NDLI: A new watershed segmentation (NWS) and particle swarm optimization (PSO-SVM) techniques in remote sensing image retrieval

Content Provider	IEEE Xplore Digital Library
Author	Bhandari, Kiran Ashok Ramchandra R, Manthalkar
Copyright Year	2014
Description	Author affiliation: Department of Electronics and Telecommunication, SGGS IE & T, Vishnupuri Nanded, India (Ramchandra R, Manthalkar) \|\| Department of CMPN, TCET Kandivali (E) Mumbai, India (Bhandari, Kiran Ashok)
Abstract	In this paper, the latest watershed segmentation process is actually found in the object feature extraction process. In the proposed method, initially the actual visual features are usually taken from the images using the spatial spectral heterogeneity method. Afterwards, the object features are usually taken from the new watershed segmentation method in which segmented objects are usually grouped with the PSO-SVM method. With PSO-SVM, the actual SVM parameters are usually optimized to achieve higher classification accuracy. Then similar scene images from the data base are usually taken from the SS modelling. A further variety of remote sensing images are utilized in the overall performance analysis process. The particular implementation benefits show the effectiveness of proposed new watershed segmentation method in RSIR and the reached advancement in sensitivity and also recall measures. Moreover, the actual overall performance of the proposed technique is actually considered by comparing with all the existing RSIR and the typical SBRSIR methods.
Starting Page	1
Ending Page	6
File Size	386282
Page Count	6
File Format	PDF
ISBN	9781479968954
e-ISBN	9781479968961
DOI	10.1109/ICRITO.2014.7014722
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-08
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Image segmentation Image retrieval Semantics Support Vector Machine (SVM) Particle Swarm Optimization (PSO) Feature extraction Scene Semantic (SS) Sensors Remote Sensing Image Retrieval (RSIR) Remote sensing
Content Type	Text
Resource Type	Article

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