Abstract
Abstract. Over the past few years, many research works have utilized Convolutional Neural Networks (CNN) in the development of fully automated change detection pipelines from high resolution satellite imagery. Even though CNN architectures can achieve state-of-the-art results in a wide variety of vision tasks, including change detection applications, they require extensive amounts of labelled training examples in order to be able to generalize to new data through supervised learning. In this work we experiment with the implementation of a semi-supervised training approach in an attempt to improve the image semantic segmentation performance of models trained using a small number of labelled image pairs by leveraging information from additional unlabelled image samples. The approach is based on the Mean Teacher method, a semi-supervised approach, successfully applied for image classification and for sematic segmentation of medical images. Mean Teacher uses an exponential moving average of the model weights from previous epochs to check the consistency of the model’s predictions under various perturbations. Our goal is to examine whether its application in a change detection setting can result in analogous performance improvements. The preliminary results of the proposed method appear to be compatible to the results of the traditional fully supervised training. Research is continuing towards fine-tuning of the method and reaching solid conclusions with respect to the potential benefits of the semi-supervised learning approaches in image change detection applications.
Highlights
For the past few decades, the development of automatic change detection applications has been an active research area in remote sensing
In this work we implemented a Mean Teacher semi-supervised training setup following the work of Li et al (2021) and applied it to a Change Detection setting to explore the potential benefits of the method compared to a fully supervised training process, especially when only a few labelled training examples are available
We expected that the consistency regularization constraint would allow the model to learn useful information from unlabelled data, improving the model’s performance when limited labelled samples are available, which is often the case in change detection (CD) applications
Summary
For the past few decades, the development of automatic change detection applications has been an active research area in remote sensing. CNN models are specialized to work with data that have grid-like topology, are easier to train and can generalize better than traditional fully connected neural networks Thanks to their stacks of convolutional and pooling layers CNN can learn useful context information from images by taking advantage of the hierarchical structure of an image's features. CNN approaches based on encoder-decoder architectures have been successfully applied to the change detection (CD) task (Peng et al, 2019; Zhang et al, 2019a; Bousias Alexakis & Armenakis, 2020). These models perform image semantic segmentation in an end-to-end manner producing state-of-the-art results. The networks take as input a pair of coregistered image instances collected at different time periods and produce a prediction mask classifying each pixel location as changed or unchanged
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