Abstract
In this manuscript, a land-cover classification (LCC) mechanism has been investigated for the practical situations where a remotely sensed aerial image can be annotated by more than one land-cover class. A striking factor for the development of the proposed technique is its ability to operate under situations when there is scarcity of labelled images in the training set, thereby alleviating extensive manual collection of multi-label ground truth information for LCC. The solution using very few labelled training images has been envisioned through a semi-supervised deep learning-based methodology where templates have been generated corresponding to each of the classes present in the training images. Thereafter, each of the test images is assigned to multiple classes based on their similarity with each of the class templates. Experimentation conducted on UCM and AID benchmark multi-label aerial image datasets suggests promising results for the proposed approach.
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Chakraborty, S., Agarwal, N., Roy, M. (2021). A Deep Semi-supervised Approach for Multi-label Land-Cover Classification Under Scarcity of Labelled Images. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_1
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