FPGA) implementation of the JPEG-LS encoder for lossless image compression is introduced. Encoder architecture comprises both regular mode and run mode with run interruption sample encoding procedures for full compliance with the ISO/ITU standard. Differently from former reported implementations, prediction error computation is optimized with pipeline data forwarding technique for optimum delay and minimum complexity. Besides, procedures of the run-length encoding are realized with normalization scheme using look-up tables without update latency. Synthesis results showed that proposed optimizations improved the processing speed of the encoder noticeably while FPGA hardware footprint is significantly reduced.

FPGA-based JPEG-LS encoder for onboard real-time lossless image compression

In this paper, a high speed pipelined architecture with pipeline scheduler is presented for the JPEG2000 arithmetic encoder. The new design has removed the need for the additional hardware units for the procedures of the encoding process with suitably controlling the pipeline stages. The proposed system is described in Verilog HDL and targeted for the Xilinx Virtex-5 FPGA family. Results of the timing analysis showed that designed architecture is able to achieve 132 Msymbols/sec throughput.

A high throughput MQ arithmetic encoder implementation for JPEG2000

This study focuses on improving capabilities of a single-band lossless JPEG-LS encoder with a preprocessing unit. Main motivation is preserving its genuine low complexity that enables high-throughput hardware implementations. Although JPEG-LS standard describes procedures for near-lossless and multicomponent compression, a conveniently designed preprocessor unit can easily bestow any single-band lossless JPEG-LS encoder gain these capabilities without change on itself. Similarly, its compression performance can be improved with selective compression. Idea depends on the detection of regions out-of-interest (cloud, snow etc.) employing their distinct spectral signature and remapping pixels in these regions so that highest compression with the JPEG-LS can be yielded based on its algorithm. Regions out-ofinterest can be compressed regardless of the outcome quality as they contain no significant information. Through analyses are achieved on satellite images for the preprocess approaches with software. Besides, designed preprocessor unit is implemented with field-programmable-gate-array (FPGA) and implementation details are provided.

Yakup Murat Mert

Papers

Lossless hyperspectral image compression using wavelet transform based spectral decorrelation

Evaluation of on-board integer wavelet transform based spectral decorrelation schemes for lossless compression of hyperspectral images

FPGA-based JPEG-LS encoder for onboard real-time lossless image compression

A high throughput MQ arithmetic encoder implementation for JPEG2000

Low complexity scheme with JPEG-LS for near-lossless, multi-component and selective compression