Selected demographic indicators from the United Nations' world population prospects, the 2008 revision

In this article, the problem of synthetic aperture radar (SAR) images coregistration is considered. In particular, a novel algorithm aimed at achieving a fine subpixel coregistration accuracy is developed. The procedure is based on the parabolic interpolation of the 2-D cross correlation computed between the two SAR images to be aligned. More precisely, from the 2-D cross correlation, a neighborhood of its peak value is extracted and the interpolation of both the 2-D paraboloid and the two alternative 1-D parabolas is computed to provide the finer misregistration estimation with subpixel accuracy. The main advantage of the proposed framework is that the overall computational burden is only due to the 2-D cross correlation estimation since the parabolic interpolation is calculated with a closed-form expression. The results obtained on real recorded unmanned aerial vehicle (UAV) SAR data highlight the effectiveness of the proposed approach as well as its capabilities to provide some benefits with respect to other available strategies.

Subpixel SAR Image Registration Through Parabolic Interpolation of the 2-D Cross Correlation

This work presents an overview of the multiple aperture synthetic aperture radar interferometric (MAI) technique, which is primarily used to measure the along-track components of the Earth’s surface deformation, by investigating its capabilities and potential applications. Such a method is widely used to monitor the time evolution of ground surface changes in areas with large deformations (e.g., due to glaciers movements or seismic episodes), permitting one to discriminate the three-dimensional (up–down, east–west, north–south) components of the Earth’s surface displacements. The MAI technique relies on the spectral diversity (SD) method, which consists of splitting the azimuth (range) Synthetic Aperture RADAR (SAR) signal spectrum into separate sub-bands to get an estimate of the surface displacement along the azimuth (sensor line-of-sight (LOS)) direction. Moreover, the SD techniques are also used to correct the atmospheric phase screen (APS) artefacts (e.g., the ionospheric and water vapor phase distortion effects) that corrupt surface displacement time-series obtained by currently available multi-temporal InSAR (MT-InSAR) tools. More recently, the SD methods have also been exploited for the fine co-registration of SAR data acquired with the Terrain Observation with Progressive Scans (TOPS) mode. This work is primarily devoted to illustrating the underlying rationale and effectiveness of the MAI and SD techniques as well as their applications. In addition, we present an innovative method to combine complementary information of the ground deformation collected from multi-orbit/multi-track satellite observations. In particular, the presented technique complements the recently developed Minimum Acceleration combination (MinA) method with MAI-driven azimuthal ground deformation measurements to obtain the time-series of the 3-D components of the deformation in areas affected by large deformation episodes. Experimental results encompass several case studies. The validity and relevance of the presented approaches are clearly demonstrated in the context of geospatial analyses.

The Multiple Aperture SAR Interferometry (MAI) Technique for the Detection of Large Ground Displacement Dynamics: An Overview

Departments are critical intervention points for enhancing diversity in any academic discipline, yet their experiences related to diversity differ widely. This article explores how several geography departments that vary by region, setting, and institutional type have experienced and promoted diversity. We also explore geography at different types of institutions, particularly minority-serving institutions and land-grant colleges and universities. We conclude that plans for improving the recruitment and retention of diverse students and faculty should make explicit the connection between structural factors, such as institutional contexts, and the agency of geography departments as key actors.

Diverse Experiences in Diversity at the Geography Department Scale

Advances in synthetic aperture radar (SAR) interferometry have enabled the seamless monitoring of the Earth’s crust deformation. The dense archive of the Sentinel-1 Copernicus mission provides unprecedented spatial and temporal coverage; however, time-series analysis of such big data volumes requires high computational efficiency. We present a parallelized-PSI (P-PSI), a novel, parallelized, and end-to-end processing chain for the fully automated assessment of line-of-sight ground velocities through persistent scatterer interferometry (PSI), tailored to scale to the vast multitemporal archive of Sentinel-1 data. P-PSI is designed to transparently access different and complementary Sentinel-1 repositories, and download the appropriate datasets for PSI. To make it efficient for large-scale applications, we re-engineered and parallelized interferogram creation and multitemporal interferometric processing, and introduced distributed implementations to best use computing cores and provide resourceful storage management. We propose a new algorithm to further enhance the processing efficiency, which establishes a non-uniform patch grid considering land use, based on the expected number of persistent scatterers. P-PSI achieves an overall speed-up by a factor of five for a full Sentinel-1 frame for processing in a 20-core server. The processing chain is tested on a large-scale project to calculate and monitor deformation patterns over the entire extent of the Greek territory—our own Interferometric SAR (InSAR) Greece project. Time-series InSAR analysis was performed on volumes of about 12 TB input data corresponding to more than 760 Single Look Complex Sentinel-1A and B images mostly covering mainland Greece in the period of 2015–2019. InSAR Greece provides detailed ground motion information on more than 12 million distinct locations, providing completely new insights into the impact of geophysical and anthropogenic activities at this geographic scale. This new information is critical to enhancing our understanding of the underlying mechanisms, providing valuable input into risk assessment models. We showcase this through the identification of various characteristic geohazard locations in Greece and discuss their criticality. The selected geohazard locations, among a thousand, cover a wide range of catastrophic events including landslides, land subsidence, and structural failures of various scales, ranging from a few hundredths of square meters up to the basin scale. The study enriches the large catalog of geophysical related phenomena maintained by the GeObservatory portal of the Center of Earth Observation Research and Satellite Remote Sensing BEYOND of the National Observatory of Athens for the opening of new knowledge to the wider scientific community.

InSAR Greece with Parallelized Persistent Scatterer Interferometry: A National Ground Motion Service for Big Copernicus Sentinel-1 Data

Synthetic aperture radar (SAR) image co-registration is one of the essential steps for interferometric SAR processing and the following multitemporal analysis. All acquisitions from time-series images with the Sentinel-1 Terrain Observation by Progressive Scans (TOPS) model should be co-registrated to a common reference geometry with an accuracy of approximately 0.001 pixels. Such a high accuracy can be achieved by correcting the residual azimuth mis-registration using the Enhanced Spectral Diversity (ESD) technique. However, the performance of the ESD depends on the coherence of image pairs, especially over fast decorrelation areas. To improve the coherence between acquisitions, we develop a joint co-registration procedure based on the minimum-spanning-tree algorithm and iterative reweighted least squares. In order to further improve the accuracy of the azimuth mis-registration over low-coherence scenes, we present a coherence estimator by combining two consecutive bursts of SLC samples to reduce both bias and variance. The proposed method is tested over two low-coherence scenes and compared to the network-based ESD approach. The results from both synthetic and real data demonstrated the advantages of our method.

Minimum Spanning Tree Co-registration Approach for Time-Series Sentinel-1 TOPS Data

Urban change detection analysis utilizing multiresolution texture features from polarimetric SAR images

Synthetic Aperture Radar Interferometry (SAR, InSAR) is increasingly being used for deformation monitoring. Uncertainty in satellite state vectors is considered to be one of the main sources of errors in applications such as this. In this paper, we present frequency and spatial domain based algorithms to model orbital errors in InSAR interferograms. The main advantage of this method, when applied to the spatial domain, is that the order of the polynomial coefficient is automatically determined according to the features of the orbital errors, using K-cross validation. In the frequency domain, a maximum likelihood fringe rate estimate is deployed to resolve linear orbital patterns in strong noise interferograms, where spatial-domain-based algorithms are unworkable. Both methods were tested and compared with synthetic data and applied to historical Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) sensor and modern instruments such as Gaofen-3 (GF-3) and Sentinel-1. The validation from the simulation demonstrated that an accuracy of ~1mm can be obtained under optimal conditions. Using an independent GPS measurement that is discontinuous from the InSAR measurement over the Tohoku-Oki area, we found a 31.45% and 73.22% reduction in uncertainty after applying our method for ASAR tracks 347 and 74, respectively.

Modeling Orbital Error in InSAR Interferogram Using Frequency and Spatial Domain Based Methods

This study examines the role Historically Black Colleges and Universities (HBCUs) play in the GIS educational landscape in the United States. Research of HBCU institutional web sites was used to develop a database of GIS and related geospatial courses, concentrations, degrees, and departments offering these courses. The review of this information indicated strong differences in access to GIS education for students between rural and urban settings, community colleges and universities, and private and public HBCUs. The availability of GIS technology at HBCUs and at other institutions of higher education was also contrasted to examine the existence of a technology divide and data show that HBCUs have kept in step with other institutions in introducing GIS into their curriculum. Information from a similar study conducted almost a decade ago (Padgett and Crayton 2001) was used to determine advances in GIS educational offerings at HBCUs over the recent past. As employability trends for geography graduates show increasing emphasis on transferable technical skills—like GIS, the availability of geospatial courses, programs, and certifications at HBCUs is critical to increasing diversity in geography as a profession and, in the long run, the vitality of geography as a discipline. Unfortunately, the advent of GIS has not had any material impact on the establishment of core geography programs at HBCUs.

GIS Educational Opportunities at Historically Black Colleges and Universities in the United States

As the global urban population grows due to the influx of migrants from rural areas, many cities in developing countries face the emergence and proliferation of unplanned and informal settlements. However, even though the rise of unplanned development influences planning and management of residential land-use, reliable and detailed information about these areas is often scarce. While formal settlements in urban areas are easily mapped due to their distinct features, this does not hold true for informal settlements because of their microstructure, instability, and variability of shape and texture. Therefore, detecting and mapping these areas remains a challenging task. This research will contribute to the development of tools to identify such informal built-up areas by using an integrated approach of multiscale deep learning. The authors propose a composite architecture for semantic segmentation using the U-net architecture aided by information obtained from a multiscale contourlet transform. This work also analyzes the effects of wavelet and contourlet decompositions in the U-net architecture. The performance was evaluated in terms of precision, recall, F-score, mean intersection over union, and overall accuracy. It was found that the proposed method has better class-discriminating power as compared to existing methods and has an overall classification accuracy of 94.9-95.7%.

Rakesh Malhotra

Papers

Minimum Spanning Tree Co-registration Approach for Time-Series Sentinel-1 TOPS Data

Urban change detection analysis utilizing multiresolution texture features from polarimetric SAR images

Modeling Orbital Error in InSAR Interferogram Using Frequency and Spatial Domain Based Methods

GIS Educational Opportunities at Historically Black Colleges and Universities in the United States

Identifying Informal Settlements Using Contourlet Assisted Deep Learning