Multitask learning for large-scale semantic change detection

By Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau. Computer Vision and Image Understanding, 2018

409 citations recorded by the source corpus.

Abstract Change detection is one of the main problems in remote sensing, and is essential to the accurate processing and understanding of the large scale Earth observation data available. Most of the recently proposed change detection methods bring deep learning to this context, but change detection labelled datasets which are openly available are still very scarce, which limits the methods that can be proposed and tested. In this paper we present the first large scale very high resolution semantic change detection dataset, which enables the usage of deep supervised learning methods for semantic change detection with very high resolution images. The dataset contains coregistered RGB image pairs, pixel-wise change information and land cover information. We then propose several supervised learning methods using fully convolutional neural networks to perform semantic change detection. Most notably, we present a network architecture that performs change detection and land cover mapping simultaneously, while using the predicted land cover information to help to predict changes. We also describe a sequential training scheme that allows this network to be trained without setting a hyperparameter that balances different loss functions and achieves the best overall results.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/f03b13d30654092129b18487393bec5cc51df3c2

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Citations in the corpus

Transformers in Remote Sensing: A Survey (2022, methodology): “SwinSUNet [56] Change Detection CCD [82], WHU [60], OSCD [98], HRSCD [99] F1 score Introduces a Swin transformer-based network with a Siamese U-shaped structure having encoder, fusion and decoder modules.”
ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model (2024, methodology): “Building on [35], Ding et al. [39] proposed a more innovative architecture to address the insufficient exchange of information between the semantic and change branches, introducing new loss functions and supervision signals to force the network to correlate two branches.”
Change Detection on Remote Sensing Images Using Dual-Branch Multilevel Intertemporal Network (2023, methodology): “…on bitemporal RS images, several SOTA models are picked as the competitors, including FC-EF [47], FC-Siam-Di [47], FC-Siam-Conc [47], FresUNet [48], image fusion network (IFNet) [35], dual-task constrained deep siamese convolutional network (DTCDSCN) [40], densely connected siamese network…”
SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection (2022, methodology): “The HRSCD [47] dataset contains 291 pairs of aerial images with a spatial resolution of 50cm per pixel.”
Self-Supervised Change Detection in Multiview Remote Sensing Images (2021, methodology): “supervised method (FC-EF and FC-EF-Res), we used the 14 bi-temporal training images considered in the previous work [46].”
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis (2020, background): “[79] have proposed an integrated network based on deep FCNNs that performs a land cover mapping and change detection simultaneously, using information from the land cover mapping branches to help with change detection.”