National-scale cropland mapping based on spectral-temporal features and outdated land cover information.

ABSTRACT: The lack of sufficient ground truth data has always constrained supervised learning, thereby hindering the generation of up-to-date satellite-derived thematic maps. This is all the more true for those applications requiring frequent updates over large areas such as cropland mapping. Therefore, we present a method enabling the automated production of spatially consistent cropland maps at the national scale, based on spectral-temporal features and outdated land cover information. Following an unsupervised approach, this method extracts reliable calibration pixels based on their labels in the outdated map and their spectral signatures. To ensure spatial consistency and coherence in the map, we first propose to generate seamless input images by normalizing the time series and deriving spectral

SUBMITTER: Waldner F 

PROVIDER: S-EPMC5560701 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature