Segmentation of Low-Cost Remote Sensing Images Combining Vegetation Indices and Mean Shift

The development of low-cost remote sensing systems is important in small agriculture business, particularly in developing countries, to allow feasible use of images to gather information. However, images obtained through such systems with uncalibrated cameras have often illumination variations, shadows, and other elements that can hinder the analysis by image processing techniques. This letter investigates the combination of vegetation indices (color index of vegetation extraction, visual vegetation index, and excess green) and the mean-shift algorithm, based on the local density estimation in the color space on images acquired by a low-cost system. The objective is to detect green coverage, gaps, and degraded areas. The results showed that combining local density estimation and vegetation indices improves the segmentation accuracy when compared with the competing methods. It deals well with images in different conditions and with regions of imbalanced sizes, confirming the practical application of the low-cost system.