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Poster: Generative-Discriminative Crop Type Identification using Satellite Images

Citation Author(s):
Nan Qiao, Yi Zhao, Ruei-Sung Lin, Bo Gong, Zhongxiang Wu, Mei Han, Jiashu Liu
Submitted by:
Nan Qiao
Last updated:
9 November 2019 - 7:23pm
Document Type:
Poster
Document Year:
2019
Event:

Abstract 

Abstract: 

Crop type identification refers to distinguishing certain crop from other landcovers, which is an essential and crucial task in agricultural monitoring. Satellite images are good data input for identifying different crops since satellites capture relatively wider area and more spectral information. Based on prior knowledge of crop phenology, multi-temporal images are stacked to extract the growth pattern of varied crops. In this paper, we proposed a machine learning model which combines generative and discriminative models and achieved averaged AP score of 0.903 overall tested crops and regions under the limitation of small datasets and label noise using satellite images taken at different times.

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Poster: Generative-Discriminative Crop Type Identification using Satellite Images

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