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Related Concept Videos

Downsampling01:20

Downsampling

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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.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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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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Aliasing01:18

Aliasing

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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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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
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.

Keywords:
hyperspectral imagesimage compressionlossless compressionnear-lossless compressionremote sensingseed generationsquare rooting

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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.

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.