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- 검색키워드
- 저자 : Farid Muhammad Imran (검색결과 3 건)
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Farid Muhammad Imran,Mingyi He 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.9
This paper presents a new technique for marker selection called marker selection using skeletonization. Markers are the most reliable pixels that represent a particular class. Marker selection using skeletonization is further analysed to do classification of hyperspectral image with very low training samples, as low as one pixel per class. Both spatial and spectral information are used to improve the final classification accuracy. An Extended Morphological Profile with duality is used to extract spatial information. Furthermore, it is shown that by using the spatial and spectral information with non- parametric supervised feature extraction methods, better classification accuracy can be achieved even when very low training samples are available. The classification maps will be shown and discussed for very low training sample analysis using marker selection by skeletonization technique.
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Farid Muhammad Imran,Mingyi He,Yifan Zhang 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.9
Hyperspectral Image processing is usually time consuming, due to its huge data size. Nowadays Hyperspectral Imaging is used in many fields where real-time solutions are required. A systemic comparison study of linear feature extraction methods for classification of hyperspectral images with various types of noises is carried out in this paper, in which the performance of different linear feature extraction methods for classification and their computation cost reduction are compared. In practice, hyperspectral images are often contaminated by different types of noises, as the atmosphere around hyperspectral cameras may change all the time. In this paper, to make it more realistic, different types of noises, including Salt-and-Pepper noise, Gaussian noise, Speckle noise and their mixtures, are artificially imposed on the hyperspectral image. Support Vector Machine based classification is employed for classification performance comparison. The experimental results are very helpful for selecting linear feature extraction methods for classification of hyperspectral images that are usually affected with noises.
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Noise Effects on Feature Mining Non-Parametric Supervised Feature Extraction Techniques
Farid Muhammad Imran 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.12
In this paper two famous and commonly used feature mining non-parametric supervised feature extraction techniques (NSFETs) called Non-parametric Weighted Feature Extraction (NWFE) and Decision Boundary Feature Extraction (DBFE) are analyzed to see their efficiency in the presence of noise. In particularly these feature extraction techniques are used in classification as they give better classification accuracy. This study reveals that NSFETs are very sensitive to noise because of which the number of features increases and we get low classification accuracy. In order to see the behavior of NSFETs, spatial and spectral information from hyperspectral image classification is used. The experimental results show that in the presence of noise, spectral information is much more effected than the spatial information when features are extracted using the NSFETs. It is also examined that NWFE is more affected by noise than DBFE. The linear filtering technique is used just before the classifier in order to mitigate the noise effects in NSFETs. Using linear filtering just before the classifier does improve the final classification accuracy but with high number of spatial and spectral features. This does not satisfy the one of the main purpose of feature extraction and that is feature reduction.
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