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Combining 2D CNN and Bidirectional LSTM to Consider Spatio-Temporal Features in Crop Classification

작물 분류에서 시공간 특징을 고려하기 위한 2D CNN과 양방향 LSTM의 결합

Kwak, Geun-Ho;Park, Min-Gyu;Park, Chan-Won;Lee, Kyung-Do;Na, Sang-Il;Ahn, Ho-Yong;Park, No-Wook
곽근호;박민규;박찬원;이경도;나상일;안호용;박노욱

  • Received : 2019.09.18
  • Accepted : 2019.10.23
  • Published : 2019.10.31

Abstract

In this paper, a hybrid deep learning model, called 2D convolution with bidirectional long short-term memory (2DCBLSTM), is presented that can effectively combine both spatial and temporal features for crop classification. In the proposed model, 2D convolution operators are first applied to extract spatial features of crops and the extracted spatial features are then used as inputs for a bidirectional LSTM model that can effectively process temporal features. To evaluate the classification performance of the proposed model, a case study of crop classification was carried out using multi-temporal unmanned aerial vehicle images acquired in Anbandegi, Korea. For comparison purposes, we applied conventional deep learning models including two-dimensional convolutional neural network (CNN) using spatial features, LSTM using temporal features, and three-dimensional CNN using spatio-temporal features. Through the impact analysis of hyper-parameters on the classification performance, the use of both spatial and temporal features greatly reduced misclassification patterns of crops and the proposed hybrid model showed the best classification accuracy, compared to the conventional deep learning models that considered either spatial features or temporal features. Therefore, it is expected that the proposed model can be effectively applied to crop classification owing to its ability to consider spatio-temporal features of crops.

Keywords

Crop classification;Convolutional neural network;Long short-term memory;Spatio-temporal features

Acknowledgement

Supported by : 농촌진흥청