Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 1; p. 162
• Main Authors: Yin, Leikun, You, Nanshan, Zhang, Geli, Huang, Jianxi, Dong, Jinwei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2020
• Subjects: Accuracy; Agricultural development; Agricultural production; Algorithms; Cereal crops; Classification; crop mapping; Crop planting; Crops; Feature selection; Food; Food security; Frequency; Image classification; Mapping; Optimization; random forest; Regional development; Regional planning; Remote sensing; Rice; Satellite imagery; Satellites; sentinel-2; separability index; Soybeans; spectro-temporal feature; Temporal variations; Time series; Traditional foods; Vegetation; China; Heilongjiang China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12010162

Accurate crop planting area information is of significance for understanding regional food security and agricultural development planning. While increasing numbers of medium resolution satellite imagery and improved classification algorithms have been used for crop mapping, limited efforts have been made in feature selection, despite its vital impacts on crop classification. Furthermore, different crop types have their unique spectral and phenology characteristics; however, the different features of individual crop types have not been well understood and considered in previous studies of crop mapping. Here, we examined an optimized strategy to integrate specific features of individual crop types for mapping an improved crop type layer in the Sanjiang Plain, a new food bowl in China, by using all Sentinel-2 time series images in 2018. First, an automatic spectro-temporal feature selection (ASTFS) method was used to obtain optimal features for individual crops (rice, corn, and soybean), including sorting all features by the global separability indices for each crop and removing redundant features by accuracy changes when adding new features. Second, the ASTFS-based optimized feature sets for individual crops were used to produce three crop probability maps with the Random Forest classifier. Third, the probability maps were then composited into the final crop layer by considering the probability of each crop at every pixel. The resultant crop layer showed an improved accuracy (overall accuracy = 93.94%, Kappa coefficient = 0.92) than the other classifications without such a feature optimizing process. Our results indicate the potential of the ASTFS method for improving regional crop mapping.