TY - GEN
T1 - Deep transfer learning with field-based measurements for large area classification
AU - Langford, Zachary L.
AU - Kumar, Jitendra
AU - Hoffman, Forrest M.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Classification of Electro-Optical (EO) datasets using Deep Neural Networks (DNNs) has lead to high-performance supervised learning algorithms. However, DNNs require a large amount of labeled data for training which often have limited availability in ecological studies. Up-to-date classification of vegetation in sensitive Arctic ecosystems continue to be a challenge. In an ecosystem undergoing rapid change, capturing the dynamics of vegetation requires the existing maps of vegetation (such as Alaska Existing Vegetation Type (AKEVT), circa 2000) to be updated based on frequent field based observation of vegetation. A method is needed to transfer the knowledge gained from field-collected observations to a larger area using DNNs. Transfer learning is a machine learning technique where a model trained on one task is re-purposed on another similar task. This paper seeks to train DNNs using field-collected observations and apply transfer learning to apply the knowledge gained to update existing vegetation map at larger scale. We test two DNN methods, (1) a deep multilayer perceptron (MLP) model and (2) siamese MLP network that uses a structure to rank similarity between inputs and can be used for training datasets with few samples and show good performance with limited datasets (e.g. few-shot learning). The results show ~90% accuracy (using the field observations for evaluation) when transfer learning is applied to a siamese network, compared to ~45% accurate when a MLP is trained on the AKEVT and evaluated on the field observations. The approach show promise for improving and update the existing vegetation maps over large areas using limited field-based observations.
AB - Classification of Electro-Optical (EO) datasets using Deep Neural Networks (DNNs) has lead to high-performance supervised learning algorithms. However, DNNs require a large amount of labeled data for training which often have limited availability in ecological studies. Up-to-date classification of vegetation in sensitive Arctic ecosystems continue to be a challenge. In an ecosystem undergoing rapid change, capturing the dynamics of vegetation requires the existing maps of vegetation (such as Alaska Existing Vegetation Type (AKEVT), circa 2000) to be updated based on frequent field based observation of vegetation. A method is needed to transfer the knowledge gained from field-collected observations to a larger area using DNNs. Transfer learning is a machine learning technique where a model trained on one task is re-purposed on another similar task. This paper seeks to train DNNs using field-collected observations and apply transfer learning to apply the knowledge gained to update existing vegetation map at larger scale. We test two DNN methods, (1) a deep multilayer perceptron (MLP) model and (2) siamese MLP network that uses a structure to rank similarity between inputs and can be used for training datasets with few samples and show good performance with limited datasets (e.g. few-shot learning). The results show ~90% accuracy (using the field observations for evaluation) when transfer learning is applied to a siamese network, compared to ~45% accurate when a MLP is trained on the AKEVT and evaluated on the field observations. The approach show promise for improving and update the existing vegetation maps over large areas using limited field-based observations.
KW - Arctic
KW - Deep learning
KW - Transfer learning
KW - Vegetation classification
UR - http://www.scopus.com/inward/record.url?scp=85078801738&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2019.00047
DO - 10.1109/ICDMW.2019.00047
M3 - Conference contribution
AN - SCOPUS:85078801738
T3 - IEEE International Conference on Data Mining Workshops, ICDMW
SP - 262
EP - 269
BT - Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
A2 - Papapetrou, Panagiotis
A2 - Cheng, Xueqi
A2 - He, Qing
PB - IEEE Computer Society
T2 - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
Y2 - 8 November 2019 through 11 November 2019
ER -