Impact of vector quantization compression on hyperspectral data in the retrieval accuracies of crop chlorophyll content for precision agriculture

Abstract

In this study, impacts of vector quantization compression on prediction of leaf chlorophyll content of crops for the application to precision agriculture were evaluated. The compression algorithm tested in this paper is called successive approximation multi-stage vector quantization (SAMVQ). The hyperspectral data used were acquired by CASI over corn fields at L' Acadie experimental farm (Agriculture and Agri-Food Canada) during the summer of 2000. Nine zones in the corn fields with different fertilization levels (no fertilization, intermediate fertilization, and over-fertilization) were used to evaluate the difference between the leaf chlorophyll contents obtained from original and reconstructed reflectance data cubes. The root mean square errors (RMSEs) and the correlations between the chlorophyll content derived from the original data cube and that derived from the reconstructed data cubes were calculated in the nine zones. The spatial variability of chlorophyll content in the nine zones was also examined for the images of chlorophyll content created from the original and reconstructed reflectance data cubes. The results show that the chlorophyll content image created from the reconstructed reflectance data cube corresponding to SAMVQ with a compression ratio of 20 maintains good agreement with that derived from the original reflectance data cube in the nine zones in terms of estimated pigment mean and spatial variability. As a result, SAMVQ with a compression ratio of 20 is considered acceptable for the retrieval of crop chlorophyll content from CASI hyperspectral data for agriculture corn crops.