• Title/Summary/Keyword: Crop Classification

Search Result 263, Processing Time 0.024 seconds

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
    • Korean Journal of Remote Sensing
    • /
    • v.35 no.5_1
    • /
    • pp.681-692
    • /
    • 2019

  • 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.

  • https://doi.org/10.7780/kjrs.2019.35.5.1.5 인용 PDF KSCI HTML

Classification of Crop Cultivation Areas Using Active Learning and Temporal Contextual Information (능동 학습과 시간 문맥 정보를 이용한 작물 재배지역 분류)

  • KIM, Ye-Seul;YOO, Hee-Young;PARK, No-Wook;LEE, Kyung-Do
    • Journal of the Korean Association of Geographic Information Studies
    • /
    • v.18 no.3
    • /
    • pp.76-88
    • /
    • 2015

  • This paper presents a classification method based on the combination of active learning with temporal contextual information extracted from past land-cover maps for the classification of crop cultivation areas. Iterative classification based on active learning is designed to extract reliable training data and cultivation rules from past land-cover maps are quantified as temporal contextual information to be used for not only assignment of training data but also relaxation of spectral ambiguity. To evaluate the applicability of the classification method proposed in this paper, a case study with MODIS time-series vegetation index data sets and past cropland data layers(CDLs) is carried out for the classification of corn and soybean in Illinois state, USA. Iterative classification based on active learning could reduce misclassification both between corn and soybean and between other crops and non crops. The combination of temporal contextual information also reduced the over-estimation results in major crops and led to the best classification accuracy. Thus, these case study results confirm that the proposed classification method can be effectively applied for crop cultivation areas where it is not easy to collect the sufficient number of reliable training data.

  • https://doi.org/10.11108/kagis.2015.18.3.076 인용 PDF KSCI