| Autor: |
Suhaily Cardona, Samuel Rosario, Carmen Zayas, Carlos Rivera-Borrero, Adrienne Mundorf, Shawn Hunt |
| Rok vydání: |
2008 |
| Předmět: |
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| Zdroj: |
SPIE Proceedings. |
| ISSN: |
0277-786X |
| DOI: |
10.1117/12.779209 |
| Popis: |
This paper presents a ground truth data collection effort along with its use in evaluating unmixing algorithms. Unmixing algorithms are typically evaluated using synthetic data generated by selecting endmember spectrums and adding them in different amounts and with added noise. Going from synthetic to real data poses many problems. One of the greatest is the amount of data to be collected. Also, there will be many unmodeled variations in real data. These include greater variation of the endmembers, additional endmembers that are a very small percentage of the image, and nonlinear effects in the data that are not modeled. The data collation effort produced a high resolution class map along with spectral measurements of 153 different sampling sites to validate the map. The methodology for using this high resolution class map for generating the ground truth data for use in the unmixing algorithms is presented. Specifically, a 1m class map is used to generate the endmember abundances for every pixel in a 30m Hyperion image of the Enrique Reef in Southwest Puerto Rico. The results using two unmixing algorithms, one with a sum to one constraint and the other with a non-negative constraint are presented. The unmixing results for each endmember are presented along with a newly developed unmixing parameter called the Correct Unmixing Index (CUI). |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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