Hyperspectral agricultural mapping using support vector machine-based endmember extraction (SVM-BEE).
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 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 estimate endmembers; synthetic data and a geologic scene were previously analyzed. Here we compared the efficacies of SVM-BEE, N-FINDR, and SMACC algorithms in extracting endmembers from a real, predominantly-agricultural scene. SVM-BEE estimated vegetation and other endmembers for all classes in the image, which N-FINDR and SMACC failed to do. SVM-BEE was consistent in the endmembers that it estimated across replicate trials. Spectral angle mapper (SAM) classifications based on SVM-BEE-estimated endmembers were significantly more accurate compared with those based on N-FINDR- and (in general) SMACC-endmembers. Linear spectral unmixing accrued overall accuracies similar to those of SAM.
Filippi
Anthony M
AM
Department of Geography, Texas A&M University, College Station, Texas 77843-3147, USA. filippi@tamu.edu
Archibald
Rick
R
Bhaduri
Budhendra L
BL
Bright
Edward A
EA
D000383:Agriculture / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D010363:Pattern Recognition, Automated / Q000379:methods*; D010944:Plants / Q000737:chemistry* / Q000145:classification*; D013057:Spectrum Analysis / Q000379:methods*