Compression Algorithm For Hyperspectral Images Research Articles

Curvelet Transform Based Compression Algorithm for Low Resource Hyperspectral Image Sensors

The wavelet transform is widely used in the task of hyperspectral image compression (HSIC). They have achieved outstanding performance in the compression of a hyperspectral (HS) image, which has attracted great interest. However, transform based hyperspectral image compression algorithm (HSICA) has low-coding gain than the other state of art HSIC algorithms. To solve this problem, this manuscript proposes a curvelet transform based HSIC algorithm. The curvelet transform is a multiscale mathematical transform that represents the curve and edges of the HS image more efficiently than the wavelet transform. The experiment results show that the proposed compression algorithm has high-coding gain, low-coding complexity, at par coding memory requirement, and works for both (lossy and lossless) compression. Thus, it is a suitable contender for the compression process in the HS image sensors.

Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations

The work is devoted to the description of the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery of 85 % or more, taking into account which an adaptive algorithm for compressing hyperspectral AI and the generated quantization table have been developed. The existing solutions to the lossless compression problem for hyperspectral aerospace images are analyzed. Based on them, a compression algorithm is proposed taking into account inter-channel correlation and the Walsh-Hadamard transformation, characterized by data transformation with a decrease in the range of the initial values by forming a set of channel groups [10–15] with high intra-group correlation [0.9–1] of the corresponding pairs with the selection of optimal parameters. The results obtained in the course of the research allow us to determine the optimal parameters for compression: the results of the compression ratio indicators were improved by more than 30 % with an increase in the size of the parameter channels. This is due to the fact that the more values to be converted, the fewer bits are required to store them. The best values of the compression ratio [8–12] are achieved by choosing the number of channels in an ordered group with high correlation.

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.

Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations

The paper describes the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery, taking into account which an adaptive algorithm for compressing hyperspectral AI and the generated quantization table has been developed. The conducted studies have shown that the proposed lossy algorithms have sufficient efficiency for use and can be applied when transmitting hyperspectral remote sensing data in conditions of limited buffer memory capacity and bandwidth of the communication channel.

Design and Implementation of a Lossless Compression System for Hyperspectral Images

Despite its popularity, the hyperspectral image compression algorithm recommended by the Consultative Committee for Space Data Systems (CCSDS) faces a long delay of the feedback loop and complex computations in the modes of band sequential (BSQ) and band interleaved by line (BIL). After analyzing the features of the CCSDS algorithm, this paper proposes a forward prediction method based on the xc7k325tffg9000 field programmable gate array (FPGA) chip (Xilinx Inc.), and adjusts the calculation flow of the CCSDS algorithm, aiming to shorten the time delay in the feedback loop. In addition, full-pipeline construction was implemented on FPGA board to realize real-time processing of data, and dynamic configuration of image parameters. Through functional simulation and off-board test, it is learned that, for the speed-insensitive path, the optimized algorithm can realize the complex operations of the original algorithm with less hardware resources; for hyperspectral image data with an effective input bit width of 12bit, the proposed method can reach a maximum operating frequency of 103MHz, and the data throughput of 103M samples per second (1.237Gbps).

CCSDS-MHC on Raspberry Pi for Lossless Hyperspectral Image Compression

This paper is about Consultative Committee for Space Data System for Lossless Multispectral and Hyperspectral Image Compression (CCSDS-MHC) algorithm that is implemented on Raspberry Pi 3 Model B+ system using Open Multi-Processing (OpenMP). CCSDS-MHC algorithm along with the full prediction mode is opted due to its best compression ratio (CR) performance. The issue of Hyperspectral Image Compression is the loss of data when compress, thus CCSDS algorithm is used in this research. Besides, with current technologies that require low-power but high-performance devices, Raspberry Pi was chosen to be tested in terms of its performances while being compared to other platforms. AVIRIS (airborne) and Hyperion (spaceborne) are used to test the performance of the system. OpenMP is introduced to simplify the computational operation through parallelization to take advantage of the multi-core architecture of the hardware system. In term of execution time, CCSDS-MHC algorithm when parallelize using OpenMP gave the best performances about 69.4% for AVIRIS 1997 dataset, 69.3% for AVIRIS 2006 dataset and 67.7% for Hyperion dataset. The execution time of performance CCSDS-MHC in Raspberry Pi 3 Model B+ comparing with other different multicore platform is validate and the mean parallelize is measured. The result of comparison proves that faster performing task gives the best result due to higher speed of CPU-cores performances. From this research, it is proven that Raspberry Pi which is a low-power embedded platform able to compress the hyperspectral images at optimized speed with the implementation of OpenMP through CCSDS algorithm. Therefore, this research will be useful for further studies in lossless of hyperspectral images.

An Extremely Pipelined FPGA Implementation of a Lossy Hyperspectral Image Compression Algorithm

Segmented and pipelined execution has been a staple of computing for the past decades. Operations over different values can be carried out at the same time speeding up computations. Hyperspectral image compression sequentially processes samples, exploiting local redundancies to generate a predictable data stream that can be compressed. In this article, we take advantage of a low complexity predictive lossy compression algorithm which can be executed over an extremely long pipeline of hundreds of stages. We can avoid most stalls and maintain throughput close to the theoretical maximum. The different steps operate over integers with simple arithmetic operations, so they are especially well-suited for our FPGA implementation. Results on a Virtex-7 show a maximum frequency of over 300 MHz for a throughput of over 290 MB/s, with a space-qualified Virtex-5 reaching 258 MHz, being five times as fast as the previous FPGA designs. This shows that a modular pipelined approach is beneficial for these kinds of compression algorithms.

The Hyper-spectral Image Compression Based on K-Means Clustering and Parallel Prediction Algorithm*

In this paper, we propose a lossless compression algorithm for hyper-spectral images with the help of the K-Means clustering and parallel prediction. We use K-Means clustering algorithm to classify hyper-spectral images, and we obtain a number of two dimensional sub images. We use the adaptive prediction compression algorithm based on the absolute ratio to compress the two dimensional sub images. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. So we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. In this paper, a variety of hyper-spectral image compression algorithms are compared with the proposed method. The experimental results show that the proposed algorithm can effectively improve the compression ratio of hyper-spectral images and reduce the compression time effectively.

Erratum to: An Improved Compression Algorithm for Hyperspectral Images based on DVAT-SVD

Erratum to: An Improved Compression Algorithm for Hyperspectral Images based on DVAT-SVD

An Improved Compression Algorithm for Hyperspectral Images based on DVAT-SVD

Hyperspectral image (HSI) compression has recently become a popular research area in remote sensing applications. However, existing clustering and compression techniques are not based on the intensity value and work by forming static rules. To overcome these issues, a novel lossy (nearly lossless) compression algorithm based on the distributed visual assessment cluster tendency (DVAT) – singular value decomposition (SVD) technique is proposed. At first, the given HSI image is preprocessed by cellular automata filtering to remove the white Gaussian and speckle noise. After that, the preprocessed image is split into bands, which are formed to a distance matrix. Here, the distances between the image bands are predicted to identify pixels with related intensity values. Hence, the DVAT technique is implemented to cluster the nearest distance images. Then, the label of the cluster is optimally selected for segregating the band into a band index. Consequently, the cluster index is formed and the image matrix is clustered based on the label features. After that, the SVD technique is applied to encode the HSI band image. Therefore, the discrete wavelet transform technique is applied for transformation and the transformed cell redundancy check technique is employed to encode the image band. Then, the image is reconstructed by reversing the above processes. The major advantages of the proposed method comprise a cleared clustered output, a correct index value, and the reduction of data losses during compression. During experimental results, the performance of the proposed method was evaluated in terms of the compression ratio, the mean-square error, and the peak signal-to-noise ratio.

Distributed lossless compression algorithm for hyperspectral images based on the prediction error block and multiband prediction

Distributed lossless compression algorithm for hyperspectral images based on the prediction error block and multiband prediction

DSC的超光谱图像无损压缩算法

DSC的超光谱图像无损压缩算法

基于分类的高光谱图像压缩算法

基于分类的高光谱图像压缩算法

基于分类的高光谱图像压缩算法

基于分类的高光谱图像压缩算法

Distributed Lossless Compression Algorithm for Hyperspectral Images Based on Classification

This article addresses the problem of distributed lossless compression for hyperspectral images and proposes an effective lossless compression algorithm based on classification. First, a band selection algorithm was performed on the hyperspectral images to select those bands with considerable information. Next, the K-means algorithm was performed on those selected bands to obtain the classification map. To make full use of the spectral and spatial correlation, a multilinear regression model was introduced to construct the high-quality side information of each class within the identical block according to the classification map. Subsequently, the (n, k) linear grouping codes were employed to perform the distributed source coding for each class separately. The experimental results showed that the proposed algorithm has a competitive lossless compression performance compared with other state-of-the-art algorithms.

Hybrid Prediction and Fractal Hyperspectral Image Compression

The data size of hyperspectral image is too large for storage and transmission, and it has become a bottleneck restricting its applications. So it is necessary to study a high efficiency compression method for hyperspectral image. Prediction encoding is easy to realize and has been studied widely in the hyperspectral image compression field. Fractal coding has the advantages of high compression ratio, resolution independence, and a fast decoding speed, but its application in the hyperspectral image compression field is not popular. In this paper, we propose a novel algorithm for hyperspectral image compression based on hybrid prediction and fractal. Intraband prediction is implemented to the first band and all the remaining bands are encoded by modified fractal coding algorithm. The proposed algorithm can effectively exploit the spectral correlation in hyperspectral image, since each range block is approximated by the domain block in the adjacent band, which is of the same size as the range block. Experimental results indicate that the proposed algorithm provides very promising performance at low bitrate. Compared to other algorithms, the encoding complexity is lower, the decoding quality has a great enhancement, and the PSNR can be increased by about 5 dB to 10 dB.

Improved non-negative tensor Tucker decomposition algorithm for interference hyper-spectral image compression

The compression method, first proposed in 2012, is based on the non-negative tensor decomposition for interference hyper-spectral image data. As a tensor is generated by a huge amount of interference hyper-spectral images, the multiplicative update algorithm is made extremely complicated, and even unfeasible. To reduce the computational cost and speed up the convergence, this paper, based on the characteristics of interference hyper-spectral images, develops a new algorithm using different down-sampling factors for different non-negative wavelet sub-band tensors. The experimental results showed that this algorithm could significantly shorten the running time, while maintaining a good compression performance compared with the conventional methods.

FIVQ algorithm for interference hyper-spectral image compression

Based on the improved vector quantization (IVQ) algorithm [1] which was proposed in 2012, this paper proposes a further improved vector quantization (FIVQ) algorithm for LASIS (Large Aperture Static Imaging Spectrometer) interference hyper-spectral image compression. To get better image quality, IVQ algorithm takes both the mean values and the VQ indices as the encoding rules. Although IVQ algorithm can improve both the bit rate and the image quality, it still can be further improved in order to get much lower bit rate for the LASIS interference pattern with the special optical characteristics based on the pushing and sweeping in LASIS imaging principle. In the proposed algorithm FIVQ, the neighborhood of the encoding blocks of the interference pattern image, which are using the mean value rules, will be checked whether they have the same mean value as the current processing block. Experiments show the proposed algorithm FIVQ can get lower bit rate compared to that of the IVQ algorithm for the LASIS interference hyper-spectral sequences.

Distributed near lossless compression algorithm for hyperspectral images

This paper presents a new near lossless compression algorithm for hyperspectral images based on distributed source coding. The algorithm is performed on blocks that have the same location and size in each band. Because the importance varies from block to block along the spectral orientation, an adaptive rate allocation algorithm that weights the energy of each block under the target rate constraints is introduced. A simple linear prediction model is employed to construct the side information of each block for Slepian–Wolf coding. The relationship between the quantized step size and the allocated rate of each block is determined under the condition of correct reconstruction with the side information at the Slepian–Wolf decoder. Slepian–Wolf coding is performed on the quantized version of each block. Experimental results show that the performance of the proposed algorithm is competitive with that of state-of-the-art compression algorithms, making it appropriate for on-board compression.

Highly-Parallel GPU Architecture for Lossy Hyperspectral Image Compression

Graphics Processing Units (GPU) are becoming a widespread tool for general-purpose scientific computing, and are attracting interest for future onboard satellite image processing payloads due to their ability to perform massively parallel computations. This paper describes the GPU implementation of an algorithm for onboard lossy hyperspectral image compression, and proposes an architecture that allows to accelerate the compression task by parallelizing it on the GPU. The selected algorithm was amenable to parallel computation owing to its block-based operation, and has been optimized here to facilitate GPU implementation incurring a negligible overhead with respect to the original single-threaded version. In particular, a parallelization strategy has been designed for both the compressor and the corresponding decompressor, which are implemented on a GPU using Nvidia's CUDA parallel architecture. Experimental results on several hyperspectral images with different spatial and spectral dimensions are presented, showing significant speed-ups with respect to a single-threaded CPU implementation. These results highlight the significant benefits of GPUs for onboard image processing, and particularly image compression, demonstrating the potential of GPUs as a future hardware platform for very high data rate instruments.