Reprint

Remote Sensing of Target Object Detection and Identification II

September 2024
320 pages
  • ISBN978-3-7258-2119-8 (Hardback)
  • ISBN978-3-7258-2120-4 (PDF)
https://doi.org/10.3390/books978-3-7258-2120-4 (registering)

This is a Reprint of the Special Issue Remote Sensing of Target Object Detection and Identification II that was published in

Engineering
Environmental & Earth Sciences
Summary

The articles belonging to this Special Issue provide a comprehensive overview of the advancements, challenges, and future trends in object detection and tracking, with a particular emphasis on remote sensing applications. They discuss a wide range of topics, including different types of targets (e.g., ships, small targets), imaging modalities (e.g., optical, SAR, infrared), image processing techniques, and deep learning algorithms.

In the first group of articles, different aspects of ship detection in remote sensing images, including challenges, advancements, and datasets, are discussed. These sources specifically focus on ship detection in SAR images, which poses unique challenges due to the presence of speckle noise and the need for robust algorithms that can handle different ship sizes and orientations. The second group addresses the problem of detecting small targets in infrared images, which is a complex task due to the small size of the targets, low contrast with the background, and the presence of noise and clutter. The third group focuses on target tracking in image sequences, which involves estimating the trajectory of a target over time.

Format
  • Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
space target; heterogeneous multi-core system; detection and tracking; MJDTM; FPGA; DSP; star background suppression; recursive moving target indication; dim space target detection; optical remote sensing; small-ship detection; lightweight detection; convolutional neural network; infrared small target detection; proximal gradient; approximate partial SVD; GPU acceleration; infrared small target detection; principle component pursuit; group low-rank regularization; infrared patch-image model; anomaly detection; multi-dimensional; low-rank; active sonar; low signal-to-reverberation ratio; underwater target; tracking by detection; kernel correlation filter; convolutional neural network; multiscale features; infrared image; small-target detection; ship detection; reppoints; adaptive sample selection; guided learning; synthetic aperture radar (SAR); scattering point; ship detection; deep learning; optical remote-sensing images; convolutional neural network; transformer; event cameras; multi-scale fusion; remote sensing; small target detection; corner reflector array; combat dilution jamming; change the frequency modulation slope; mismatched filter; support vector machine; content loss; Recurrent Rain-Attentive Module; single-image deraining; squeeze-and-excitation; n/a

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