SAR-Based Signal Processing and Target Recognition

ISBN 10: 3036586369 / ISBN 13: 9783036586366

Published by MDPI AG, 2023

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Synthetic aperture radar (SAR) is a class of significantly important remote sensors that work effectively during all weather conditions and all times of day. SAR has the capability to provide very-high-resolution images and multi-dimensional data during limited periods of time, enhancing the spatial-time resolution of observations. In recent years, SAR technology has been developing towards the trend of multi-dimensional imaging and fine-grained image recognition. Meanwhile, the paradigms of SAR imaging and information perception have also been greatly changed to multi-mode, multi-dimensional and intelligent processing strategies. Recently, machine learning and deep learning methods have been applied to SAR imaging and target recolonization to drive various algorithms, which can be classified as model-based and data-learning techniques. Compared to model-based approaches, learning algorithms are more adaptive and show superior performance. However, when limited to small data sets, complex scenes, etc., these learning algorithms may suffer from bad generalization capability and low feature robustness. This Special Issue introduces some newly advanced signal processing and target recognition technologies in SAR, including some new theories, models, concepts and architecture designs for multi-mode/multi-dimensional SAR imaging and parameter inversion, sparse techniques of SAR and ISAR imaging, SAR interference and anti-interference, SAR/InSAR image enhancement, SAR target detection and recognition, SAR image classification and interpretation.

Seller Inventory # 9783036586366

Bibliographic Details

• Title: SAR-Based Signal Processing and Target Recognition
• Publisher: MDPI AG
• Publication Date: 2023
• Language: English
• ISBN 10: 3036586369
• ISBN 13: 9783036586366
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 39D millimeters 250 millimeters width by 175 millimeters height by 39 millimeters depth
• Weight: 1564 grams

About this title

Synopsis

Synthetic aperture radar (SAR) is a class of significantly important remote sensors that work effectively during all weather conditions and all times of day. SAR has the capability to provide very-high-resolution images and multi-dimensional data during limited periods of time, enhancing the spatial-time resolution of observations. In recent years, SAR technology has been developing towards the trend of multi-dimensional imaging and fine-grained image recognition. Meanwhile, the paradigms of SAR imaging and information perception have also been greatly changed to multi-mode, multi-dimensional and intelligent processing strategies. Recently, machine learning and deep learning methods have been applied to SAR imaging and target recolonization to drive various algorithms, which can be classified as model-based and data-learning techniques. Compared to model-based approaches, learning algorithms are more adaptive and show superior performance. However, when limited to small data sets, complex scenes, etc., these learning algorithms may suffer from bad generalization capability and low feature robustness. This Special Issue introduces some newly advanced signal processing and target recognition technologies in SAR, including some new theories, models, concepts and architecture designs for multi-mode/multi-dimensional SAR imaging and parameter inversion, sparse techniques of SAR and ISAR imaging, SAR interference and anti-interference, SAR/InSAR image enhancement, SAR target detection and recognition, SAR image classification and interpretation.