Extinction Profiles Fusion for Hyperspectral Images Classification
| Autor: | Pedram Ghamisi, Shutao Li, Nanjun He, Jon Atli Benediktsson, Leyuan Fang |
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| Rok vydání: | 2018 |
| Předmět: |
Fusion
Computer science business.industry Feature extraction 0211 other engineering and technologies Hyperspectral imaging Pattern recognition 02 engineering and technology Support vector machine hyperspectral images (HSIs) Simple (abstract algebra) Extinction (optical mineralogy) Kernel (statistics) 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business Spatial analysis 021101 geological & geomatics engineering extinction profile (EP) feature extraction method |
| DOI: | 10.1109/tgrs.2017.2768479 |
| Popis: | An extinction profile (EP) is an effective spatial–spectral feature extraction method for hyperspectral images (HSIs), which has recently drawn much attention. However, the existing methods utilize the EPs in a stacking way, which is hard to fully explore the information in EPs for HSI classification. In this paper, a novel fusion framework termed EPs-fusion (EPs-F) is proposed to exploit the information within and among EPs for HSI classification. In general, EPs-F includes the following two stages. In the first stage, by extracting the EPs from three independent components of an HSI, three complementary groups of EPs can be constructed. For each EP, an adaptive superpixel-based composite kernel strategy is proposed to explore the spatial information within an EP. The weights to create the composite kernel and the number of superpixels are automatically determined based on the spatial information of each EP. In the second stage, since the different EPs contain highly complementary information, a simple yet effective decision fusion method is further applied to obtain the final classification result. Experiments on three real HSI data sets verify the qualitative and quantitative superiority of the proposed EPs-F method over several state-of-the-art HSI classifiers. |
| Databáze: | OpenAIRE |
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