Abstract
In this paper, a novel approach for unsupervised spectral unmixing in remote sensing imagery is presented. This approach is derived from Independent Component Analysis (ICA). First, we present the limitations of Gaussian Mixture Model (GMM) and ICA for spectral unmixing. To overcome these limitations we have developed an approach that employs the ICA model to characterize the data generation process and have proposed an ICA mixture model (ICAMM) based approach for unsupervised spectral unmixing. This approach estimates the endmember probability density function by modeling it with a non-Gaussian probability distribution. Thus, the ability to model higher order statistical properties of remote sensing imagery increases the practical applicability of ICAMM for spectral unmixing. The results from our experimental study have demonstrated the efficacy of the proposed algorithm for unsupervised spectral unmixing.
| Original language | English (US) |
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| Pages | 1065-1068 |
| Number of pages | 4 |
| State | Published - 2004 |
| Event | 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004 - Anchorage, AK, United States Duration: Sep 20 2004 → Sep 24 2004 |
Other
| Other | 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004 |
|---|---|
| Country/Territory | United States |
| City | Anchorage, AK |
| Period | 9/20/04 → 9/24/04 |
Keywords
- Blind source separation
- Gaussian mixture model
- Higher order statistics
- Independent componant analysis mixture model
- Independent component analysis
- Linear mixture model
- Spectral unmixing
ASJC Scopus subject areas
- Computer Science Applications
- General Earth and Planetary Sciences