Validating the Crop Identification Capability of the Spectral Variance at Key Stages (SVKS) Computed via an Object Self-Reference Combined Algorithm

Abstract

Crop-distribution information constitutes the premise of precise management for crop cultivation. Euclidean distance and spectral angle mapper algorithms (ED and SAM) mostly use the spectral similarity and difference metric (SSDM) to determine the spectral variance associated with the spatial location for crop distribution acquisition. These methods are relatively insensitive to spectral shape or amplitude variation and must reconstruct a reference curve representing the entire class, possibly resulting in notable indeterminacy in the ultimate results. Few studies utilize these methods to compute the spectral variance associated with time and to define a new index for crop identification—namely, the spectral variance at key stages (SVKS)—even though this temporal spectral characteristic could be helpful for crop identification. To integrate the advantages of sensibility and avoid reconstructing the reference curve, an object self-reference combined algorithm comprising ED and SAM (CES) was proposed to compute SVKS. To objectively validate the crop-identification capability of SVKS-CES (SVKS computed via CES), SVKS-ED (SVKS computed via ED), SVKS-SAM (SVKS computed via SAM), and five spectral index (SI) types were selected for comparison in an example of maize identification. The results indicated that SVKS-CES ranges can characterize greater interclass spectral separability and attained better identification accuracy compared to other identification indexes. In particular, SVKS-CES2 provided the greatest interclass spectral separability and the best PA (92.73%), UA (100.00%), and OA (98.30%) in maize identification. Compared to the performance of the SI, SVKS attained greater interclass spectral separability, but more non-maize fields were incorrectly identified as maize fields via SVKS usage. Owning to the accuracy-improvement capability of SVKS-CES, the omission and commission errors were obviously reduced via the combined utilization of SVKS-CES and SI. The findings suggest that SVKS-CES application is expected to further spread in crop identification.