Image fusion techniqes for remote sensing applications

Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the Synthetic Aperture Radar (SAR) Interferometry, where a pair of antennas are used to obtain an elevation map of the observed scene; the second one refers to the fusion of multisensor and multitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the third one presents a processor to fuse multifrequency, multipolarization and mutiresolution SAR images, based on wavelet transform and multiscale Kalman filter. Each study case presents also results achieved by the proposed techniques applied to real data

A comparison of phase unwrapping techniques in synthetic aperture radar interferometry

A comparison of different phase unwrapping techniques based on the least mean square error is presented. A testing environment based on simulated interferograms has been created in order to assess the methods described in the literature. Each of them has shown good properties under different constraints. Multigrid with a previous adaptive maximum likelihood gradient estimation is very robust when strong aliasing is not expected. In a general scenario with aliasing, an adaptive multiresolution gradient estimator gives a coarse approximation to the low resolution topography.Peer ReviewedPostprint (published version

Three-Dimensional Nepal Earthquake Displacement Using Hybrid Genetic Algorithm Phase Unwrapping from Sentinel-1A Satellite

Introduction: Geophysicists had forewarned for decades that Nepal was exposed to a deadly earthquake, exceptionally despite its geology, urbanization and architecture. Gorkha earthquake is the most horrible natural disaster to crash into Nepal since the 1934 Nepal-Bihar earthquake. Gorkha earthquake occurred on April 25, 2015, at 11:56 NST and killed more than 10,000 people and injured more than 23,000 population. Objective: The main objective of this work is to utilize hybrid genetic algorithm for three-dimensional phase unwrapping of Nepal earthquake displacement using Sentinel-1A satellite. The three-dimensional best-path avoiding singularity loops (3DBPASL) algorithm was implemented to perform 3D Sentinel-1A satellite phase unwrapping. The hybrid genetic algorithm (HGA) was used to achieve 3DBPASL phase matching. Advancely, the errors in phase decorrelation were reduced by optimization of 3DBPASL using HGA. Results: The findings indicate a few cm of ground deformation and vertical northern of Kathmandu. Approximately, an area of 12,000 km2 has been drifted also the northern of Kathmandu. Further, each fringe of colour represents about 2.5 cm of deformation. The large amount of fringes indicates a large deformation pattern with ground motions of 3 m. Conclusion: In conclusion, HGA can be used to produce accurate 3D quake deformation using Sentinel-1A satellite

Fusion of Urban TanDEM-X raw DEMs using variational models

Recently, a new global Digital Elevation Model (DEM) with pixel spacing of 0.4 arcseconds and relative height accuracy finer than 2m for flat areas (slopes 20%) was created through the TanDEM-X mission. One important step of the chain of global DEM generation is to mosaic and fuse multiple raw DEM tiles to reach the target height accuracy. Currently, Weighted Averaging (WA) is applied as a fast and simple method for TanDEM-X raw DEM fusion in which the weights are computed from height error maps delivered from the Interferometric TanDEM-X Processor (ITP). However, evaluations show that WA is not the perfect DEM fusion method for urban areas especially in confrontation with edges such as building outlines. The main focus of this paper is to investigate more advanced variational approaches such as TV-L1 and Huber models. Furthermore, we also assess the performance of variational models for fusing raw DEMs produced from data takes with different baseline configurations and height of ambiguities. The results illustrate the high efficiency of variational models for TanDEM-X raw DEM fusion in comparison to WA. Using variational models could improve the DEM quality by up to 2m particularly in inner-city subsets.Comment: This is the pre-acceptance version, to read the final version, please go to IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing on IEEE Xplor

Assessment of Landslide-Induced Geomorphological Changes in Hítardalur Valley, Iceland, Using Sentinel-1 and Sentinel-2 Data

Publisher's version (útgefin grein)Landslide mapping and analysis are essential aspects of hazard and risk analysis. Landslides can block rivers and create landslide-dammed lakes, which pose a significant risk for downstream areas. In this research, we used an object-based image analysis approach to map geomorphological features and related changes and assess the applicability of Sentinel-1 data for the fast creation of post-event digital elevation models (DEMs) for landslide volume estimation. We investigated the Hítardalur landslide, which occurred on the 7 July 2018 in western Iceland, along with the geomorphological changes induced by this landslide, using optical and synthetic aperture radar data from Sentinel-2 and Sentinel-1. The results show that there were no considerable changes in the landslide area between 2018 and 2019. However, the landslide-dammed lake area shrunk between 2018 and 2019. Moreover, the Hítará river diverted its course as a result of the landslide. The DEMs, generated by ascending and descending flight directions and three orbits, and the subsequent volume estimation revealed that-without further post-processing-the results need to be interpreted with care since several factors influence the DEM generation from Sentinel-1 imagery.This research has been supported by the Austrian Science Fund (FWF) through the project MORPH (Mapping, monitoring and modelling the spatio-temporal dynamics of land surface morphology; FWF-P29461-N29) and the Doctoral Collage GIScience (DKW1237-N23), as well as by the Austrian Academy of Sciences (?AW) through the project RiCoLa (Detection and analysis of landslide-induced river course changes and lake formation).Peer Reviewe