Special Issue "Time Series Analysis Based on SAR Images"

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

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Dr. Francesco De Zan
E-Mail Website
Guest Editor
German Aerospace Center (DLR), Remote Sensing Technology InstituteResearch fellow, 82234 Wessling, Germany
Tel. +49-8153-28-2150
Interests: Synthetic Aperture Radar; interferometry; time series; performance analysis
Dr. Piyush Shanker Agram
E-Mail Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, Signal Analyst, MS 300-319, Pasadena, CA 91109, USA
Tel. +1-818-354-2751
Interests: radar interferometry; geospatial big data; deformation time series

Special Issue Information

Dear Colleagues,

With its weather-independent imaging capability, Synthetic Aperture Radar (SAR) is the ideal tool for continuous monitoring of the Earth’s surface. A number of major civilian SAR satellite missions have accumulated repeated observations over the last few decades. Today, especially thanks to the Sentinel-1 constellation, almost every corner of the Earth is covered routinely every 24 days in general and numerous high priority targets are imaged every 6–12 days in multiple imaging geometries. The global observation capability is growing rapidly, as new players are entering the SAR field. Alongside large space agencies, which have traditionally focused their efforts on building large and capable SAR instruments, relatively small and dynamic private companies are launching constellations of dozens of small SAR satellites, significantly increasing temporal sampling. Consequently, in addition to large missions like NISAR, SAOCOM, Tandem-L, etc., we expect to see rich datasets being acquired by small satellite constellations launched by organizations like Capella Space, Urthecast, Iceye, and NovaSAR. Another chapter will open once SAR geo–synchronous satellites are launched.

Temporal series of various radar-derived properties, such as backscatter, interferometric phase, interferometric coherence, and polarimetric decompositions, have been used to monitor a wide range of processes, like ice flows of glaciers, which are so important for understanding climate change; volcanic unrest (inflation/deflation) to predict eruptions; detecting changes occurring on the surface, from flooding events to very subtle ones. Permanent or persistent scatterer SAR interferometry in particular has been used to monitor the physical infrastructure of all kinds of structures, such as railways, roads, bridges, dams, or pipelines. Following the phase variations, one can reconstruct deformation with sub-centimeter to millimeter precision. SAR, Polarimetric SAR, and Interferometric SAR have also enabled monitoring of moisture content (soils, forests), with important implications for fire prevention, agriculture, crop monitoring, snow water equivalent estimation, grounding line delineation, permafrost monitoring, land use classification, and much more.

The benefits of improved sampling in the temporal dimension are manifold: It enhances our ability to follow and decouple physical processes as they develop in time, and allows us to measure rate of change with unprecedented precision and, in other cases, to enable advanced processing techniques that can preserve a finer spatial resolution.

Considering the processing capabilities available today, we are certainly facing a shortage of efficient algorithms that can fully exploit the potential of the large volumes of SAR data that are already being gathered. A whole spectrum of algorithms, ranging from heuristic approaches to physics-driven techniques, needs to be developed to maximize the return from these large datasets. While artificial intelligence and machine-learning approaches represent an important path forward, there is still room for developing techniques that build on explicit physical modeling of the interaction of the radar waves with the target of interest. Fusion of backscatter and interferometric phase information from almost contemporaneous multi-polarization, multi-frequency SAR data will further contribute to our understanding of the basic scattering of radar waves, opening up new avenues for SAR-related applications.

We are looking forward to receiving your contribution to this Special Issue on “Time Series Analysis Based on SAR Images”.

Dr. Francesco De Zan
Dr. Piyush Shanker Agram
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Synthetic Aperture Radar
  • time series
  • backscatter
  • interferometry
  • coherent change detection

Published Papers (5 papers)

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Open AccessArticle
A Novel Relational-Based Transductive Transfer Learning Method for PolSAR Images via Time-Series Clustering
Remote Sens. 2019, 11(11), 1358; https://doi.org/10.3390/rs11111358 - 06 Jun 2019
Abstract
The combination of transfer learning and remote sensing image processing technology can effectively improve the automation level of image information extraction from a remote sensing time series. However, in the processing of polarimetric synthetic aperture radar (PolSAR) time-series images, the existing transfer learning [...] Read more.
The combination of transfer learning and remote sensing image processing technology can effectively improve the automation level of image information extraction from a remote sensing time series. However, in the processing of polarimetric synthetic aperture radar (PolSAR) time-series images, the existing transfer learning methods often cannot make full use of the time-series information of the images, relying too much on the labeled samples in the target domain. Furthermore, the speckle noise inherent in synthetic aperture radar (SAR) imagery aggravates the difficulty of the manual selection of labeled samples, so these methods have difficulty in meeting the processing requirements of large data volumes and high efficiency. In lieu of these problems and the spatio-temporal relational knowledge of objects in time-series images, this paper introduces the theory of time-series clustering and proposes a new three-phase time-series clustering algorithm. Due to the full use of the inherent characteristics of the PolSAR images, this algorithm can accurately transfer the labels of the source domain samples to those samples that have not changed in the whole time series without relying on the target domain labeled samples, so as to realize transductive sample label transfer for PolSAR time-series images. Experiments were carried out using three different sets of PolSAR time-series images and the proposed method was compared with two of the existing methods. The experimental results showed that the transfer precision of the proposed method reaches a high level with different data and different objects and it performs significantly better than the existing methods. With strong reliability and practicability, the proposed method can provide a new solution for the rapid information extraction of remote sensing image time series. Full article
(This article belongs to the Special Issue Time Series Analysis Based on SAR Images)
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Open AccessArticle
A Novel Spatio-Temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images
Remote Sens. 2019, 11(8), 990; https://doi.org/10.3390/rs11080990 - 25 Apr 2019
Cited by 4
Abstract
In recent years, analyzing Synthetic Aperture Radar (SAR) data has turned into one of the challenging and interesting topics in remote sensing. Radar sensors are capable of imaging Earth’s surface independently of the weather conditions, local time of day, penetrating of waves through [...] Read more.
In recent years, analyzing Synthetic Aperture Radar (SAR) data has turned into one of the challenging and interesting topics in remote sensing. Radar sensors are capable of imaging Earth’s surface independently of the weather conditions, local time of day, penetrating of waves through clouds, and containing spatial information on agricultural crop types. Based on these characteristics, the main goal sought in this research is to reveal the SAR imaging data capability in recognizing various agricultural crops in the main growth season in a more clarified and detailed way by using a deep-learning-based method. In the present research, the multi-temporal C-band Sentinel 1 images were used to classify 14 major classes of agricultural crops plus background in Denmark. By considering the capability of a deep learning method in analyzing satellite images, a novel, optimal, and lightweight network structure was developed and implemented based on a combination of a fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM) network. The average pixel-based accuracy and Intersection over Union obtained from the proposed network were 86% and 0.64, respectively. Winter rapeseed, winter barley, winter wheat, spring barley, and sugar beet had the highest pixel-based accuracies of 95%, 94%, 93%, 90%, and 90%; respectively. The pixel-based accuracies for eight crop types and the background class were more than 84%. The network prediction showed that in field borders the classification confidence was lower than the center regions of the fields. However, the proposed structure has been able to identify different crops in multi-temporal Sentinel 1 data of a large area of around 254 thousand hectares with high performance. Full article
(This article belongs to the Special Issue Time Series Analysis Based on SAR Images)
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Open AccessArticle
Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks
Remote Sens. 2019, 11(6), 711; https://doi.org/10.3390/rs11060711 - 25 Mar 2019
Cited by 2
Abstract
Grassland contributes to carbon storage and animal feed production. Its yield is largely determined by the cutting times of grassland. Previous studies have used remote sensing data for grassland biomass estimation, but only a few studies have focused on SAR remote sensing approaches [...] Read more.
Grassland contributes to carbon storage and animal feed production. Its yield is largely determined by the cutting times of grassland. Previous studies have used remote sensing data for grassland biomass estimation, but only a few studies have focused on SAR remote sensing approaches for automatic grassland cutting status detection. Due to the occurrence of multiple cuttings in a year, it is crucial to effectively monitor grassland cutting events in order to achieve accurate biomass estimations of a whole season. In this study, we examined the capabilities of multilayer perceptron neural networks for automatic grassland cutting status detection using SAR imagery. The proposed model inputs are a time series dataset of VV and VH Sentinel-1 C-band SAR and second-order texture metrics (homogeneity, entropy, contrast and dissimilarity). The proposed approach has been successfully tested on a dataset collected from several fields in Germany in 2016, with an overall accuracy of 85.71% for the validation set. Full article
(This article belongs to the Special Issue Time Series Analysis Based on SAR Images)
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Open AccessArticle
Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series
Remote Sens. 2019, 11(4), 449; https://doi.org/10.3390/rs11040449 - 21 Feb 2019
Cited by 4
Abstract
Crop planting area mapping and phenology monitoring are of great importance to analyzing the impacts of climate change on agricultural production. In this study, crop planting area and phenology were identified based on Sentinel-1 backscatter time series in the test region of the [...] Read more.
Crop planting area mapping and phenology monitoring are of great importance to analyzing the impacts of climate change on agricultural production. In this study, crop planting area and phenology were identified based on Sentinel-1 backscatter time series in the test region of the North China Plain, East Asia, which has a stable cropping pattern and similar phenological stages across the region. Ground phenological observations acquired from a typical agro-meteorological station were used as a priori knowledge. A parallelepiped classifier processed VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving) backscatter signals in order to map the winter wheat planting area. An accuracy assessment showed that the total classification accuracy reached 84% and the Kappa coefficient was 0.77. Both the difference ( σ d ) between VH and VV and its slope were obtained to contrast with a priori knowledge and then used to extract the phenological metrics. Our findings from the analysis of the time series showed that the seedling, tillering, overwintering, jointing, and heading of winter wheat may be closely related to σ d and its slope. Overall, this study presents a generalizable methodology for mapping the winter wheat planting area and monitoring phenology using Sentinel-1 backscatter time series, especially in areas lacking optical remote sensing data. Our results suggest that the main change in Sentinel-1 backscatter is dominated by the vegetation canopy structure, which is different from the established methods using optical remote sensing data, and it is available for phenological metrics extraction. Full article
(This article belongs to the Special Issue Time Series Analysis Based on SAR Images)
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Open AccessTechnical Note
K-Matrix: A Novel Change-Pattern Mining Method for SAR Image Time Series
Remote Sens. 2019, 11(18), 2161; https://doi.org/10.3390/rs11182161 - 17 Sep 2019
Abstract
In this paper, we present a novel method for change-pattern mining in Synthetic Aperture Radar (SAR) image time series based on a distance matrix clustering algorithm, called K-Matrix. As it is different from the state-of-the-art methods, which analyze the SAR image time series [...] Read more.
In this paper, we present a novel method for change-pattern mining in Synthetic Aperture Radar (SAR) image time series based on a distance matrix clustering algorithm, called K-Matrix. As it is different from the state-of-the-art methods, which analyze the SAR image time series based on the change detection matrix (CDM), here, we directly use the distance matrix to determine changed pixels and extract change patterns. The proposed scheme involves two steps: change detection in SAR image time series and change-pattern discovery. First, these distance matrices are constructed for each spatial position over the time series by a dissimilarity measurement. The changed pixels are detected by using a thresholding algorithm on the energy feature map of all distance matrices. Then, according to the change detection results in SAR image time series, the changed areas for pattern mining are determined. Finally, the proposed K-Matrix algorithm which clusters distance matrices by the matrix cross-correlation similarity is used to group all changed pixels into different change patterns. Experimental results on two datasets of TerraSAR-X image time series illustrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Time Series Analysis Based on SAR Images)
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