A Prediction-Based Spatial-Spectral Adaptive Hyperspectral Compressive Sensing Algorithm

Xu, Ping; Chen, Bingqiang; Xue, Lei; Zhang, Jingcheng; Zhu, Lei

doi:10.3390/s18103289

Cited by 11 publications

(4 citation statements)

References 28 publications

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“…The preprocessor's main function is to decorrelate the input data, which is then passed to the entropy encoder. Decorrelation can be achieved by means of prediction [25,62,[69][70][71][72] or transform-based techniques [51,[73][74][75][76]. According to [69], techniques that use lookup tables and vector quantization are also categorized as prediction-based since both types are used to generate a prediction of the data.…”

Section: Preprocessingmentioning

confidence: 99%

Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images

Altamimi,

Ben Youssef

2024

Entropy

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Rapid and continuous advancements in remote sensing technology have resulted in finer resolutions and higher acquisition rates of hyperspectral images (HSIs). These developments have triggered a need for new processing techniques brought about by the confined power and constrained hardware resources aboard satellites. This article proposes two novel lossless and near-lossless compression methods, employing our recent seed generation and quadrature-based square rooting algorithms, respectively. The main advantage of the former method lies in its acceptable complexity utilizing simple arithmetic operations, making it suitable for real-time onboard compression. In addition, this near-lossless compressor could be incorporated for hard-to-compress images offering a stabilized reduction at nearly 40% with a maximum relative error of 0.33 and a maximum absolute error of 30. Our results also show that a lossless compression performance, in terms of compression ratio, of up to 2.6 is achieved when testing with hyperspectral images from the Corpus dataset. Further, an improvement in the compression rate over the state-of-the-art k2-raster technique is realized for most of these HSIs by all four variations of our proposed lossless compression method. In particular, a data reduction enhancement of up to 29.89% is realized when comparing their respective geometric mean values.

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Section: Preprocessingmentioning

confidence: 99%

Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images

Altamimi,

Ben Youssef

2024

Entropy

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“…The decision layer describes verbs using one or more prediction algorithms, assuming that generic behaviors are quite likely. Based on the prediction algorithm’s results, the decision layer can perform one or more actions [ 124 ]. The intended activity is automatable.…”

Section: Application Of Agents In Sgsmentioning

confidence: 99%

Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid

Binyamin

Slama

2022

Sensors

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Multi-Agent Systems (MAS) have been seen as an attractive area of research for civil engineering professionals to subdivide complex issues. Based on the assignment’s history, nearby agents, and objective, the agent intended to take the appropriate action to complete the task. MAS models complex systems, smart grids, and computer networks. MAS has problems with agent coordination, security, and work distribution despite its use. This paper reviews MAS definitions, attributes, applications, issues, and communications. For this reason, MASs have drawn interest from computer science and civil engineering experts to solve complex difficulties by subdividing them into smaller assignments. Agents have individual responsibilities. Each agent selects the best action based on its activity history, interactions with neighbors, and purpose. MAS uses the modeling of complex systems, smart grids, and computer networks. Despite their extensive use, MAS still confronts agent coordination, security, and work distribution challenges. This study examines MAS’s definitions, characteristics, applications, issues, communications, and evaluation, as well as the classification of MAS applications and difficulties, plus research references. This paper should be a helpful resource for MAS researchers and practitioners. MAS in controlling smart grids, including energy management, energy marketing, pricing, energy scheduling, reliability, network security, fault handling capability, agent-to-agent communication, SG-electrical cars, SG-building energy systems, and soft grids, have been examined. More than 100 MAS-based smart grid control publications have been reviewed, categorized, and compiled.

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“…Both the DCT and DST have some other important applications in digital signal processing, telecommunications, and cryptography. The DCT is also used in wireless communications [7][8][9], digital watermarking [10,11], and analysis of hyperspectral data [12,13]. The DST has additional important applications such as Lossless Image and Intra-Frame Compression [14], image interpolation [15], video steganography [16], and graph filtering [17].…”

Section: Introductionmentioning

confidence: 99%

An Overview of Systolic Arrays for Forward and Inverse Discrete Sine Transforms and Their Exploitation in View of an Improved Approach

2022

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This paper aims to present a unified overview of the main Very Large-Scale Integration (VLSI) implementation solutions of forward and inverse discrete sine transforms using systolic arrays. The main features of the most important solutions to implement the forward and inverse discrete sine transform (DST) using systolic arrays are presented. One of the central ideas presented in the paper is to emphasize the advantages of using regular and modular systolic array computational structures such as cyclic convolution, circular correlation, and pseudo-band correlation in the VLSI implementation of these transforms. The use of such computational structures leads to architectures well adapted to the features of VLSI technologies, with an efficient use of the hardware structures and a reduced I/O cost that helps avoiding the so-called I/O bottleneck. With the techniques presented in this review, we have developed a new VLSI implementation of the DST using systolic arrays that allow efficient hardware implementation with reduced complexity while maintaining high-speed performances. Using a new restructuring input sequence, we have been able to efficiently reformulate the computation of the forward DST transform into a special computational structure using eight short quasi-cycle convolutions that can be computed with low complexity and where some of the coefficients are identical. This leads to a hardware structure with high throughput. The new restructuring sequence is the use of the input samples in a natural order as opposed to previous solutions, leading to a significant reduction of the hardware complexity in the pre-processing stage due to avoiding a permutation stage to reverse the order. Moreover, the proposed VLSI architecture allows an efficient incorporation of the obfuscation technique with very low overheads.

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A Prediction-Based Spatial-Spectral Adaptive Hyperspectral Compressive Sensing Algorithm

Cited by 11 publications

References 28 publications

Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images

Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images

Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid

An Overview of Systolic Arrays for Forward and Inverse Discrete Sine Transforms and Their Exploitation in View of an Improved Approach

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