Comparing endmember selection techniques for accurate mapping of plant species and land cover using imaging spectrometer data

Building representative spectral libraries and quantitatively selecting a subset of spectra for mapping plant species and land cover/land use within remotely sensed imagery remain challenging for accurate classification. Multiple Endmember Spectral Mixture Analysis (MESMA) can be used for both classification and modeling fractional composition, and has been applied to map multiple biogeophysical variables. Our major objectives in this research were to 1) test a sampling design for building independent and representative training and validation spectral libraries; 2) compare endmember selection by a combination of two established techniques (count-based selection (CoB) and endmember average root mean square error (EAR)) with a recently introduced one (iterative endmember selection (IES)); and 3) develop and test a hybrid method, which combines the strengths of the previous two methods. We applied CoB/EAR, IES, and the new hybrid technique to mapping plant species and cover types in the Santa Ynez Mountains and Santa Barbara urban area, California, USA, using Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. For all endmember selection techniques, the number of selected endmembers varied across 25 random training samples. IES was consistently more accurate than CoB/EAR, but resulted in spectral libraries more than twice as large and failed to model rare species. The hybrid endmember selection technique resulted in the highest overall accuracy and kappa values and proved to be least sensitive to the random sampling protocols, but also produced the largest spectral libraries. A modified hybrid method, in which the number of endmembers selected was limited, produced the second highest accuracies, combining the strengths of the more parsimonious endmember selection by CoB/EAR with improved endmember selection by IES. Both IES and the hybrid methods selected endmembers that successfully classified a wide range of plant species and cover types, indicating their usefulness for these applications.

► Existing and hybrid endmember selection techniques were compared.
► Endmembers were selected from spectral libraries with stratified random sampling.
► Automated iterative selection techniques produced high classification accuracies.
► Hybrid selection techniques yielded the highest accuracy (80%).
► Hybrid selection techniques were least sensitive to random sampling.