NDLI: New feature extraction algorithm for satellite image non-linear small objects

Content Provider	IEEE Xplore Digital Library
Author	Shoujuan Zhang Quan Zhou
Copyright Year	2012
Description	Author affiliation: National Key Laboratory of Space Microwave Technology, China Academy of Space Technology, Xi'an, China (Shoujuan Zhang; Quan Zhou)
Abstract	For satellite image non-linear small objects, a new feature extraction algorithm is proposed. This algorithm extracts features in a hierarchical structure. The features which represent the global shape property of the objects are extracted in inferior levels and the features which describe more local details of the objects are extracted in superior levels. In the first-level phase, the algorithm extracts the image binary entropy and the image normalized moment of inertia. In the second-level phase, based on wavelet transform as the detail micro tool, the algorithm extracts the Hu moments, the Zernike moments and the Fourier descriptors for all the child wave band images, and the features of each sub-wave band are respectively weighted according to their descriptive power. All the feature extractors are made invariant to translation, rotation and scale. The minimum Europe distance classification experimental results, the FCM and SVM recognition experimental results demonstrate that the algorithm can gradually describe the non-linear small objects in satellite images from global to local, from rough to fine. Compared with classical Hu moments, the Zernike moments and the Fourier descriptors, this algorithm is able to offer an available more competitive feature extractor for pattern recognition of satellite image non-linear small objects.
Starting Page	626
Ending Page	629
File Size	966829
Page Count	4
File Format	PDF
ISBN	9781467323635
e-ISBN	9781467323659
DOI	10.1109/EEESym.2012.6258736
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-24
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Wavelet Transform Shape Europe Non-linear Small Objects Feature extraction Hierarchical Invariant Feature Extractor Satellite Image Manganese
Content Type	Text
Resource Type	Article

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