Supervised Hyperspectral Image Classification With Rejection
نویسندگان
چکیده
منابع مشابه
Semi-supervised feature learning for hyperspectral image classification
Hyperspectral image has high-dimensional Spectral–spatial features, those features with some noisy and redundant information. Since redundant features can have significant adverse effect on learning performance. So efficient and robust feature selection methods are make the best of labeled and unlabeled points to extract meaningful features and eliminate noisy ones. On the other hand, obtaining...
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The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification. In this paper, we proposed a novel method, called semi-supervised marginal Fisher analysis (SSMFA), to process HSI of natural scenes, which uses ...
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Article history: Received 12 October 2014 Received in revised form 26 December 2014 Accepted 1 January 2015 Available online 25 February 2015
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ژورنال
عنوان ژورنال: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
سال: 2016
ISSN: 1939-1404,2151-1535
DOI: 10.1109/jstars.2015.2510032