Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering: Via Level Set Evolution - Brossura

Kama, Ramudu; Chenigaram, Kalyani; Ganta, Raghotham Reddy

 
9786200007124: Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering: Via Level Set Evolution
Brossura
ISBN 10: 6200007128 ISBN 13: 9786200007124
Casa editrice: LAP LAMBERT Academic Publishing, 2019

Sinossi

Due to the difficulties occurred in remote sensing image information, an analysis algorithms growth of a large scale image segmentation haven’t kept a place with the requirement for the methods which to develop the final accuracy of object detection as well as the recognition. Traditional Level set segmentation methods which are Chan-Vese (CV), IVC 2010, ACM with SBGFRLS, Online Region Based ACM (ORACM) were suffered from more amount of time complexity, as well as low segmentation accuracy due to the large intensity homogeneities and the noise. The robust segmentation of remote sensing images is a tedious task because due to lack of spatial information and pixel intensities are non-homogenous. In this regard region based segmentation is impossible. So this is the reason we consider clustering algorithms in pre-processing to improve the cluster efficiency & overcome the obstacles present in traditional methods. In the proposed method we were having two stages, the first stage, in order to pre-process the image we were utilizing the fuzzy logic and k-means clustering known as Fuzzy-k-Means clustering. Here the clustered segmentation results suffering from boundaries and edge leak.

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Editore
LAP LAMBERT Academic Publishing
Data di pubblicazione
2019
Lingua
Inglese
ISBN 10 
6200007128
ISBN 13 
9786200007124
Rilegatura
Copertina flessibile
Numero di pagine
56
Contatto del produttore
non disponibile
Persona responsabile
KreGa Onlinehandel GmbH
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