NDLI: Object-based multispectral image segmentation and classification

Content Provider	IEEE Xplore Digital Library
Author	Mirzapour, F. Ghassemian, H.
Copyright Year	2014
Description	Author affiliation: Fac. of Electr. & Comput. Eng., Tarbiat Modares Univ., Tehran, Iran (Mirzapour, F.; Ghassemian, H.)
Abstract	In this paper an object-based method for multispectral image segmentation and classification is proposed. Normally, in remote sensing a scene is represented by pixel-based features. It is possible to reduce data redundancy by a segmentfeature extraction process, where the segment-features, rather than the pixel-features, are used for multispectral scene representation and classification. Object-based algorithms partition the observation space into a set of disjoint segments (called objects). Then, pixels belonging to each segment are represented by segment features. In this paper, an unsupervised segmentation algorithm based on statistical region merging (SRM) framework is presented. Also a partial differential equations (PDE) algorithm is suggested as a preprocessing phase to improve the segmentation results. Illustrative examples are presented, and the performance of the extracted object features for classification purposes is investigated. Results show significant classification performance improvement.
Starting Page	430
Ending Page	435
File Size	451749
Page Count	6
File Format	PDF
e-ISBN	9781479953592
DOI	10.1109/ISTEL.2014.7000742
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-09
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Multispectral Images Segmentation Merging Support vector machine classification Object-based Classification Feature extraction Remote Sensing Image compression Classification algorithms Kernel
Content Type	Text
Resource Type	Article

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