The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning
Abstract
In this paper, we focus on tackling the problem of automatic semantic annotation of high resolution (HR) optical satellite images, which aims to assign one or several predefined semantic concepts to an image according to its content. The main challenges arise from the difficulty of characterizing complex and ambiguous contents of the satellite images and the high human labor cost caused by preparing a large amount of training examples with high-quality pixel-level labels in fully supervised annotation methods. To address these challenges, we propose a unified annotation framework by combining discriminative high-level feature learning and weakly supervised feature transferring. Specifically, an efficient stacked discriminative sparse autoencoder (SDSAE) is first proposed to learn high-level features on an auxiliary satellite image data set for the land-use classification task. Inspired by the motivation that the encoder of the prelearned SDSAE can be regarded as a generic high-level feature extractor for HR optical satellite images, we then transfer the learned high-level features to semantic annotation. To compensate the difference between the auxiliary data set and the annotation data set, the transferred high-level features are further fine-tuned in a weakly supervised scheme by using the tile-level annotated training data. Finally, the fine-tuning process is formulated as an ultimate optimization problem, which can be solved efficiently with our proposed alternate iterative optimization method. Comprehensive experiments on a publicly available land-use classification data set and an annotation data set demonstrate the superiority of our SDSAE-based high-level feature learning method and the effectiveness of our weakly supervised semantic annotation framework compared with state-of-the-art fully supervised annotation methods.
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Cites
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BackgroundCheng, G., Han, J., Zhou, P., & Guo, L. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.10.002 · Cited by 1,007 · cited 1×
“For land-use classification experiments, we follow the setting in [2], [3], and [7] to randomly select 80% of the land-use images from each class for training and the remaining 20% of the images for testing.”
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Cited by
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Cheng, G., Han, J., & Lu, X. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2675998 · Cited by 2,926
Self-citationAID: A benchmark data set for performance evaluation of aerial scene classification
Xia, G.-S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L., & Lu, X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2685945 · Cited by 2,372
Cheng, G., Zhou, P., & Han, J. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2601622 · Cited by 1,771
Self-citationAlgorithms for semantic segmentation of multispectral remote sensing imagery using deep learning
Kemker, R., Salvaggio, C., & Kanan, C. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.014 · Cited by 558
Geography-Aware Self-Supervised Learning
Ayush, K., Uzkent, B., Meng, C., Tanmay, K., Burke, M., Lobell, D., & Ermon, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01002 · Cited by 288
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID
Long, Y., Xia, G.-S., Li, S., Yang, W., Yang, M. Y., Zhu, X. X., Zhang, L., & Li, D. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3070368 · Cited by 250
Robust Object Co-Segmentation Using Background Prior
Han, J., Quan, R., Zhang, D., & Nie, F. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2781424 · Cited by 169
Self-citationLiu, T., & Abd-Elrahman, A. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.03.006 · Cited by 103
Metadata
- Authors
- Yao, X., Han, J., Cheng, G., Qian, X., & Guo, L.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2016-06
- Volume
- 54
- Issue
- 6
- Pages
- 3660-3671
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2342880667
- Cited by
- 346
- References
- 8
- FWCI
- 54.03
- Type
- journal article
- Language
- en
- Open access
- closed
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