Relevant bibliographies by topics / RADARSAT-1 SAR

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'RADARSAT-1 SAR'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "RADARSAT-1 SAR"

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Wackerman, C. C., K. S. Friedman, W. G. Pichel, P. Clemente-Colón, and X. Li. "Automatic Detection of Ships in RADARSAT-1 SAR Imagery." Canadian Journal of Remote Sensing 27, no. 4 (August 2001): 371–78. http://dx.doi.org/10.1080/07038992.2001.10854879.

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Wackerman, C. C., K. S. Friedman, W. G. Pichel, P. Clemente-Colón, and X. Li. "Automatic Detection of Ships in RADARSAT-1 SAR Imagery." Canadian Journal of Remote Sensing 27, no. 5 (October 2001): 568–77. http://dx.doi.org/10.1080/07038992.2001.10854896.

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Li, Xiaofeng, Jun A. Zhang, Xiaofeng Yang, William G. Pichel, Mark DeMaria, David Long, and Ziwei Li. "Tropical Cyclone Morphology from Spaceborne Synthetic Aperture Radar." Bulletin of the American Meteorological Society 94, no. 2 (February 1, 2013): 215–30. http://dx.doi.org/10.1175/bams-d-11-00211.1.

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In 2008, the Canadian Space Agency sponsored the Radarsat Hurricane Applications Project (RHAP), for researching new developments in the application of Radarsat-1 synthetic aperture radar (SAR) data and innovative mapping approaches to better understand the dynamics of tropical cyclone genesis, morphology, and movement. Although tropical cyclones can be detected by many remote sensors, SAR can yield high-resolution (subkilometer) and low-level storm information that cannot be seen below the clouds by other sensors. In addition to the wind field and tropical cyclone eye information, structures associated with atmospheric processes can also be detected by SAR. We have acquired 161 Radarsat-1 SAR images through RHAP between 2001 and 2007. Among these, 73 images show clear tropical cyclone eye structure. In addition, we also acquired 10 images from the European Space Agency's Envisat SAR between 2004 and 2010. Both Atlantic hurricanes and Pacific typhoons are included. In this study, we analyze these 83 (73 Radarsat-1 and 10 Envisat) images with tropical cyclone eye information along with ancillary tropical cyclone intensity information from the archive to generate tropical cyclone morphology statistics. Histograms of wave-number asymmetry and intensity are presented. The statistics show that when the storm has higher intensity, the tropical cyclone eye tends to become more symmetric, and the area of the tropical cyclone eye, defined by the minimum wind area, tends to be smaller. Examples of finescale structures within the tropical cyclone (i.e., eye/eyewall mesovortices, arc clouds, double eyewalls, and abnormally high wind or rain within eyes) are presented and discussed.
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Joughin, Ian. "Ice-sheet velocity mapping: a combined interferometric and speckle-tracking approach." Annals of Glaciology 34 (2002): 195–201. http://dx.doi.org/10.3189/172756402781817978.

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AbstractThe first and second RADARSAT Antarctic Mapping Missions (AMM-1 and -2) have now acquired interferometric synthetic aperture radar (SAR) overmuch of the ice sheet. The RADARSAT 24 day repeat cycle is nearly ideal for measuring slow ice motion (e.g. <100ma–1), but application of SAR interferometry is limited in faster-moving areas. With a 1day repeat period, ERS-1/-2 tandem SAR data are much better matched to fast motion, but are not always available. Fortunately, several authors have demonstrated the ability to measure velocity in fast-moving areas by tracking SAR speckle from image to image, which works well even in the absence of visible features. While these estimates have intrinsically lower resolution and poorer accuracy than direct phase measurements, they serve well in areas where there are no data suitable for conventional interferometry. This paper describes algorithms I have developed for merging interferometric and speckle-tracking data from multiple swaths to form a single seamless mosaic of velocity. At each point in the mosaic, all the available data are combined to produce estimates of the velocity and the associated error. This technique is demonstrated using RADARSAT data collected over Lambert Glacier, Antarctica, during AMM-1 and -2.
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Toutin, T. "Path Processing and Block Adjustment With RADARSAT-1 SAR Images." IEEE Transactions on Geoscience and Remote Sensing 41, no. 10 (October 2003): 2320–28. http://dx.doi.org/10.1109/tgrs.2003.814661.

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Jezek, Kenneth C. "RADARSAT-1 Antarctic Mapping Project: change-detection and surface velocity campaign." Annals of Glaciology 34 (2002): 263–68. http://dx.doi.org/10.3189/172756402781818030.

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AbstractThe RADARSAT-1 Antarctic Mapping Project (RAMP) is a collaboration between NASA and the Canadian Space Agency to map Antarctica using synthetic aperture radar (SAR). The first Antarctic Mapping Mission (AMM-1) was successfully completed in October 1997. Data from the acquisition phase of the 1997 campaign have been used to achieve the primary goal of producing the first high-resolution SAR image map of Antarctica. The Modified Antarctic Mapping Mission (MAMM) occurred during the fall of 2000. The acquisition strategy concentrated on collecting highest- resolution RADARSAT-1 data of Antarctica’s fast glaciers for change detection and feature-retracking estimates of surface velocity. Additionally, extensive data were acquired for interferometric analysis over the entire viewable region, which extends north of 80.1˚ S latitude. This paper summarizes the goals and strategy behind MAMM. It goes on to discuss ice-sheet margin changes observed on several ice shelves around the continent. Margin changes are documented by comparing AMM-1 and MAMM data with earlier datasets including European Remote-sensing Satellite-1 SAR imagery, Landsat imagery, the Antarctic Digital Database (version 1) coastline and Declassified Intelligence Satellite Photography. Analysis reveals a complex pattern of ice-margin advance and retreat without indicating any systematic behavior in ice-sheet extent about the ice-sheet perimeter.
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Salman Alsalman, Abdullah. "Interpretation of Radarsat-1 SAR Imagery of Riyadh for Mapping Applications." Journal of King Saud University - Engineering Sciences 22, no. 2 (July 2010): 89–98. http://dx.doi.org/10.1016/s1018-3639(18)30496-3.

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Karvonen, J., M. Simila, and M. Makynen. "Open Water Detection From Baltic Sea Ice Radarsat-1 SAR Imagery." IEEE Geoscience and Remote Sensing Letters 2, no. 3 (July 2005): 275–79. http://dx.doi.org/10.1109/lgrs.2005.847930.

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马, 智峰. "Combined Multi-Temporal Radarsat-1 and Sentinel-1 SAR Data Inversion Forest Tree Height." Geomatics Science and Technology 09, no. 02 (2021): 59–66. http://dx.doi.org/10.12677/gst.2021.92007.

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Hong Quang, Nguyen, Vu Anh Tuan, Le Thi Thu Hang, Nguyen Manh Hung, Doan Thi The, Dinh Thi Dieu, Ngo Duc Anh, and Christopher R. Hackney. "Hydrological/Hydraulic Modeling-Based Thresholding of Multi SAR Remote Sensing Data for Flood Monitoring in Regions of the Vietnamese Lower Mekong River Basin." Water 12, no. 1 (December 23, 2019): 71. http://dx.doi.org/10.3390/w12010071.

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Synthetic Aperture Radar (SAR) remote sensing data can be used as an effective alternative to detect surface water and provide useful information regarding operational flood monitoring, in particular for the improvement of rapid flood assessments. However, this application frequently requires standard and simple, yet robust, algorithms. Although thresholding approaches meet these requirements, limitations such as data inequality over large spatial regions and challenges in estimating optimal threshold values remain. Here, we propose a new method for SAR water extraction named Hammock Swing Thresholding (HST). We applied this HST approach to four SAR remote sensing datasets, namely, Sentinel-1, ALOS-2, TerraSAR-X, and RadarSAT-2 for flood inundation mapping for a case study focusing on the Tam Nong district in the Vietnam Mekong delta. A 2D calibrated Hydrologic Engineering Centers River Analysis System (HEC-RAS) model was coupled with the HST outputs in order to estimate the optimal thresholds (OTs) where the SAR-based water masks fitted best with HEC-RAS’s inundation patterns. Our results showed that water levels extracted from Sentinel-1 data best agreed with the HEC-RAS water extent (88.3%), following by ALOS-2 (85.9%), TerraSAR-X (77.2%). and RadarSAT-2 (72%) at OTs of −15, 68, 21, and 35 decibel (dB), respectively. Generated flood maps indicated changes in the flood extent of the flooding seasons from 2010 and 2014–2016 with variations in spatial extent appearing greater in the TerraSAR-X and RadarSAT-2 higher resolution maps. We recommend the use of OTs in applications of flood monitoring using SAR remote sensing data, such as for an open data cube (ODC).
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Dissertations / Theses on the topic "RADARSAT-1 SAR"

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Hu, Hongtao. "Urban Land-cover Mapping with High-resolution Spaceborne SAR Data." Licentiate thesis, KTH, Geoinformatik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-26931.

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Urban areas around the world are changing constantly and therefore it is necessary to update urban land cover maps regularly. Remote sensing techniques have been used to monitor changes and update land-use/land-cover information in urban areas for decades. Optical imaging systems have received most of the attention in urban studies. The development of SAR applications in urban monitoring has been accelerated with more and more advanced SAR systems operating in space.   This research investigated object-based and rule-based classification methodologies for extracting urban land-cover information from high resolution SAR data. The study area is located in the north and northwest part of the Greater Toronto Area (GTA), Ontario, Canada, which has been undergoing rapid urban growth during the past decades. Five-date RADARSAT-1 fine-beam C-HH SAR images with a spatial resolution of 10 meters were acquired during May to August in 2002. Three-date RADARSAT-2 ultra-fine-beam C-HH SAR images with a spatial resolution of 3 meters were acquired during June to September in 2008.   SAR images were pre-processed and then segmented using multi-resolution segmentation algorithm. Specific features such as geometric and texture features were selected and calculated for image objects derived from the segmentation of SAR images. Both neural network (NN) and support vector machines (SVM) were investigated for the supervised classification of image objects of RADARSAT-1 SAR images, while SVM was employed to classify image objects of RADARSAT-2 SAR images. Knowledge-based rules were developed and applied to resolve the confusion among some classes in the object-based classification results.   The classification of both RADARSAT-1 and RADARSAT-2 SAR images yielded relatively high accuracies (over 80%). SVM classifier generated better result than NN classifier for the object-based supervised classification of RADARSAT-1 SAR images. Well-designed knowledge-based rules could increase the accuracies of some classes after the object-based supervised classification. The comparison of the classification results of RADARSAT-1 and RADARSAT-2 SAR images showed that SAR images with higher resolution could reveal more details, but might produce lower classification accuracies for certain land cover classes due to the increasing complexity of the images. Overall, the classification results indicate that the proposed object-based and rule-based approaches have potential for operational urban land cover mapping from high-resolution space borne SAR images.
QC 20101209
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Denize, Julien. "Evaluation of time-series SAR and optical images for the study of winter land-use." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S062.

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L'étude de l'utilisation hivernale du sol représente un enjeu majeur afin de préserver et d'améliorer la qualité des sols et des eaux de surfaces. Cependant la connaissance des dynamiques spatio-temporelles associées à l'utilisation du sol en période hivernale demeure aujourd'hui encore un défi pour la communauté scientifique. C'est dans ce contexte que s'inscrivent ces travaux de thèse dont l'objectif est d'évaluer le potentiel de séries temporelles d'images optiques et RSO à haute résolution spatiale pour l'étude de l'utilisation des sols en période hivernale à une échelle locale et régionale. Pour se faire, une méthodologie a été établie afin : (i) de déterminer la méthode de classification la plus adaptée pour identifier l'usage des sols en hiver; (ii) de comparer des images RSO Sentinel-1 et optiques Sentinel-2; (iii) de définir la configuration RSO la plus adaptée en comparant trois séries temporelles d'images (Alos-2, Radarsat-2 et Sentinel-1).Les résultats ont tout d'abord mis en évidence l'intérêt de l'algorithme de classification Random Forest pour discriminer à une échelle fine les types d'usage des sols en hiver qui sont très variés. Dans un second temps, ils ont souligné l'intérêt des données Sentinel-2 pour cartographier l'utilisation hivernale des sols à une échelle locale et régionale. Enfin, ils ont permis de déterminer qu'une série temporelle dense d'images Sentinel-1 était la configuration RSO la plus adaptée afin d'identifier l'utilisation hivernale du sol. De manière générale, si cette thèse a permis de montrer que les données Sentinel-2 sont les plus adaptées pour étudier l'utilisation du sol en période hivernale, les images RSO ont tout leur intérêt dans les régions où le couvert nuageux est important, les séries temporelles denses Sentinel- 1 ayant été définies comme les plus performantes
The study of winter land-use is a major challenge in order to preserve and improve the quality of soils and surface water. However, knowledge of the spatio-temporal dynamics associated with winter land-use remains a challenge for the scientific community. In this context, the objective of this study is to evaluate the potential of time series of high spatial resolution optical and SAR images for the study of winter land-use at a local and regional scale. For that purpose, a methodology has been established to: (i) determine the most suitable classification method for identifying winter land-use ; (ii) compare Sentinel-1 SAR and Sentinel-2 optical images; (iii) define the most suitable SAR configuration by comparing three image time-series (Alos-2, Radarsat-2 and Sentinel-1).The results first of all highlighted the interest of the Random Forest classification algorithm to discriminate at a fine scale the different types of land use in winter. Secondly, they showed the value of Sentinel-2 data for mapping winter land-use at a local and regional scale. Finally, they determined that a dense time series of Sentinel-1 images was the most appropriate SAR configuration to identify winter land-use. In general, while this thesis has shown that Sentinel-2 data are best suited to studying land use in winter, SAR images are of great interest in regions with significant cloud cover, dense Sentinel-1 time-series having being defined as the most efficient
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LAU, SIN WAI. "Comparison and Fusion of space borne L-, C- and X- Band SAR Images for Damage Identification in the 2008 Sichuan Earthquake." Thesis, KTH, Geoinformatik och Geodesi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-37005.

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Remote sensing has been widely used in disaster management. However, application of optical imageries in damage detection is not always feasible for immediate damage assessment. In the case of the Sichuan earthquake in 2008, the damaged areas were covered by cloud and fog for most of the time. The all weather SAR imageries could instead provide information of the damaged area. Therefore, more efforts are needed to explore the usability of SAR data. In regards to this purpose, this research focuses on studying the ability of using various SAR data in damage identification through image classification, and furthermore the effectiveness of fusion of various sensors in classification is evaluated.   Three different types of SAR imagery were acquired over the heavily damaged zone Qushan town in the Sichuan earthquake. The 3 types of SAR data are ALOS PALSAR L-band, RADARSAT-1 C-band and the TerraSAR-X X- band imageries.   Maximum likelihood classification method is applied on the imageries.  Four classes: Water, collapsed area, built-up area and landslide area are defined in the study area. The ability of each band in identifying these four classes is studied and the overall classification accuracy is analysed. Furthermore, fusion of these 3 types of imageries is performed and the effectiveness and accuracy of image fusion classification are evaluated.   The results show that classification accuracy from individual SAR imagery is not ideal. The overall accuracy which PALSAR gives is 30.383%, RADARSAT-1 is 31.268% while TerraSAR-X only achieves 37.168%. Accuracy statistics demonstrate that TerraSAR-X performs the best in classifying these four classes.   SAR image fusion shows a better classification result. Double image fusion of PALSAR and RADARSAT-1, PALSAR and TerraSAR-X, and RADARSAT-1 and TerraSAR-X give an overall classification accuracy of 41.88%, 42.478% and 37.758% respectively. The result from triple image fusion even reaches 52.507%. They are all higher than the result given by the individual images.   The study illustrates that the VHR TerraSAR X band SAR data has a higher ability in classification of damages, and fusion of different band can improve the classification accuracy.
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Chiu, Wen-Chin, and 邱文欽. "Study on Radarsat-1 SAR Image Matching by Least Square method." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/82052879994585897995.

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碩士
國立中央大學
土木工程研究所
91
The orientation parameters are solved from the satellite header files information and adjustment model. Then add digital terrain model to develop relations between object space and image space. In this study, we use groundel area images to match them with one another. Object surface element is the basic element of object space matching, also called groundel. The size is pixels rate project to object space. Groundel images are derived from object space position to get image coordinates and gray level. The methodology has to develop the mathematical correlation between object space and image space. We provide another groundel area image as additional information for image matching. Next, match groundel area images base on the least square matching. To improve a precise matching, we use the iterated data snooping（IDS）－use the normalized residual to detect and delete the blunder of observation at each iteration. Finally, we use the best invariant quadratic unbiased estimation（BIQUE）to provide a method to estimate the variance components and to get reasonable weight.
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"Qualidade geometrica de imagens JERS-1 (SAR)e Radarsat (modos S2 e S3)em ambiente de floresta tropical (Rondonia)." Tese, Arquivo URLib de Teses e Dissertações do INPE, 1999. http://bibdigital.sid.inpe.br/rep-/sid.inpe.br/deise/2000/04.27.16.33.

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Book chapters on the topic "RADARSAT-1 SAR"

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Marghany, Maged. "Three-Dimensional Coastal Front Visualization from RADARSAT-1 SAR Satellite Data." In Computational Science and Its Applications – ICCSA 2012, 447–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31137-6_34.

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Marghany, Maged, and Mazlan Hashim. "Comparative Algorithms for Oil Spill Detection from Multi Mode RADARSAT-1 SAR Satellite Data." In Computational Science and Its Applications - ICCSA 2011, 318–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21887-3_25.

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Conference papers on the topic "RADARSAT-1 SAR"

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Fan Wu, Chao Wang, Hong Zhang, and Bo Zhang. "Change detection and analysis with radarsat-1 SAR image." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423377.

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B., Asha Rani, Deepak Putrevu, Arundhati Misra, and Tapan Misra. "ScanSAR processing algorithm for spaceborne SAR: results with Radarsat-1 data." In Asia-Pacific Remote Sensing Symposium, edited by Azita Valinia, Seiho Uratsuka, and Tapan Misra. SPIE, 2006. http://dx.doi.org/10.1117/12.693538.

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Li, Na, Fang Liu, and Lei Qiu. "Change detection approach on multitemporal Radarsat-1 SAR imagery for port surveillance." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8126925.

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Torma, M., and J. Koskinen. "Land-use classification using multitemporal ERS-1, Radarsat and JERS SAR-images." In IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). IEEE, 1998. http://dx.doi.org/10.1109/igarss.1998.703762.

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Marghany, Maged. "Finite difference model for modeling sea surface current from RADARSAT-1 SAR data." In 2009 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5418123.

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Marghany, Maged, and Mazlan Hashim. "Comparative algorithms for oil spill automatic detection using multimode RADARSAT-1 SAR data." In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6049597.

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Maged Marghany. "Robust model for retrieval sea surface current from different RADARSAT-1 SAR mode data." In 2009 IEEE International Conference on Signal and Image Processing Applications. IEEE, 2009. http://dx.doi.org/10.1109/icsipa.2009.5478705.

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Chae, H., H. Lee, S. J. Cho, and N. W. Park. "Variation of Radar Backscattering Coefficient of Tidal Mudflat Observed by Radarsat-1 SAR and Polarimetric Scatterometer." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.771.

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Horstmann, Jochen, Wolfgang Koch, and Susanne Lehner. "High Resolution Wind Fields Retrieved From Spaceborne Synthetic Aperture Radar Images in Comparison to Numerical Models." In ASME 2002 21st International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/omae2002-28301.

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This paper introduces a recently developed algorithm to retrieve high-resolution wind fields over the ocean surface from spaceborne synthetic aperture radar (SAR) data. The algorithm consists of two parts, the first for determining wind direction and the second for wind speed retrieval. Wind directions are extracted from wind induced streaks e.g. from boundary layer rolls, Langmuir cells, or wind shadowing, which are approximately in line with the mean wind direction. Wind speed is derived from the normalized radar cross section (NRCS) and image geometry of the SAR image, together with the local retrieved wind direction. The application of SAR-wind retrieval in coastal regions is demonstrated using data acquired aboard the European satellites ERS-1 and ERS-2 and the Canadian satellite RADARSAT-1. These data allow to measure wind fields of an area of up to 500 km × 500 km with a resolution of up to 200 m. To improve and validate the set-up of numerical high-resolution models in coastal regions SAR-retrieved wind fields offer an unique opportunity. This is shown by comparisons of wind fields measured by SAR to results of the numerical model REMO, HIRLAM and GESIMA.
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Kang, Moon-Kyung, Hoonyol Lee, Chan-Su Yang, and Wang-Jung Yoon. "Estimation of Ocean Current Velocity in Coastal Area Using Radarsat-1 SAR Images and HF-Radar Data." In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4778882.

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Reports on the topic "RADARSAT-1 SAR"

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Vachon, P. W., P. Black, P. Dodge, K. Katsaros, P. Clemente-Colon, W. Pichel, and K. MacDonell. RADARSAT-1 SAR for Hurricane Watch. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219787.

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Hetu, R. J., and V. Singhroy. Airborne geophysics, geology and Radarsat-1 SAR compilation, image-map Radarsat-1 SAR and geology, Cape Breton Island, Nova Scotia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/209968.

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Toutin, Th. Path processing and block adjustment with RADARSAT-1 SAR images. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/220025.

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Hetu, R. J., and V. Singhroy. Airborne geophysics, geology and Radarsat-1 SAR compilation, composite image-map of Radarsat-1 SAR and ternary radioelement data, Cape Breton Island, Nova Scotia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/209970.

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Hetu, R. J., and V. Singhroy. Airborne geophysics, geology and Radarsat-1 SAR compilation, composite image-map of Radarsat-1 SAR and residual magnetic data, Cape Breton Island, Nova Scotia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/209971.

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Hetu, R. J., and V. Singhroy. Airborne geophysics, geology and Radarsat-1 SAR compilation, composite image-map of Radarsat-1 SAR and Canadian digital elevation data, Cape Breton Island, Nova Scotia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/209969.

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Hetu, R. J., and V. Singhroy. Airborne geophysics, geology and Radarsat-1 SAR compilation, composite image-map of Radarsat-1 SAR and vertical derivative magnetic data, Cape Breton Island, Nova Scotia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/209972.

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Toutin, Th, Y. Carbonneau, and R. Chénier. Path Processing and Block Bundle Adjustment with RADARSAT-1 SAR Images. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219878.

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Hetu, R. J., V. Singhroy, and R. McGregor. Airborne geophysics, geology and Radarsat-1 SAR compilation, Cape Breton Island, Nova Scotia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/209967.

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Touzi, R., and A. Sasitiwarih. On the Use of Radarsat and JERS-1 Satellite SARs for Trail and Road Detection in Tropical Rainforests. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/219360.

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