The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Semantic labeling in very high resolution images via a self-cascaded convolutional neural network
Open accessAbstract
Abstract Semantic labeling for very high resolution (VHR) images in urban areas, is of significant importance in a wide range of remote sensing applications. However, many confusing manmade objects and intricate fine-structured objects make it very difficult to obtain both coherent and accurate labeling results. For this challenging task, we propose a novel deep model with convolutional neural networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet). Specifically, for confusing manmade objects, ScasNet improves the labeling coherence with sequential global-to-local contexts aggregation. Technically, multi-scale contexts are captured on the output of a CNN encoder, and then they are successively aggregated in a self-cascaded manner. Meanwhile, for fine-structured objects, ScasNet boosts the labeling accuracy with a coarse-to-fine refinement strategy. It progressively refines the target objects using the low-level features learned by CNN’s shallow layers. In addition, to correct the latent fitting residual caused by multi-feature fusion inside ScasNet, a dedicated residual correction scheme is proposed. It greatly improves the effectiveness of ScasNet. Extensive experimental results on three public datasets, including two challenging benchmarks, show that ScasNet achieves the state-of-the-art performance.
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Cites
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Cited by
Zhang, C., Jiang, W., Zhang, Y., Wang, W., Zhao, Q., & Wang, C. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3144894 · Cited by 325
Neupane, B., Horanont, T., & Aryal, J. (2021) · Remote Sensing · doi:10.3390/rs13040808 · Cited by 247
Triplet adversarial domain adaptation for pixel-level classification of VHR remote sensing images
Yan, L., Fan, B., Liu, H., Huo, C., Xiang, S., & Pan, C. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2958123 · Cited by 115
Self-citation
Metadata
- Authors
- Liu, Y., Fan, B., Wang, L., Bai, J., Xiang, S., & Pan, C.
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 145
- Pages
- 78-95
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2778539913
- Cited by
- 290
- References
- 20
- FWCI
- 17.82
- Type
- journal article
- Language
- en
- Open access
- green
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- Liu, Y., Fan, B., Wang, L., Bai, J., Xiang, S., & Pan, C. (2018). Semantic labeling in very high resolution images via a self-cascaded convolutional neural network. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2017.12.007
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