Hyperspectral Data Segmentation and Classification in Precision Agriculture: A Multi-Scale Analysis

The conventional pixel-oriented classification is the most commonly used approach in remote sensing for land use product extraction. The object-oriented classification based on the image segmentation is an alternative, which uses the pixel context, texture and shapes, in addition to their spectral characteristics. This paper reports on a comparative study between supervised pixel-oriented and object-oriented classifications in a precision agriculture context using three hyperspectral images. The images were acquired with the Compact Airborne Spectrographic Imager (CASI) sensor at three different altitudes, providing three different spatial resolutions: 1, 2 and 4 m. Pixel-oriented classifications were carried out using the maximum likelihood algorithm, and object-oriented classifications with a hierarchical segmentation and nearest neighbor classifier. The raw CASI data were transformed to absolute ground reflectance using calibration coefficients determined in the laboratory and the CAM5S radiative transfer code for atmospheric corrections. After segmentation, statistical comparison on the mean difference to neighbor objects confirmed that the segments had minimum mixing effects in respect to other segmentation levels and neighboring ground entities. After accuracy analysis on the classifications, the segmentation process allowed for the use of a spatially coarser hyperspectral image (4 m with kappa of 0.8268) to achieve better results than pixel oriented classification of spatially finer hyperspectral image (1 m with kappa of 0.7730), in the task of delineating agricultural classes.