Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data.

ABSTRACT: Substantial efforts have been made to identify crop types by region, but few studies have been able to classify crops in early season, particularly in regions with heterogeneous cropping patterns. This is because image time series with both high spatial and temporal resolution contain a number of irregular time series, which cannot be identified by most existing classifiers. In this study, we firstly proposed an improved artificial immune network (IAIN), and tried to identify major crops in Hengshui, China at early season using IAIN classifier and short image time series. A time series of 15-day composited images was generated from 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Near-infrared (NIR) band and normalized difference vegetation index (NDVI) were selected as optimal band

SUBMITTER: Hao P 

PROVIDER: S-EPMC6120442 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature