UCL: Unsupervised Curriculum Learning for water body classification from remote sensing imagery

• DOI: 10.1016/j.jag.2021.102568
• Publication Date: 2021-10-29T11:12:19Z
• Authors: Nosheen Abid; Muhammad Shahzad; Muhammad Imran Malik; Ulrich Schwanecke; Adrian Ulges; György Kovács; Faisal Shafait
• Keywords: Physical geography; Computer Sciences; 0211 other engineering and technologies; 02 engineering and technology; Aircraft Imagery; Water classification; 6. Clean water; ddc:; GB3-5030; Remote Sensing; Unsupervised Curriculum Learning; Environmental sciences; Datavetenskap (datalogi); Deep Learning; GE1-350; Sentinel-2; Multi-scale Classification

This paper presents a Convolutional Neural Networks (CNN) based Unsupervised Curriculum Learning approach for the recognition of water bodies to overcome the stated challenges for remote sensing based RGB imagery. The unsupervised nature of the presented algorithm eliminates the need for labelled training data. The problem is cast as a two class clustering problem (water and non-water), while clustering is done on deep features obtained by a pre-trained CNN. After initial clusters have been identified, representative samples from each cluster are chosen by the unsupervised curriculum learning algorithm for fine-tuning the feature extractor. The stated process is repeated iteratively until convergence. Three datasets have been used to evaluate the approach and show its effectiveness on varying scales: (i) SAT-6 dataset comprising high resolution aircraft images, (ii) Sentinel-2 of EuroSAT, comprising remote sensing images with low resolution, and (iii) PakSAT, a new dataset we created for this study. PakSAT is the first Pakistani Sentinel-2 dataset designed to classify water bodies of Pakistan. Extensive experiments on these datasets demonstrate the progressive learning behaviour of UCL and reported promising results of water classification on all three datasets. The obtained accuracies outperform the supervised methods in domain adaptation, demonstrating the effectiveness of the proposed algorithm.