scholarly journals COMBINATION OF SPECKLE DIVERGENCE AND NEIGHBORHOOD ANALYSIS TO CLASSIFY SETTLEMENT FROM TERASAR-X DATA

2012 ◽  
Vol 9 (1) ◽  
Author(s):  
Rokhis Komarudin ◽  
Agung Indrajit
Keyword(s):  
Specific Area ◽  
Temporal Analysis ◽  
Radar Data ◽  
Tropical Country ◽  
Neighborhood Analysis ◽  
Object Based ◽  
Radar Imagery ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Spatial Indexes

Abstract.  The  objectives  of  this  research  were  to  develop  and  improve  methods  for determination  of  settlements  area  with  focus  on  synthetic  aperture  radar  (SAR)  data. Remote  sensing  settlement  classification  has  made  great  progress,  both  for  optical  and radar  data  as  well  for  their  fusion.  Yet,  in  radar  imagery,  settlement  classification  still contains  some  problems.  Several  studies  on  application  of  radar  imagery  have  been conducted  using  techniques  such  as  textural  analysis,  multi-temporal  analysis,  statistical model,  spatial  indexes,  and  object-based  classification.  Most  of  the  development  methods have several problems in the specific area especially in the tropical country. Several studies also  showed  that  settlement  classification  accuracies  were  just  below  60%.    This  was  not sufficient    enough  to  classify  settlement  areas  using  SAR  imagery.  Therefore,  in  this research, we proposed a new method i.e., the combination of the speckle divergence and the neighborhood  analysis.  The  proposed  method  was  applied  to  classify  settlement  area  in Cilacap  and  Padang  Districts  of  Indonesia.  The  results  showed  that  the  proposed  method produced a good accuracy i.e., 85.5% for Cilacap Districts and 78.1% for Padang Districts. 

scholarly journals A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery

2020 ◽  
Vol 12 (6) ◽  
pp. 961 ◽  
Author(s):  
Marinalva Dias Soares ◽  
Luciano Vieira Dutra ◽  
Gilson Alexandre Ostwald Pedro da Costa ◽  
Raul Queiroz Feitosa ◽  
Rogério Galante Negri ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Data Classification ◽  
Test Site ◽  
Radar Data ◽  
Input Image ◽  
Optical Data ◽  
Land Use Classification ◽  
Object Based ◽  
Sar Imagery ◽  
Small Change

Per-point classification is a traditional method for remote sensing data classification, and for radar data in particular. Compared with optical data, the discriminative power of radar data is quite limited, for most applications. A way of trying to overcome these difficulties is to use Region-Based Classification (RBC), also referred to as Geographical Object-Based Image Analysis (GEOBIA). RBC methods first aggregate pixels into homogeneous objects, or regions, using a segmentation procedure. Moreover, segmentation is known to be an ill-conditioned problem because it admits multiple solutions, and a small change in the input image, or segmentation parameters, may lead to significant changes in the image partitioning. In this context, this paper proposes and evaluates novel approaches for SAR data classification, which rely on specialized segmentations, and on the combination of partial maps produced by classification ensembles. Such approaches comprise a meta-methodology, in the sense that they are independent from segmentation and classification algorithms, and optimization procedures. Results are shown that improve the classification accuracy from Kappa = 0.4 (baseline method) to a Kappa = 0.77 with the presented method. Another test site presented an improvement from Kappa = 0.36 to a maximum of 0.66 also with radar data.

scholarly journals Refocusing of Multiple Moving Targets Based on the Joint Sparse Processing of One Channel Synthetic Aperture Radar Imagery Patches

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1215 ◽  
Author(s):  
Xin Wang ◽  
Ling Qiao
Keyword(s):  
Synthetic Aperture Radar ◽  
Moving Objects ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Moving Targets ◽  
Lp Norm ◽  
Radar Imagery ◽  
Sar Imagery ◽  
Norm Constraint ◽  
Aperture Radar

A sparse-based refocusing methodology for multiple slow-moving targets (MTs) located inside strong clutter regions is proposed in this paper. The defocused regions of MTs in synthetic aperture radar (SAR) imagery were utilized here instead of the whole original radar data. A joint radar projection operator for the static and moving objects was formulated and employed to construct an optimization problem. The Lp norm constraint was utilized to promote the separation of MT data and the suppression of clutter. After the joint sparse imaging processing, the energy of strong static targets could be suppressed significantly in the reconstructed MT imagery. The static scene imagery could be derived simultaneously without the defocused MT. Finally, numerical simulations were used verify the validity and robustness of the proposed methodology.

scholarly journals APPLICABILITY OF MULTI-SEASONAL X-BAND SAR IMAGERY FOR MULTIRESOLUTION SEGMENTATION: A CASE STUDY IN A RIPARIAN MIXED FOREST

2015 ◽  
Vol XL-1-W5 ◽  
pp. 123-128
Author(s):  
Z. Dabiri ◽  
D. Hölbling ◽  
S. Lang ◽  
A. Bartsch
Keyword(s):  
Scale Parameter ◽  
Spatial Scales ◽  
Mixed Forest ◽  
Scale Parameters ◽  
Image Objects ◽  
Object Based ◽  
Multi Temporal ◽  
Reference Map ◽  
Sar Imagery ◽  
Multiresolution Segmentation

The increasing availability of synthetic aperture radar (SAR) data from a range of different sensors necessitates efficient methods for semi-automated information extraction at multiple spatial scales for different fields of application. The focus of the presented study is two-fold: 1) to evaluate the applicability of multi-temporal TerraSAR-X imagery for multiresolution segmentation, and 2) to identify suitable Scale Parameters through different weighing of different homogeneity criteria, mainly colour variance. Multiresolution segmentation was used for segmentation of multi-temporal TerraSAR-X imagery, and the ESP (Estimation of Scale Parameter) tool was used to identify suitable Scale Parameters for image segmentation. The validation of the segmentation results was performed using very high resolution WorldView-2 imagery and a reference map, which was created by an ecological expert. The results of multiresolution segmentation revealed that in the context of object-based image analysis the TerraSAR-X images are applicable for generating optimal image objects. Furthermore, ESP tool can be used as an indicator for estimation of Scale Parameter for multiresolution segmentation of TerraSAR-X imagery. Additionally, for more reliable results, this study suggests that the homogeneity criterion of colour, in a variance based segmentation algorithm, needs to be set to high values. Setting the shape/colour criteria to 0.005/0.995 or 0.00/1 led to the best results and to the creation of adequate image objects.

scholarly journals ANALYSES OF MULTI-YEAR SYNTHETIC APERTURE RADAR IMAGERY OF DRY-FALLEN INTERTIDAL FLATS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 941-947 ◽  
Author(s):  
M. Gade ◽  
S. Melchionna ◽  
L. Kemme
Keyword(s):  
Synthetic Aperture Radar ◽  
Morphological Changes ◽  
Temporal Analysis ◽  
Synthetic Aperture ◽  
Mussel Beds ◽  
Intertidal Flats ◽  
Show Evidence ◽  
Radar Imagery ◽  
Sar Imagery ◽  
Aperture Radar

We analyzed a great deal of high-resolution Synthetic Aperture Radar (SAR) data of dry-fallen intertidal flats in the German Wadden Sea with respect to the imaging of sediments, macrophytes, and mussels. TerraSAR-X and Radarsat-2 images of five test areas along the German North Sea coast acquired between 2008 and 2013 form the basis for the present investigation and are used to demonstrate that pairs of SAR images, if combined through basic algebraic operations, can already provide useful indicators for morphological changes and for bivalve (oyster and mussel) beds. Depending on the type of sediment, but also on the water level and on environmental conditions (wind speed) exposed sediments may show up on SAR imagery as areas of enhanced, or reduced, radar backscattering. The (multi-temporal) analysis of series of such images allows for the detection of mussel beds, and our results show evidence that also single-acquisition, multi-polarization SAR imagery can be used for that purpose.

scholarly journals BACKSCATTER ANALYSIS USING MULTI-TEMPORAL SENTINEL-1 SAR DATA FOR CROP GROWTH OF MAIZE IN KONYA BASIN, TURKEY

2018 ◽  
Vol XLII-3 ◽  
pp. 9-13 ◽  
Author(s):  
S. Abdikan ◽  
A. Sekertekin ◽  
M. Ustunern ◽  
F. Balik Sanli ◽  
R. Nasirzadehdizaji
Keyword(s):  
Land Use ◽  
Plant Height ◽  
Stem Elongation ◽  
Test Site ◽  
Central Anatolia ◽  
Crop Fields ◽  
Object Based ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Konya Basin

Temporal monitoring of crop types is essential for the sustainable management of agricultural activities on both national and global levels. As a practical and efficient tool, remote sensing is widely used in such applications. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was utilized to investigate the performance of the sensor backscatter image on crop monitoring. Multi-temporal C-band VV and VH polarized SAR images were acquired simultaneously by in-situ measurements which was conducted at Konya basin, central Anatolia Turkey. During the measurements, plant height of maize plant was collected and relationship between backscatter values and plant height was analysed. The maize growth development was described under Biologische Bundesanstalt, bundessortenamt und CHemische industrie (BBCH). Under BBCH stages, the test site was classified as leaf development, stem elongation, heading and flowering in general. The correlation coefficient values indicated high correlation for both polarimetry during the early stages of the plant, while late stages indicated lower values in both polarimetry. As a last step, multi-temporal coverage of crop fields was analysed to map seasonal land use. To this aim, object based image classification was applied following image segmentation. About 80 % accuracies of land use maps were created in this experiment. As preliminary results, it is concluded that Sentinel-1 data provides beneficial information about plant growth. Dual-polarized Sentinel-1 data has high potential for multi-temporal analyses for agriculture monitoring and reliable mapping.

scholarly journals Spaceborne radar imagery – An under-utilized source of information for humanitarian relief

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Andreas Braun Braun
Keyword(s):  
Radar Data ◽  
Humanitarian Relief ◽  
Humanitarian Action ◽  
Radar Images ◽  
Radar Imagery ◽  
Multi Temporal ◽  
Radar Satellite ◽  
Spaceborne Radar ◽  
Source Of Information ◽  
Humanitarian Work

This practical paper gives an overview about the widely unused potential of radar satellite imagery to assist humanitarian action. It briefly introduces the basic differences between optical and radar images and demonstrates the practical use of the latter in different settings based on their information content and their potential for multi-temporal analyses. It gives recommendations on further reading and closes with suggestions on the practical integration of radar data into humanitarian work.

scholarly journals Multi-Temporal InSAR Analysis for Monitoring Ground Deformation in Amorgos Island, Greece

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 338 ◽  
Author(s):  
Stavroula Alatza ◽  
Ioannis Papoutsis ◽  
Demitris Paradissis ◽  
Charalampos Kontoes ◽  
Gerassimos A. Papadopoulos
Keyword(s):  
Ground Deformation ◽  
Temporal Analysis ◽  
Small Scale ◽  
Data Sets ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterer ◽  
Spatial Coverage ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Small Baseline

Radar Interferometry is a widely used method for estimating ground deformation, as it provides precision to a few millimeters to centimeters, and at the same time, a wide spatial coverage of the study area. On 9 July 1956, one of the strongest earthquakes of the 20th century in the area of the South Aegean, occurred in Amorgos, with a magnitude of Mw = 7.7. The objective of this research is to map ground deformation in Amorgos island, using InSAR techniques. We conducted a multi-temporal analysis of all available data from 2003 to 2019 by exploiting historical ENVISAT SAR imagery, as well as the dense archive of Sentinel-1 SLC imagery. Persistent Scatterer Interferometry (PS) and Small Baseline Subset (SBAS) methods were implemented. Results of both data-sets indicate a small-scale deformation on the island. A multi-track analysis was implemented on Sentinel-1 data to decompose the line of sight velocities to vertical and horizontal. The central south coast is experiencing horizontal movement, while uplift of a maximum value of 5 mm/y is observed in the southeastern coast. The combination of the good spatial coverage achievable via InSAR, with GPS measurements, is suggested an important tool for the seamless monitoring of Amorgos island towards tectonic hazard estimation.

scholarly journals MULTI-TEMPORAL ANALYSIS OF DENSE AND SPARSE FORESTS’ RADAR BACKSCATTER USING SENTINEL-1A COLLECTION IN GOOGLE EARTH ENGINE

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 23-30
Author(s):  
C. M. Arellano ◽  
A. A. Maralit ◽  
E. C. Paringit ◽  
C. J. Sarmiento ◽  
R. A. Faelga ◽  
...  
Keyword(s):  
Data Extraction ◽  
Temporal Analysis ◽  
Radar Data ◽  
The Philippines ◽  
Google Earth ◽  
Radar Backscatter ◽  
Multi Temporal ◽  
Climatic Drivers ◽  
Google Earth Engine ◽  
Computing Platforms

Abstract. Radar data has been historically expensive and complex to process. However, in this milieu of cloud-computing platforms and open-source datasets, radar data analysis has become convenient and can now be performed for more exploratory researches. This study aims to perform multi-temporal analysis of radar backscatter to characterize dense and sparse forest from Sentinel-1 images. The area of study are reforested sites under the National Greening Program (NGP) of the Philippines. Ground data were collected: (1) in 2019, from a 1.35 ha -site in Brgy. Calula, Ipil, Zamboanga Sibugay, (2) in 2019, from a 1.10 ha- site in Brgy. Cabatuanan, Basay, Negros Oriental, and (3) from PhilLiDAR 2 – Project 3: FRExLS’ 2.4 ha -validated site in Ubay, Bohol. SAR intensity values were derived from Sentinel-1 from Google Earth Engine, which is a cloud-based platform with a repository of satellite images and functionalities for data extraction and processing. The temporal variation in C-band radar backscatter from 2014 to 2018 were analyzed. The results show, for the whole period of analysis, that: in VH polarization, dense forest samples backscatter range from −11 to −18 dB in VH and −2 to -13 dB in VV; sparse forest samples range from −12 to -21 dB in VH and −7 to −14 dB in VV; ground samples range from −12 to −24 dB in VH and −6 to −15 dB in VV; and water samples range from −21 to −30 dB in VH and −11 to −26 dB in VV. Forest backscatter are expected to saturate over time, especially in dense forests. These variations are due to differences in forest species, landscape, environmental and climatic drivers, and phenomenon or interventions on the site.

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