Tag: SAR

IEEE-GRSS Distinguished Lecturer

Posted on by Carlos López-Martínez in Teaching

From July 1st, 2020, and for a period of two years, I have been appointed as Distinguished Lecturer of the IEEE-GRSS Geoscience and Remote Sensing Society.

The Distinguished Lecturer Program is a service of the GRSS and its members to
support the chapter activities. The goal is to provide chapters with access to with
leading professionals in geoscience and remote sensing and discuss novel topics in
current research. This is an opportunity for the GRSS membership to hear interesting
talks about work being done in in geoscience and remote sensing.

In particular, I will take car of the following topics:

  • Basics of SAR Polarimetry,
  • SAR Polarimetry: Theory and Applications,
  • SAR, SAR Polarimetry & Multitemporal #SAR Statistical Description

➲ More information

Polarimetric SAR Time Series Change Analysis Over Agricultural Areas

Posted on by Carlos López-Martínez in Publication

A. Alonso-González, C. López-Martínez, K. P. Papathanassiou and I. Hajnsek, “Polarimetric SAR Time Series Change Analysis Over Agricultural Areas,” in IEEE Transactions on Geoscience and Remote Sensing, Early Access, 2020

➲ Open access full paper

Summary

This article proposes a change detection and analysis technique for monitoring the phenological development of agricultural vegetation by means of multitemporal Polarimetric Synthetic Aperture Radar (PolSAR) acquisitions. The technique relies on the generalized eigendecomposition of the polarimetric covariance matrices of the individual acquisitions. It both quantifies the magnitude of the change between PolSAR images acquired at different times and also provides an interpretation of occurred change in terms of the modified polarization states. This makes the algorithm suitable for investigating scattering dynamics associated with the phenological development of agricultural vegetation. To aid the interpretation of the changes detected, a representation based on the polarization states affected by the change process is proposed. The technique is evaluated using part of the multitemporal AGRISAR 2006 campaign data set. This data set consists of 12 quad-polarimetric images acquired by the German Aerospace Center (DLR) E-SAR airborne system at L-band from April 2006 to August 2006 over the Demmin test site. It covers large parts of the development cycle of different crop types. As a part of the evaluation, reference ground measurements are used to facilitate the interpretation of the data. The evaluation focuses on five important crop types: wheat, barley, rape, maize, and sugar beet. The results show that the proposed technique is able to detect and characterize different types of changes related to distinct development states of different crop types as the plant growing, maturation, and drying processes.

Sentinel-1 InSAR Coherence for LandCover Mapping: A comparison of multiple feature-based classifiers

Posted on by Carlos López-Martínez in Publication

Jacob, A., Vicente-Guijalba, F., Lopez-Martinez, C., Lopez-Sanchez, J.M., Litzinger, M., Kristen, H., Mestre-Quereda, A., Ziolkowski, D., Lavalle, M., Notarnicola, C., Suresh, G., Antropov, O., Ge, S., Praks, J., Ban, Y., Pottier, E., Mallorqui, J., Duro, J. & Engdahl, M., “Sentinel-1 InSAR Coherence for LandCover Mapping: A comparison of multiple feature-based classifiers”, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 13, pp. 535-552, 2020.

➲ Open access full paper

Summary

This work investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyses the performance of this feature along with polarisation and intensity products according to different classification strategies and algorithms. Seven different classification work flows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain: interferometric coherence, backscattered intensities and polarisation. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximise diversity land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Doñana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%), obtained also promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherencebased results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinels-1A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1A/B stacks, i.e., 6-day sampling, are considered.

Large-Scale automatic vessel monitoring based on dual-polarization Sentinel-1 and AIS data

Posted on by Carlos López-Martínez in Publication

Pelich, R.; Chini, M.; Hostache, R.; Matgen, P.; Lopez-Martinez, C.; Nuevo, M.; Ries, P.; Eiden, G. “Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data”. Remote Sens. 2019, 11, 1078.

➲ Open access full paper

Summary

This research addresses the use of dual-polarimetric descriptors for automatic large-scale ship detection and characterization from synthetic aperture radar (SAR) data. Ship detection is usually performed independently on each polarization channel and the detection results are merged subsequently. In this study, we propose to make use of the complex coherence between the two polarization channels of Sentinel-1 and to perform vessel detection in this domain. Therefore, an automatic algorithm, based on the dual-polarization coherence, and applicable to entire large scale SAR scenes in a timely manner, is developed. Automatic identification system (AIS) data are used for an extensive and also large scale cross-comparison with the SAR-based detections. The comparative assessment allows us to evaluate the added-value of the dual-polarization complex coherence, with respect to SAR intensity images in ship detection, as well as the SAR detection performances depending on a vessel’s size. The proposed methodology is justified statistically and tested on Sentinel-1 data acquired over two different and contrasting, in terms of traffic conditions, areas: the English Channel the and Pacific coastline of Mexico. The results indicate a very high SAR detection rate, i.e., >80%, for vessels larger than 60 m and a decrease of detection rate up to 40% for smaller size vessels. In addition, the analysis highlights many SAR detections without corresponding AIS positions, indicating the complementarity of SAR with respect to cooperative sources for detecting dark vessels.

Towards a 20 m Global Building Map from Sentinel-1 SAR Data

Posted on by Carlos López-Martínez in Publication

Chini, M.; Pelich, R.; Hostache, R.; Matgen, P.; Lopez-Martinez, C. “Towards a 20 m Global Building Map from Sentinel-1 SAR Data”. Remote Sens. 2018, 10, 1833.

➲ Open access full paper

Summary

This study introduces a technique for automatically mapping built-up areas using synthetic aperture radar (SAR) backscattering intensity and interferometric multi-temporal coherence generated from Sentinel-1 data in the framework of the Copernicus program. The underlying hypothesis is that, in SAR images, built-up areas exhibit very high backscattering values that are coherent in time. Several particular characteristics of the Sentinel-1 satellite mission are put to good use, such as its high revisit time, the availability of dual-polarized data, and its small orbital tube. The newly developed algorithm is based on an adaptive parametric thresholding that first identifies pixels with high backscattering values in both VV and VH polarimetric channels. The interferometric SAR coherence is then used to reduce false alarms. These are caused by land cover classes (other than buildings) that are characterized by high backscattering values that are not coherent in time (e.g., certain types of vegetated areas). The algorithm was tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. The resulting building maps were compared with the Global Urban Footprint (GUF) derived from the TerraSAR-X mission data and, on average, a 92% agreement was obtained.