Bi, H., Xu, F., Wei, Z., Xue, Y. & Xu, Z. An active deep learning approach for minimally supervised polsar image classification. IEEE Trans. Geosci. Remote Sens. 57(11), 9378–9395 (2019).Article
ADS
Google Scholar
Yao, H., Qin, R. & Chen, X. Unmanned aerial vehicle for remote sensing applications-A review. Remote Sens. 11(12), 1443 (2019).Article
ADS
Google Scholar
Li, R., Zheng, S., Duan, C., Wang, L. & Zhang, C. Land cover classification from remote sensing images based on multi-scale fully convolutional network. Geo-spatial Inform. Sci. 25(2), 278–294 (2022).Article
Google Scholar
Ding, L., Zhang, J. & Bruzzone, L. Semantic segmentation of large-size VHR remote sensing images using a two-stage multiscale training architecture. IEEE Trans. Geosci. Remote Sens. 58(8), 5367–5376 (2020).Article
ADS
Google Scholar
Pal, M. & Mather, P. M. Support vector machines for classification in remote sensing. Int. J. Remote Sens. 26(5), 1007–1011 (2005).Article
Google Scholar
Cao, X., Yao, J., Xu, Z. & Meng, D. Hyperspectral image classification with convolutional neural network and active learning. IEEE Trans. Geosci. Remote Sens. 58(7), 4604–4616 (2020).Article
ADS
Google Scholar
Krähenbühl, P. & Koltun, V. Efficient inference in fully connected CRFS with Gaussian edge potentials. Adv. Neural Inform. Process. Syst. 24, 109–117 (2011).
Google Scholar
Ding, L., Tang, H. & Bruzzone, L. Lanet: Local attention embedding to improve the semantic segmentation of remote sensing images. IEEE Trans. Geosci. Remote Sens. 59(1), 426–435 (2020).Article
ADS
Google Scholar
Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234–241. SpringerCarion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A. & Zagoruyko, S. End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213–229. SpringerChen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K. & Yuille, A. L. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017).Article
PubMed
Google Scholar
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2881-2890).Tripathy, S. K., Kostha, H. & Srivastava, R. Ts-mda: Two-stream multiscale deep architecture for crowd behavior prediction. Multimedia Syst. 29(1), 15–31 (2023).Article
Google Scholar
Tripathy, S. K., Sudhamsh, R., Srivastava, S. & Srivastava, R. Must-pos: Multiscale spatial-temporal 3d Atrous-net and PCA guided OC-SVM for crowd panic detection. J. Intell. Fuzzy Syst. 42(4), 3501–3516 (2022).Article
Google Scholar
Ding, X., Guo, Y., Ding, G. & Han, J. Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1911–1920Arora, S., Tripathy, S. K., Gupta, R. & Srivastava, R. Exploiting multimodal CNN architecture for automated teeth segmentation on dental panoramic X-ray images. Proc. Inst. Mech. Eng. 237(3), 395–405 (2023).Article
Google Scholar
Yan, P. et al. Clustered remote sensing target distribution detection aided by density-based spatial analysis. Int. J. Appl. Earth Obs. Geoinf. 132, 104019 (2024).
Google Scholar
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R. & Bengio, Y. Show, attend and tell: Neural image caption generation with visual attention. In: International Conference on Machine Learning, pp. 2048–2057. PMLRWoo, S., Park, J., Lee, J.-Y. & Kweon, I.S. Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19Li, H., Qiu, K., Chen, L., Mei, X., Hong, L., Tao, C. Scattnet: Semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images. IEEE Geosci. Remote Sens. Lett. 18(5), 905–909 (2020)Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z. & Lu, H. Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146–3154Zhou, G., Liu, W., Zhu, Q., Lu, Y. & Liu, Y. Eca-mobilenetv3 (large)+ Segnet model for binary sugarcane classification of remotely sensed images. IEEE Trans. Geosci. Remote Sens. 60, 1–15 (2022).
Google Scholar
Zhou, G. & Liu, X. Orthorectification model for extra-length linear array imagery. IEEE Trans. Geosci. Remote Sens. 60, 1–10 (2022).
Google Scholar
Zhou, G. et al. Orthorectification of fisheye image under equidistant projection model. Remote Sens. 14(17), 4175 (2022).Article
ADS
Google Scholar
Liu, K. et al. On image transformation for partial discharge source identification in vehicle cable terminals of high‐speed trains. High Voltage (2024).Xu, H., Li, Q. & Chen, J. Highlight removal from a single grayscale image using attentive GAN. Appl. Artif. Intell. 36(1), 1988441 (2022).Article
Google Scholar
Cheng, D., Chen, L., Lv, C., Guo, L. & Kou, Q. Light-guided and cross-fusion u-net for anti-illumination image super-resolution. IEEE Trans. Circuits Syst. Video Technol. 32(12), 8436–8449 (2022).Article
Google Scholar
Zeiler, M.D. & Fergus, R. Visualizing and understanding convolutional networks. In: Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13, pp. 818–833. SpringerChen, J., Shen, D., Chen, W. & Yang, D. Hiddencut: Simple data augmentation for natural language understanding with better generalization. arXiv preprint arXiv:2106.00149 (2021).Henaff, O. Data-efficient image recognition with contrastive predictive coding. In: International Conference on Machine Learning, pp. 4182–4192. PMLRZhou, L., Zhang, C. & Wu, M. D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 182–186Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S. & Guo, B. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022He, X. et al. Swin transformer embedding UNET for remote sensing image semantic segmentation. IEEE Trans. Geosci. Remote Sens. 60, 1–15 (2022).Article
Google Scholar
Badrinarayanan, V., Kendall, A. & Cipolla, R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017).Article
PubMed
Google Scholar
Sun, K. et al. High-resolution representations for labeling pixels and regions. arXiv preprint arXiv:1904.04514 (2019).Ma, A., Wang, J., Zhong, Y. & Zheng, Z. Factseg: Foreground activation-driven small object semantic segmentation in large-scale remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2021).
Google Scholar
Yu, F. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015).Zhao, H., Qi, X., Shen, X., Shi, J. & Jia, J: Icnet for real-time semantic segmentation on high-resolution images. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 405–420Li, R., Wang, L., Zhang, C., Duan, C. & Zheng, S. A2-FPN for semantic segmentation of fine-resolution remotely sensed images. Int. J. Remote Sens. 43(3), 1131–1155 (2022).Article
Google Scholar
Cao, Y., Xu, J., Lin, S., Wei, F. & Hu, H. Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141Xing, J., Yuan, H., Hamzaoui, R., Liu, H. & Hou, J. Gqe-net: A graph-based quality enhancement network for point cloud color attribute. IEEE Trans. Image Process. 32, 6303–6317 (2023).Article
ADS
PubMed
Google Scholar
Vaswani, A. Attention is all you need. Advances in Neural Information Processing Systems (2017).Dosovitskiy, A. An image is worth 16×16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).Strudel, R., Garcia, R., Laptev, I. & Schmid, C: Segmenter: Transformer for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7262–7272Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P. & Clark, J: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLRDai, Z., Liu, H., Le, Q. V. & Tan, M. Coatnet: Marrying convolution and attention for all data sizes. Adv. Neural Inform. Process. Syst 34, 3965–3977 (2021).
Google Scholar
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q. & Wang, M: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218. SpringerVolpi, M. & Tuia, D. Dense semantic labeling of subdecimeter resolution images with convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 55(2), 881–893 (2016).Article
ADS
Google Scholar
Liu, Y., Minh Nguyen, D., Deligiannis, N., Ding, W. & Munteanu, A. Hourglass-shapenetwork based semantic segmentation for high resolution Aerial imagery. Remote Sens. 9(6), 522 (2017).Article
ADS
Google Scholar
Mou, L., Hua, Y. & Zhu, X. X. Relation matters: Relational context-aware fully convolutional network for semantic segmentation of high-resolution aerial images. IEEE Trans. Geosci. Remote Sens. 58(11), 7557–7569 (2020).Article
ADS
Google Scholar
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F. & Adam, H: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818Chen, J. et al. Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021).