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Development of a spatially explicit active learning method for crop type mapping from satellite image time series

Kaijage, Beatrice (2021) Development of a spatially explicit active learning method for crop type mapping from satellite image time series.

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Link to full-text:https://library.itc.utwente.nl/papers_2021/msc/gfm/kaijage.pdf
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Abstract:Insufficient training samples for effective classification is one of the drawbacks of existing supervised classification methods. Collecting training samples via field campaigns is time-consuming and costly, especially when gathering data from a vast area. As a result, Remote Sensing(RS) requires approaches that can work with a small number of training samples while still providing excellent accuracy. Active Learning (AL) is one of these approaches. AL is a Machine Learning(ML) method whose purpose is to attain satisfactory classification results with a small number of training datasets, resulting in accurate information extraction at low annotation costs. AL reduces the training sample size required for training a classifier up to tenfold by identifying the most informative and diverse samples from a set of unlabeled samples. Informative samples are those for which a classifier has difficulty classifying or labeling them, and sample diversity refers to how dissimilar the selected samples are from one another. Most of the existing AL approaches are dedicated to querying informative samples based on their spectral characteristics, neglecting spatial information. This research aims to develop a spatially explicit AL method for crop type mapping using Satellite Image Time Series(SITS) and assesses its performance compared to the existing AL techniques that ignore the spatial component in the selection of informative samples. The developed AL method that includes the spatial component and the AL technique that excludes the spatial component were both evaluated using crop data and Sentinel-2 time-series images collected in 2019. The two AL techniques were compared to the classification performance obtained utilizing the whole training dataset. The AL method with the spatial component used 27% of the entire training sample dataset and 57% of the informative training samples acquired from the AL method that excludes the spatial component to achieve an overall accuracy of 80%.This accuracy is almost identical to the overall accuracy of the AL method without the spatial component (82%) and when using the entire data set (84% ). Comparisons were made using other metrics like Kappa statistic, user’s and producer’s accuracy and quality of the sample design. The developed spatially explicit AL method showed a good performance with a low number of samples. In addition, it performed better in the case of crop types with high interclass similarities like potatoes and maize. A challenge was faced in classifying mixed classes consisting of different land cover classes. Given these findings, adding the spatial component in AL is a critical contribution to the field of agriculture, especially in developing countries where we do not have access to a large number of samples required for accurate crop mapping and monitoring due to the high cost of sample acquisition.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
Link to this item:https://purl.utwente.nl/essays/88638
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