Object-oriented crop classification using multitemporal ETM+ SLC-off imagery and random forest

Abstract

The utility of Enhanced Thematic Mapper Plus (ETM+) has been diminished since the 2003 scan-line corrector (SLC) failure. Uncorrected images have data gaps of approximately 22% and gap-filling schemes have been developed to improve their usability. We present a method to classify a northeast Montana agricultural landscape using ETM+ SLC-off imagery without gap-filling. We use multitemporal data analysis and employ an object-oriented approach to define objects, agricultural fields, with cadastral data. This approach was assessed by comparison to a pixel-based approach. Results indicate that an ETM+ SLC-off image can be classified with better than 85% overall accuracy without gap-filling.