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Downsampling01:20

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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  11. Lossless And Near-lossless Compression Algorithms For Remotely Sensed Hyperspectral Images.

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

Amal Altamimi1,2, Belgacem Ben Youssef1

  • 1Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.

Entropy (Basel, Switzerland)
|April 26, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

New compression methods for hyperspectral images (HSIs) enable efficient onboard processing. These techniques offer significant data reduction for remote sensing applications, improving satellite capabilities.

Area of Science:

  • Remote Sensing
  • Data Compression
  • Image Processing

Background:

  • Advancements in hyperspectral imaging (HSI) necessitate efficient onboard data processing due to satellite resource constraints.
  • High-resolution and rapid acquisition rates of HSI data demand novel compression techniques.

Purpose of the Study:

  • To propose two novel lossless and near-lossless compression methods for hyperspectral images (HSIs).
  • To address the need for efficient onboard data compression in resource-constrained satellite environments.

Main Methods:

  • Development of a near-lossless compression method using seed generation algorithms for real-time onboard application.
  • Implementation of a lossless compression method utilizing quadrature-based square rooting algorithms.
  • Evaluation of compression performance on hyperspectral images from the Corpus dataset.
Keywords:
hyperspectral imagesimage compressionlossless compressionnear-lossless compression

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Main Results:

  • The near-lossless method achieves a data reduction of nearly 40% with low error rates (max relative error 0.33, max absolute error 30).
  • The lossless method achieves compression ratios up to 2.6 on the Corpus dataset.
  • Proposed lossless methods show improved compression rates compared to the k2-raster technique, with up to 29.89% enhancement in geometric mean values.

Conclusions:

  • The proposed compression methods are suitable for real-time onboard processing of hyperspectral images.
  • These techniques offer significant data reduction, enhancing the capabilities of remote sensing satellites.
  • The methods provide a competitive alternative to existing state-of-the-art compression techniques.
remote sensing
seed generation
square rooting