Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite Imagery

Chopra, MuskaanChandigarh College of Engineering and Technology, Punjab University, Chandigarh, India (författare)

This work presents a novel domain adaption paradigm for studying contrastive self-supervised representation learning and knowledge transfer using remote sensing satellite data. Major state-of-the-art remote sensing visual domain ef-forts primarily focus on fully supervised learning approaches that rely entirely on human annotations. On the other hand, human annotations in remote sensing satellite imagery are always subject to limited quantity due to high costs and domain expertise, making transfer learning a viable alternative. The proposed approach investigates the knowledge transfer of self-supervised representations across the distinct source and target data distributions in depth in the remote sensing data domain. In this arrangement, self-supervised contrastive learning- based pretraining is performed on the source dataset, and downstream tasks are performed on the target datasets in a round-robin fashion. Experiments are conducted on three publicly avail-able datasets, UC Merced Landuse (UCMD), SIRI-WHU, and MLRSNet, for different downstream classification tasks versus label efficiency. In self-supervised knowledge transfer, the pro-posed approach achieves state-of-the-art performance with label efficiency labels and outperforms a fully supervised setting. A more in-depth qualitative examination reveals consistent evidence for explainable representation learning. The source code and trained models are published on GitHub1.

Chhipa, Prakash ChandraLuleå tekniska universitet,EISLAB(Swepub:ltu)prachh (författare)
Mengi, GopalChandigarh College of Engineering and Technology, Punjab University, Chandigarh, India (författare)
Gupta, VarunChandigarh College of Engineering and Technology, Punjab University, Chandigarh, India (författare)
Liwicki, MarcusLuleå tekniska universitet,EISLAB(Swepub:ltu)marliw (författare)
Chandigarh College of Engineering and Technology, Punjab University, Chandigarh, IndiaEISLAB (creator_code:org_t)

Ingår i:IJCNN 2023 - International Joint Conference on Neural Networks, Conference Proceedings: Institute of Electrical and Electronics Engineers Inc.97816654886869781665488679

Av författaren/redakt...: Chopra, Muskaan; Chhipa, Prakash ...; Mengi, Gopal; Gupta, Varun; Liwicki, Marcus

Om ämnet

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Elektroteknik oc ...; och Signalbehandling

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