NDLI: Classification of QuickBird imagery based on spectral feature and wavelet-domain fractal texture

Content Provider	IEEE Xplore Digital Library
Author	Luan, Haijun Zhang, Xinxin Wang, Xiaoqin Yang, Nana Zhu, Xiaoling Zhang, Aiguo
Copyright Year	2015
Description	Author affiliation: College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China (Luan, Haijun; Zhang, Xinxin; Zhu, Xiaoling; Zhang, Aiguo) \|\| Spatial Information Research Center of Fujian Province; Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, China (Wang, Xiaoqin) \|\| Xiamen Jiuhua Communication Equipment Factory, Xiamen, China (Yang, Nana)
Abstract	As an important assisted feature for spectral one, texture plays an important role in image analysis and automatic recognition, especially in high spatial resolution remotely sensed images. Meanwhile, wavelet is an effective method of extracting multi-scale features of ground objects in images. Then in this research, the extraction approach of wavelet-domain fractal texture (WDFT) was proposed, and it was implemented to improve the image classification of QuickBird of Fuzhou City. WDFTs of QuickBird image were computed on different window sizes and decomposition layers, and three texture images were selected from the viewpoint of image classification and thematic extraction of buildings, the different box-counting (DBC) cap features of CA1 (the coarse image of the first decomposed layer of QuickBird image) on 64 ∗ 64 and 16 ∗ 16 windows and the multi-fractal feature of CA1 on 16 × 16 window. The experiment results implied that: because of the addition of WDFT information, the aquafarm, major roads and bare land confused with buildings were distinguished well; the supervised classification based on spectral feature was modified, and its total classification accuracy and Kappa coefficient became better (from 76.17% to 81.25%, and from 0.7006 to 0.7587, respectively), and also made the extraction accuracy (user one and mapping one) of buildings better (from 80.70% to 82.54%, and from 65.71% to 74.29%, respectively). It proves that the WDFT was effective. Addressed on the disadvantages in the research, the WDFT extraction on rectangular window and adaptive sizes will be studied and more decomposition layers information will be integrated in the next work.
Starting Page	748
Ending Page	753
File Size	1869256
Page Count	6
File Format	PDF
e-ISBN	9781467390989
DOI	10.1109/CISP.2015.7407977
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	remotely sensed imagery Image recognition fractal texture Roads QuickBird Buildings Vegetation mapping wavelet Feature extraction Fractals classification Remote sensing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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