NDLI: Spectral-spatial based super pixel remote sensing image classification

Content Provider	IEEE Xplore Digital Library
Author	Guangyun Zhang Xiuping Jia Kwok, N.M.
Copyright Year	2011
Description	Author affiliation: School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, Australia (Kwok, N.M.) \|\| School of Engineering and Information Technology, University College, The University of New South Wales, Canberra, Australia (Guangyun Zhang; Xiuping Jia)
Abstract	Apart from the rich spectral information provided by multispectral or hypersepctral sensors, the spatial information has been paid more and more attention in remote sensing classification, especially for high spatial resolution images. Pixel-wise spatial features can be generated by applying Gray Level Co-occurrence Matrix (GLCM) locally to describe an image's texture properties. Morphological filtering provides spatial structure enhancement and watershed processing aims at contextual boundary identification. In this paper, the advantages and disadvantages of these spatial treatments are investigated. A combined procedure is developed to maximize spatial information extraction. Texture feature selection is emphasized for class separability enhancement. Morphological filtering is introduced as a preprocessing for watershed segmentation in order to reduce false alarm on contextual boundaries. Super pixels are formed for the objects defined from the watershed segmentation. The experimental results show that the combined spatial treatment is effective and, by integrating it with spectral information, an object oriented classification map can be obtained with significantly reduced ‘salt and pepper’ noise.
Starting Page	1680
Ending Page	1684
File Size	321299
Page Count	5
File Format	PDF
ISBN	9781424493043
e-ISBN	9781424493067
DOI	10.1109/CISP.2011.6100425
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	super pixel Image segmentation watershed Correlation Filtering texture Feature extraction remote sensing Spatial resolution Image reconstruction Remote sensing morphological filtering
Content Type	Text
Resource Type	Article

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