Land cover classification using satellite images: an approach based on tim-series composites and ensemble of supervised classifiers

Abstract

Remotely-sensed images have been used for a long time in both military and civilization applications. The images could be collected from satellites, airborne platforms or Unmanned Aerial Vehicles (UAVs). Among the three, satellite images have gained popularity due to large coverage, available data and so on. In general, remotelysensed images store information about Earth object’s reflectance of lights, i.e. Sun’s light in passive remote sensing [1]. Therefore, the images contain itself lots of valuable information of the Earth’s surface or even under the surface. Applications of remotely-sensed images are diverse. For example, satellite images could be used in agriculture, forestry, geology, hydrology, sea ice, land cover mapping, ocean and coastal [1]. In agriculture, two important tasks are crop type mapping and crop monitoring. Crop type mapping is the process of identification crops and its distribution over an area. This is the first step to crop monitoring which includes crop yield estimation, crop condition assessment, and so on. To these aims, satellite images are efficient and reliable means to derive the required information [1]. In forestry, potential applications could be deforestation mapping, species identification and forest fire mapping. In the forest where human access is restricted, satellite imagery is an unique source of4 information for management and monitoring purposes. In geology, satellite images could be used for structural mapping and terrain analysis. In hydrology, some possible applications cloud be flood delineation and mapping, river change detection, irrigation canal leakage detection, wetlands mapping and monitoring, soil moisture monitoring, and a lot of other researches. Iceberg detection and tracking is also done via satellite data. Furthermore, air pollution and meteorological monitoring could be possible from satellite perspective. In general, many of the applications more or less relate to land cover mapping, i.e. agriculture, flood mapping, forest mapping, sea ice mapping, and so on.