Extreme Learning Machine-based Crop Classification using ALOS/PALSAR Images

Abstract

Classification maps are required for agricultural management and the estimation of agricultural disaster compensation. The extreme learning machine (ELM), a newly developed single hidden layer neural network is used as a supervised classifier for remote sensing classifications. In this study, the ELM was evaluated to examine its potential for multi-temporal ALOS/PALSAR images for the classification of crop type. In addition, the k-nearest neighbor algorithm (k-NN), one of the traditional classification methods, was also applied for comparison with the ELM. In the study area, beans, beets, grasses, maize, potato, and winter wheat were cultivated; and these crop types in each field were identified using a data set acquired in 2010. The result of ELM classification was superior to that of k-NN; and overall accuracy was 79.3%. This study highlights the advantages of ALOS/PALSAR images for agricultural field monitoring and indicates the usefulness of regular monitoring using the ALOS-2/PALSAR-2 system.

Mapping crop cover using multi-temporal Landsat 8 OLI imagery

“…The study area is located in the western Tokachi plain, Hokkaido, Japan (Figure 1, 142°55′12″ to 143°05′51″ E, 42°52′48″ to 43°02′42″ N) (Sonobe et al, 2015b). The area has a humid continental climate characterised by warm summers and cold winters, with an average annual temperature of 6°C and an annual precipitation of 920 mm.…”