2022
DOI: 10.32620/reks.2022.3.13
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On classifier learning methodologies with application to compressed remote sensing images

Abstract: Remote sensing images have found numerous applications nowadays. A traditional outcome or intermediate result of their processing is a classification map. Such maps are usually obtained from a pre-trained classifier and it is desired to have the produced classification maps as accurately as possible. The basic subject of this article is the factors determining this accuracy. The main among them are the quality of remote sensing data and classifier type, parameters and training approach. Image quality can be de… Show more

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Cited by 3 publications
(1 citation statement)
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“…It plays a crucial role in many applications such as satellite imagery [1], robotics [2], stereoscopy [3], motion estimation [4], and especially medical imaging. In this latter context, the registration finds a fertile field of its application and represents a crucial step in many situations such as the fusion of data coming from different modalities [5], remote sensing images [6,7], medical images [8], and radiotherapy [9], etc.…”
Section: Introductionmentioning
confidence: 99%
“…It plays a crucial role in many applications such as satellite imagery [1], robotics [2], stereoscopy [3], motion estimation [4], and especially medical imaging. In this latter context, the registration finds a fertile field of its application and represents a crucial step in many situations such as the fusion of data coming from different modalities [5], remote sensing images [6,7], medical images [8], and radiotherapy [9], etc.…”
Section: Introductionmentioning
confidence: 99%