ARRSI: Automatic Registration of Remote-Sensing Images

Wong, Alexander; Clausi, David A.

doi:10.1109/tgrs.2007.892601

Cited by 214 publications

(131 citation statements)

References 29 publications

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“…Automatic Registration of Remote-Sensing Images (ARRSI), an automatic registration system built to register multimodal and multitemporal satellite and aerial remotely sensed images was developed by Wong et al [68] which used phase-congruency model and a variation on the Random Sample and Consensus algorithm called Maximum Distance Sample Consensus (MDSAC) for registration and was said to be capable of handling remotely sensed images geometrically distorted by various transformations such as translation, rotation, and shear.…”

Section: Class 4 -Methods Using Points Edges Corners Road-junctionmentioning

confidence: 99%

Remote Sensing Image Registration Techniques: A Survey

Dawn

Saxena

Sharma

2010

Lecture Notes in Computer Science

104

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Abstract. Image Registration is the first step towards using remote sensed images for any purpose. Despite numerous techniques being developed for image registration, only a handful has proved to be useful for registration of remote sensing images due to their characteristic of being computationally heavy. Recent flux in technology has prompted a legion of approaches that may suit divergent remote sensing applications. This paper presents a comprehensive survey of such literatures including recently developed techniques.

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Section: Class 4 -Methods Using Points Edges Corners Road-junctionmentioning

confidence: 99%

Remote Sensing Image Registration Techniques: A Survey

Dawn

Saxena

Sharma

2010

Lecture Notes in Computer Science

104

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“…Manera et al [6] and Cheng et al [7] obtained their images from Unmanned Airborne Vehicles (UAVs) and used Tetracam images. Rube et al [8], Wong and Clausi [9] and Fonseca and Manjunath [10] used LandSat-7 images for registering. Danchao et al [11], and Fonseca and Manjunath [10] use SPOT-5 satellite images.…”

Section: Introductionmentioning

confidence: 99%

Big Data Frameworks for Efficient Range Queries to Extract Interested Rectangular Sub Regions

Eken¹,

Sayar²

2015

IJCA

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A satellite object can consist of more than one mosaic image. To extract any object from remote sensing satellite images, mosaic images need to be stitched. It is critical problem that which mosaics will be selected for image stitching among big mosaic dataset. In this paper, we propose two approaches to overcome mosaic selection problem by means of finding rectangular sub regions intersecting with range query. Former one is based on hybrid of Apache Hadoop and HBase and latter one is based on Apache Lucene. Their effectiveness has been compared in terms of response time under varying number of mosaics.

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“…In the second way, there are two methods: the first one is the manual registration [1], which is not feasible in the case where a large number of images must be registered, and with times cited from a few hours to 4-5 days for registering a single image [2]. And the second one is the automatic registration techniques that require little or no operator Manuscript received June 18, 2012;revised August 2, 2012 The authors are with the microelectronic and instrumentation laboratory, Faculty of Sciences, University of Monastir, Tunisia (e-mail: rochdi.bouchiha@gmail.com, kamel.besbes@fsm.rnu.tn).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a number of works have been carried out, which propose techniques for the automatic registration of remotely sensed images [1] [6], however, no comprehensive study has been carried out to analyze the performance of these techniques when dealing with different image transformation.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Remote-sensing Image Registration Using SURF

Bouchiha¹,

Besbes²

2013

IJCTE

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Abstract-Image registration is a key, essential element in analysis of Remote sensing images. Registration is critical both for initial processing and for end-user processing of those image products for data fusion, and change detection. This paper focused on the feature-based category of image registration algorithms. Many techniques for the detection and description of images' local characteristics have been proposed to register a set of images without user intervention. However, it is unclear which descriptors are more appropriate. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the sensor. In our evaluation, we have separated the detector from the descriptor as their performance depends on the interest point detector used. The descriptors are compared according to their recall and runtime efficiency and this deals with several geometric and photometric changes. We also propose an extension to the SURF descriptor and the results show the effectiveness of proposed improvements compared to base SURF method. Furthermore, we observe that the SURF descriptor outperforms the others' descriptors. Finally, based on the test results, we propose an approach to register automatically remotely sensed images.

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ARRSI: Automatic Registration of Remote-Sensing Images

Cited by 214 publications

References 29 publications

Remote Sensing Image Registration Techniques: A Survey

Remote Sensing Image Registration Techniques: A Survey

Big Data Frameworks for Efficient Range Queries to Extract Interested Rectangular Sub Regions

Automatic Remote-sensing Image Registration Using SURF

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