Research on Target Detection and Recognition Algorithms in Remote Sensing Images

[1] Li, L. (2021). Target detection network for sar images based on semi-supervised learning and attention mechanism. Remote Sensing, 13. Search in Google Scholar

[2] Chen, H., Song, J., Wu, C., Du, B., & Yokoya, N. (2023). Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange. ISPRS journal of photogrammetry and remote sensing(Dec.), 206. Search in Google Scholar

[3] Feng, S., Gao, M., Jin, X., Zhao, T., & Yang, F. (2024). Fine-grained damage detection of cement concrete pavement based on uav remote sensing image segmentation and stitching. Measurement, 226. Search in Google Scholar

[4] Wang, C., Wang, Q., Wu, H., Zhao, C., Teng, G., & Li, J. (2021). Low-altitude remote sensing opium poppy image detection based on modified yolov3. Remote Sensing, 13(11), 2130. Search in Google Scholar

[5] Yang, W., Gao, X., Zhang, C., Tong, F., Chen, G., & Xiao, Z. (2021). Bridge extraction algorithm based on deep learning and high-resolution satellite image. Scientific Programming. Search in Google Scholar

[6] Zhang, R., Yang, S., Jin, J., Zhang, P., Wang, L., & Hu, C., et al. (2023). Cobble edge detection algorithm based on digital image processing. Journal of Applied Remote Sensing, 17, 026502 - 026502. Search in Google Scholar

[7] Miao, Y. (2021). Review of image classification algorithms based on convolutional neural networks. Remote Sensing, 13. Search in Google Scholar

[8] Wei, M., Tang, J., Tang, H., Zhao, R., & Lin, R. (2021). Adoption of convolutional neural network algorithm combined with augmented reality in building data visualization and intelligent detection. Complexity, 2021(4), 1-13. Search in Google Scholar

[9] Cheng, B., Li, Z., Xu, B., Yao, X., Ding, Z., & Qin, T. (2021). Structured object-level relational reasoning cnn-based target detection algorithm in a remote sensing image. Remote Sensing, 13(2), 281. Search in Google Scholar

[10] Wang, W., Nie, T., Fu, T., Ren, J., & Jin, L. (2017). A novel method of aircraft detection based on high-resolution panchromatic optical remote sensing images. Sensors, 17(5). Search in Google Scholar

[11] Peng, C., Li, Y., Shang, R., & Jiao, L. (2023). Rsbnet: one-shot neural architecture search for a backbone network in remote sensing image recognition. Neurocomputing. Search in Google Scholar

[12] Dianqing, Y., & Yanping, M. (2022). Remote sensing landslide target detection method based on improved faster r-cnn. Journal of Applied Remote Sensing, 16(4), -. Search in Google Scholar

[13] Amrani, M., & Jiang, F. (2017). Deep feature extraction and combination for synthetic aperture radar target classification. Journal of Applied Remote Sensing, 11(4), 1. Search in Google Scholar

[14] Wang, C., Liu, H., Shen, Y., Zhao, K., Xing, H., & Wu, H. (2020). High-resolution remote-sensing image-change detection based on morphological attribute profiles and decision fusion. Complexity, 2020. Search in Google Scholar

[15] Zhang, H., & Han, J. (2020). Mathematical models for information classification and recognition of multi-target optical remote sensing images. Open Physics, 18(1), 951-960. Search in Google Scholar

[16] Chatterjee, S., Bhattacharyya, S., Saha, S., Halder, A., & Mahapatra, P. R. S. (2024). Detection of dense built-up area in low-resolution satellite images using deep learning and dbscan approaches. International Journal of Pattern Recognition and Artificial Intelligence, 38(04). Search in Google Scholar

[17] Qi, J., Chen, H., & Chen, F. (2022). Extraction of landslide features in uav remote sensing images based on machine vision and image enhancement technology. Neural computing & applications(15), 34. Search in Google Scholar

[18] Li, W., He, C., Fu, H., Zheng, J., & Luk, W. (2019). A real-time tree crown detection approach for large-scale remote sensing images on fpgas. Remote Sensing, 11(9). Search in Google Scholar