Collecting large annotated datasets in Remote Sensing is often expensive and thus can become a major obstacle for training advanced machine learning models. Common techniques of addressing this issue, based on the underlying idea of pre-training the Deep Neural Networks (DNN) on freely available large datasets, cannot be used for Remote Sensing due to the unavailability of such large-scale labeled datasets and the heterogeneity of data sources caused by the varying spatial and spectral resolution of different sensors. Self-supervised learning is an alternative approach that learns feature representation from unlabeled images without using any human annotations. In this paper, we introduce a new method for land cover mapping by using a clustering-based pretext task for self-supervised learning. We demonstrate the effectiveness of the method on two societally relevant applications from the aspect of segmentation performance, discriminative feature representation learning, and the underlying cluster structure. We also show the effectiveness of the active sampling using the clusters obtained from our method in improving the mapping accuracy given a limited budget of annotating.
Consensus Subspace Clustering
One significant challenge in the field of supervised deep learning is the lack of large-scale labeled datasets for many problems. In this paper, we propose Consensus Spectral Clustering (CSC), which leverages the strengths of convolutional autoencoders and spectral clustering to provide pseudo labels for image data. This data can be used as weakly-labeled data for training and evaluating classifiers which require supervision. The primary weaknesses of previous works lies in their inability to isolate the object of interest in an image and cluster similar images together. We address these issues by denoising input images to remove pixels which do not contain data pertinent to the target. Additionally, we introduce a voting method for label selection to improve the clustering results. Our extensive experimentation on several benchmark datasets demonstrates that the proposed CSC method achieves competitive performance with state-of-the-art methods.
- Award ID(s):
- 1909707
- Publication Date:
- NSF-PAR ID:
- 10342433
- Journal Name:
- IEEE International Conference on Tools with Artificial Intelligence
- Volume:
- 33
- Page Range or eLocation-ID:
- 391 to 395
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1109/ICTAI52525.2021.00064
