期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 15, 期 6, 页码 922-926出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2018.2813436
关键词
Building type; cost-sensitive multitask active learning (CSMTAL); LiDAR; remote sensing; roof type; support vector machines (SVMs); very high-resolution imagery
类别
资金
- German Federal Ministry for Economic Affairs and Energy's initiative Smart Data Innovations From Data through Smart Data for Catastrophe Management [01MD15008B]
- Helmholtz Association [pre_DICT PD-305]
We propose a novel cost-sensitive multitask active learning (CSMTAL) approach. Cost-sensitive active learning (CSAL) methods were recently introduced to specifically minimize labeling efforts emerging from ground surveys. Here, we build upon a CSAL method but compile a set of unlabeled samples from a learning set which can be considered relevant with respect to multiple target variables. To this purpose, a multitask meta-protocol based on alternating selection is implemented. It comprises a so-called one-sided selection (i.e., single-task AL selection for a reference target variable with simultaneous labeling of the residual target variables) with a changing leading variable in an iterative selection process. Experimental results are obtained for the city of Cologne, Germany. The target variables to be predicted, using features from remote sensing and a support vector machine framework, are building type and roof type. Comparative model accuracy evaluations underline the capability of the CSMTAL method to provide beneficial solutions with respect to a random sampling strategy and noncost-sensitive multitask active sampling.
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