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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 8685

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Dr. Filippo Biondi

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Department of Electronic and Electrical Engineering, Strathclyde University, Glasgow G1 1XQ, UK
Interests: deep learning; polarimetric SAR; signal processing
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Dear Colleagues,

This is a period of great expansion of remote sensing satellite systems using synthetic aperture radar (SAR) sensors. We have recently been able to obtain high-resolution data from various sensors, including commercial ones, so we can say that we have plenty of data to process. In this context, we have a strong increase in scientific research in signal processing, statistical signal processing, and artificial intelligence. Recently, research topics in this field have also included the phonon processing information of SAR data. This is considered to be a very interesting topic for conducting different measurement campaigns, which can be used for infrastructure monitoring, and tomographic scanning of the subsoil, for kilometers below, even at high resolution. In short, looking inside matter may not be so difficult using such systems. This Special Issue also does not overlook the multi-temporal processing of interferometric SAR data, and persistent scatterers interferometry and as well polarimetry, which are also very useful for the study of millimetric movements in the ground.
Contributions using new sensors and platforms that consider the integration of datasets or use cloud computing systems are also welcome.

Dr. Filippo Biondi
Guest Editor

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Dr. Filippo Biondi
Guest Editor

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Keywords

synthetic aperture radar
signal processing
statistical signal processing
artificial intelligence
vibrations
doppler tomography

Published Papers (6 papers)

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Research

21 pages, 6549 KiB

Open AccessArticle

The Detection of Green Tide Biomass by Remote Sensing Images and In Situ Measurement in the Yellow Sea of China

by Wei Tian, Juan Wang, Fengli Zhang, Xudong Liu, Jian Yang, Junna Yuan, Xiaofei Mi and Yun Shao

Remote Sens. 2023, 15(14), 3625; https://doi.org/10.3390/rs15143625 - 20 Jul 2023

Cited by 1 | Viewed by 883

Abstract

The world’s largest macroalgae bloom (also known as green tide) has been reported since the 29th Olympic Games in 2008, which is verified as the fast reproduction of floating green macroalgae (Ulva prolifera). It is helpful to assess the biomass of macroalgae for the government of marine environment protection. In this study, the synchronization cruise experiment was firstly introduced, which aimed to investigate the biomass evaluation of Ulva prolifera in the Yellow Sea of China. The Floating Algae Index by Polarimetric SAR image (FAIPS) was then proposed. Finally, the floating algae biomass evaluation model was demonstrated and verified, which showed an exponential relationship between FAIPS and wet biomass per area (kg/m²) of macroalgae. The model proposed in this paper can be used in the biomass assessment of floating algae in the presence of polarimetric SAR images, regardless of daylight and cloud coverage over the sea surface. Full article

(This article belongs to the Special Issue Advances of SAR Data Applications)

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23 pages, 13774 KiB

Open AccessArticle

A Novel Filtering Method of 3D Reconstruction Point Cloud from Tomographic SAR

by Shuhang Dong, Zekun Jiao, Liangjiang Zhou, Qiancheng Yan and Qianning Yuan

Remote Sens. 2023, 15(12), 3076; https://doi.org/10.3390/rs15123076 - 12 Jun 2023

Cited by 2 | Viewed by 1248

Abstract

With the development of airborne synthetic aperture radar (SAR) technology, the 3D SAR point cloud reconstruction has emerged as a crucial development trend in the current SAR community. However, due to measurement errors, environmental interference, radar decoherence, and other noises associated with the SAR system, the reconstructed tomogram is often deteriorated by numerous noisy scatterers. As a result, it becomes challenging to obtain high-quality 3D point clouds of the observed object, making it difficult to further process the point cloud and realize target identification. To address these issues, we propose a K nearest neighbor comprehensive weighted filtering algorithm. The filtered point cloud is evaluated quantitatively using three-dimensional entropy. In this study, we adopted various filtering methods for simulated data, P-band data of Genhe, and Ku-band data of Yuncheng to refine the tomogram and compare their performances. Both qualitative and quantitative analyses demonstrate the superiority of the filtering algorithm proposed in this paper. Full article

(This article belongs to the Special Issue Advances of SAR Data Applications)

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18 pages, 8090 KiB

Open AccessArticle

A Novel Edge Detection Method for Multi-Temporal PolSAR Images Based on the SIRV Model and a SDAN-Based 3D Gaussian-like Kernel

by Xiaolong Zheng, Dongdong Guan, Bangjie Li, Zhengsheng Chen and Lefei Pan

Remote Sens. 2023, 15(10), 2685; https://doi.org/10.3390/rs15102685 - 22 May 2023

Viewed by 969

Abstract

Edge detection for PolSAR images has demonstrated its importance in various applications such as segmentation and classification. Although there are many edge detectors which have demonstrated an impressive ability to achieve accurate edge detection results, these methods only focus on edge detection in a single-date PolSAR image. However, a single-date PolSAR image cannot fully characterize the changes in scattering mechanisms of land cover in different growth cycles, resulting in some omissions of the true edges. In this paper, we propose a novel edge detection method for multi-temporal PolSAR images based on the SIRV model and an SDAN-based 3D Gaussian-like kernel. The spherically invariant random vector (SIRV) and span-driven adaptive neighborhood (SDAN) improve the estimation accuracy of the average covariance matrix (ACM) in terms of data representation and spatial support, respectively. We propose an SDAN-based 2D Gaussian kernel to accurately extract the edge strength of single-date PolSAR images. Then, we design a 1D convolution kernel in the temporal dimension to smooth fluctuations in the edge strength of multi-temporal PolSAR images. The SDAN-based 2D Gaussian kernels in the X- and Y-directions are combined with the 1D convolution kernel in the Z-direction to form an SDAN-based 3D Gaussian-like kernel. In addition, we design an adaptive hysteresis threshold method to optimize the edge map. The performance of our proposed method is presented and analyzed on two real multi-temporal PolSAR datasets, and the results demonstrate that the proposed edge detector achieves a better performance than other edge detectors, particularly for crop regions with time-varying scattering mechanisms. Full article

(This article belongs to the Special Issue Advances of SAR Data Applications)

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23 pages, 90678 KiB

Open AccessArticle

Motion Error Estimation and Compensation of Airborne Array Flexible SAR Based on Multi-Channel Interferometric Phase

by Ling Yang, Fubo Zhang, Yihong Sun, Longyong Chen, Zhenhua Li and Dawei Wang

Remote Sens. 2023, 15(3), 680; https://doi.org/10.3390/rs15030680 - 23 Jan 2023

Cited by 2 | Viewed by 1450

Abstract

Airborne array synthetic aperture radar (SAR) has made a significant breakthrough in the three-dimensional resolution of traditional SAR. In the airborne array SAR 3D imaging technology, the baseline length is the main factor restricting the resolution. Airborne array flexible SAR can increase the baseline length to improve the resolution and interference performance by mounting antennae on the wing. The existing research lacks results obtained using flexible actual data processing and specific motion compensation methods. Thus, this paper proposes a motion error estimation and compensation method for an airborne array flexible SAR based on a multi-channel interferometric phase. Firstly, a flexible channel motion compensation model is established based on the multi-channel interference phase of airborne array flexible SAR. Then, based on the rigid multi-channel data, combined with the ground control points, the least square method, and the global optimal search algorithm, the accurate rigid baseline length and the central incidence angle are obtained. Finally, according to the multi-channel interference phase inversion of the flexible motion error and combined with the motion compensation model, the flexible data are compensated in the time domain. The actual results indicate that, compared with traditional motion compensation methods, our method can obtain accurate flexible compensation data. This study improves the interference performance of multi-channel data of airborne array flexible SAR and lays a solid foundation for the high-precision 3D reconstruction of airborne array flexible SAR. Full article

(This article belongs to the Special Issue Advances of SAR Data Applications)

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14 pages, 8504 KiB

Open AccessArticle

Noise-Robust ISAR Translational Motion Compensation via HLPT-GSCFT

by Fengkai Liu, Darong Huang, Xinrong Guo and Cunqian Feng

Remote Sens. 2022, 14(24), 6201; https://doi.org/10.3390/rs14246201 - 07 Dec 2022

Cited by 2 | Viewed by 939

Abstract

Translational motion compensation is a prerequisite of inverse synthetic aperture radar (ISAR) imaging. Translational motion compensation for datasets with low signal-to-noise ratio (SNR) is important but challenging. In this work, we proposed a noise-robust translational motion compensation method based on high-order local polynomial transform–generalized scaled Fourier transform (HLPT-GSCFT). We first model the translational motion as a fourth-order polynomial according to order-of-magnitude analysis, and then design HLPT-GSCFT for translation parameter estimation and parametric translational motion compensation. Specifically, HLPT is designed to estimate the acceleration and third-order acceleration of the translational motion and GSCFT is introduced to estimate the second-order acceleration. Both HLPT and GSCFT have a strong ability for cross-term suppression. In addition, we use a minimum weighted entropy algorithm to estimate the velocity of the translational motion, which can improve the noise robustness of the parameter estimation. Experimental results based on a measured dataset prove that the proposed method is effective and noise-robust. Full article

(This article belongs to the Special Issue Advances of SAR Data Applications)

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22 pages, 7160 KiB

Open AccessArticle

Temporal Subset SBAS InSAR Approach for Tropical Peatland Surface Deformation Monitoring Using Sentinel-1 Data

by Yuta Izumi, Wataru Takeuchi, Joko Widodo, Albertus Sulaiman, Awaluddin Awaluddin, Arif Aditiya, Pakhrur Razi, Titi Anggono and Josaphat Tetuko Sri Sumantyo

Remote Sens. 2022, 14(22), 5825; https://doi.org/10.3390/rs14225825 - 17 Nov 2022

Cited by 5 | Viewed by 2413

Abstract

Tropical peatland in Southeast Asia has undergone rapid degradation and shows large subsidence due to oxidation and peat shrinkage. The measurement of those deformations is thus valuable for evaluating the peat condition and assessing peat restoration. The time series interferometric synthetic aperture radar (TInSAR), especially with the small baseline subsets (SBAS) method, is capable of measuring long-term deformation. However, the dynamic surface scatterers often change in tropical peatland, which degrades the coherent scatterer (CS) distribution density. This article presents a simple and efficient TInSAR approach that enhances the CS density under such dynamic surface scatter variation based on the SBAS method. In the presented approach, a long-time series of single-look complex images is separated into subsets, and deformation estimation is performed for each subset. The effectiveness of this simple solution was investigated by InSAR simulation and validated using SAR observation data. We applied the subset SBAS approach to the three-year Sentinel-1A C-band SAR dataset acquired over tropical peatland in Indonesia. The analyses showed an improved number of CSs for the introduced subset approach. We further introduce the color representation of CS temporal behavior per subset for visual interpretation of scatterer change. Full article

(This article belongs to the Special Issue Advances of SAR Data Applications)

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