Sentinel 2 Image Scene Classifica- tion: A Comparison Between Bands and Spectral Indices.

Abstract

Given the continuous increase in the global population, the food manufacturers are advocated to either intensify the use of cropland or expand the farmland, making land cover and land usage dynamics mapping vital in the area of remote sensing. In this regard, identifying and classifying a high-resolution satellite imagery scene is a prime challenge. Several approaches have been proposed either by using static rule-based thresholds (with limitation of diversity) or neural network (with data-dependent limitations). This paper adopts an inductive approach to build classifiers from spectral reflectances, comparing usefulness of the various spectral indices to raw bands information. More specifically, it considers Sentinel2 data for six classes Scene Classification (Water, Shadow, Cirrus, Cloud, Snow and Other). The experimental results show that using raw bands performs equally well, claiming that raw bands information can be used as a replacement of the spectral indices.