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.

本文提出了一种基于卷积神经网络 (CNN) 的无监督课程学习方法，用于识别水体，以克服基于遥感的 RGB 图像所面临的挑战。所提出算法的无监督性质消除了对标记训练数据的需要。该问题被视为两类聚类问题（水和非水），而聚类是在由预训练的 CNN 获得的深层特征上完成的。在识别出初始集群后，无监督课程学习算法从每个集群中选择有代表性的样本，以对特征提取器进行微调。迭代地重复上述过程直到收敛。三个数据集已用于评估该方法并显示其在不同规模上的有效性：(i) 包含高分辨率飞机图像的 SAT-6 数据集，(ii) EuroSAT 的 Sentinel-2，包含低分辨率遥感图像，以及 (iii) PakSAT，我们为本研究创建的新数据集。PakSAT 是第一个巴基斯坦 Sentinel-2 数据集，旨在对巴基斯坦的水体进行分类。对这些数据集的大量实验证明了 UCL 的渐进式学习行为，并报告了对所有三个数据集进行水分类的有希望的结果。获得的精度在域适应方面优于监督方法，证明了所提出算法的有效性。对这些数据集的大量实验证明了 UCL 的渐进式学习行为，并报告了对所有三个数据集进行水分类的有希望的结果。获得的精度在域适应方面优于监督方法，证明了所提出算法的有效性。对这些数据集的大量实验证明了 UCL 的渐进式学习行为，并报告了对所有三个数据集进行水分类的有希望的结果。获得的精度在域适应方面优于监督方法，证明了所提出算法的有效性。