Agricultural mapping using Support Vector Machine-Based Endmember Extraction (SVM-BEE)
- ORNL
Extracting endmembers from remotely sensed images of vegetated areas can present difficulties. In this research, we applied a recently developed endmember-extraction algorithm based on Support Vector Machines (SVMs) to the problem of semi-autonomous estimation of vegetation endmembers from a hyperspectral image. This algorithm, referred to as Support Vector Machine-Based Endmember Extraction (SVM-BEE), accurately and rapidly yields a computed representation of hyperspectral data that can accommodate multiple distributions. The number of distributions is identified without prior knowledge, based upon this representation. Prior work established that SVM-BEE is robustly noise-tolerant and can semi-automatically and effectively estimate endmembers; synthetic data and a geologic scene were previously analyzed. Here we compared the efficacies of the SVM-BEE and N-FINDR algorithms in extracting endmembers from a predominantly agricultural scene. SVM-BEE was able to estimate vegetation and other endmembers for all classes in the image, which N-FINDR failed to do. Classifications based on SVM-BEE endmembers were markedly more accurate compared with those based on N-FINDR endmembers.
- Research Organization:
- Oak Ridge National Laboratory (ORNL); Center for Computational Sciences
- Sponsoring Organization:
- SC USDOE - Office of Science (SC)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 979114
- Journal Information:
- Optics Express, Journal Name: Optics Express Journal Issue: 3 Vol. 47
- Country of Publication:
- United States
- Language:
- English
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