[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Browse::
Journal Info::
Guide for Authors::
Submit Manuscript::
Articles archive::
For Reviewers::
Contact us::
Site Facilities::
Reviewers::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 14, Issue 1 (9-2024) ::
JGST 2024, 14(1): 63-83 Back to browse issues page
Target Detection in Panchromatic Remotely Sensed Images Using Deep Learning Algorithms: A Review
Mohammad Ebrahim Aghili , Amir Aghabalaei *
Abstract:   (136 Views)
In recent years, rapid advancements have been made in the use of deep learning for the detection of targets in high-resolution panchromatic remotely sensed images. This study reviews ongoing research in this field and expresses the innovative findings of existing methods. A wide range of desired detections including buildings, vehicles, aircraft, and ships is covered. Unlike traditional methods, which rely on handcrafted features, deep neural networks provide high-level feature learning and target discrimination capabilities. End-to-end training enables networks to directly learn significant representations from images for accurate recognition. Important architectures reviewed include Faster R-CNN, SSD, and YOLO, which are often customized for target detection. In addition to the direct use of panchromatic images, they are used to improve detection performance by integration with other images such as multispectral and infrared. Utilizing spatial information in panchromatic images has increased the accuracy of target detection. Mechanisms of attention, dense connections, and the combination of multi-scale features have been examined to enhance feature learning and improve results in complex scenes. Data augmentation strategies, transfer learning, exploration of low-quality samples, and model adaptability have been employed to manage limited labeled images and network training processes. Overall, advanced deep learning methods have achieved high accuracy in detecting various types of targets. However, challenges in managing small, dense, and ambiguous targets remain. Continuous learning of deep networks for new targets and model adaptation are the directions of recent methods. This review covers many of the deep learning detection methods and provides insights to steer future research towards practical and powerful detectors for target recognition in high-resolution panchromatic images.
Article number: 6
Keywords: Panchromatic image, Target detection, Deep learning, Spatial features, Spatial resolution enhancement
Full-Text [PDF 889 kb]   (133 Downloads)    
Type of Study: Research | Subject: Photo&RS
Received: 2024/02/9
References
1. W. Han et al., "Methods for Small, Weak Object Detection in Optical High-Resolution Remote Sensing Images: A survey of advances and challenges," IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 4, pp. 8-34, 2021, doi: 10.1109/MGRS.2020.3041450. [DOI:10.1109/MGRS.2020.3041450]
2. S. J. Wang et al., "Target detection of remote sensing images based on deep learning method and system," in Proceedings of the 3rd International Conference on Advanced Information Science and System, 2021, pp. 1-7. [DOI:10.1145/3503047.3503116]
3. F. Hou, Y. Zhang, Y. Zhou, M. Zhang, B. Lv, and J. Wu, "Review on infrared imaging technology," Sustainability, vol. 14, no. 18, p. 11161, 2022. [DOI:10.3390/su141811161]
4. K. Xu, S. Wang, Y. Jin, Q. Che, and B. Zhou, "Object detection-oriented style transfer network for panchromatic remote sensing image," Journal of Applied Remote Sensing, vol. 17, no. 2, pp. 026503-026503, 2023. [DOI:10.1117/1.JRS.17.026503]
5. P. Lu, Y. Ding, and C. Wang, "Multi-small target detection and tracking based on improved YOLO and SIFT for drones," International Journal of Innovative Computing, Information and Control, vol. 17, no. 1, pp. 205-224, 2021.
6. T. Nie, X. Han, B. He, X. Li, H. Liu, and G. Bi, "Ship detection in panchromatic optical remote sensing images based on visual saliency and multi-dimensional feature description," Remote Sensing, vol. 12, no. 1, p. 152, 2020. [DOI:10.3390/rs12010152]
7. L. Bo, X. Xiaoyang, W. Xingxing, and T. Wenting, "Ship detection and classification from optical remote sensing images: A survey," Chinese Journal of Aeronautics, vol. 34, no. 3, pp. 145-163, 2021. [DOI:10.1016/j.cja.2020.09.022]
8. M. J. Khan, A. Yousaf, N. Javed, S. Nadeem, and K. Khurshid, "Automatic target detection in satellite images using deep learning," Journal of Space Technology, vol. 7, no. 1, pp. 44-49, 2017.
9. F. Huang, Y. Yu, and T. Feng, "Automatic extraction of impervious surfaces from high resolution remote sensing images based on deep learning," Journal of Visual Communication and Image Representation, vol. 58, pp. 453-461, 2019. [DOI:10.1016/j.jvcir.2018.11.041]
10. B. Hou, Z. Ren, W. Zhao, Q. Wu, and L. Jiao, "Object detection in high-resolution panchromatic images using deep models and spatial template matching," IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 2, pp. 956-970, 2019. [DOI:10.1109/TGRS.2019.2942103]
11. S. Xiong, Y. Tan, Y. Li, C. Wen, and P. Yan, "Subtask attention based object detection in remote sensing images," Remote Sensing, vol. 13, no. 10, p. 1925, 2021. [DOI:10.3390/rs13101925]
12. Z. Zakria, J. Deng, R. Kumar, M. S. Khokhar, J. Cai, and J. Kumar, "Multiscale and direction target detecting in remote sensing images via modified YOLO-v4," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 1039-1048, 2022. [DOI:10.1109/JSTARS.2022.3140776]
13. J. Chen, J. Sun, Y. Li, and C. Hou, "Object detection in remote sensing images based on deep transfer learning," Multimedia Tools and Applications, pp. 1-17, 2022.
14. D. Yan et al., "Improved method to detect the tailings ponds from multispectral remote sensing images based on faster R-CNN and transfer learning," Remote Sensing, vol. 14, no. 1, p. 103, 2021. [DOI:10.3390/rs14010103]
15. Y. Zhang, K. Fu, H. Sun, X. Sun, X. Zheng, and H. Wang, "A multi-model ensemble method based on convolutional neural networks for aircraft detection in large remote sensing images," Remote Sensing Letters, vol. 9, no. 1, pp. 11-20, 2018. [DOI:10.1080/2150704X.2017.1378452]
16. X. Ji, L. Tang, T. Lu, and C. Cai, "DBENet: Dual-Branch Ensemble Network for Sea-Land Segmentation of Remote Sensing Images," IEEE Transactions on Instrumentation and Measurement, 2023. [DOI:10.1109/TIM.2023.3302376]
17. Y. Chen, L. Tang, X. Yang, M. Bilal, and Q. Li, "Object-based multi-modal convolution neural networks for building extraction using panchromatic and multispectral imagery," Neurocomputing, vol. 386, pp. 136-146, 2020. [DOI:10.1016/j.neucom.2019.12.098]
18. V. Tarverdiyev, I. Erer, N. H. Kaplan, and N. Musaoğlu, "Target Detection in Multispectral Images via Detail Enhanced Pansharpening," in IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, 2022: IEEE, pp. 1544-1547. [DOI:10.1109/IGARSS46834.2022.9884355]
19. E. R. Dougherty, Digital image processing methods. CRC Press, 2020. [DOI:10.1201/9781003067054]
20. Q. Wang et al., "Simultaneous extracting area and quantity of agricultural greenhouses in large scale with deep learning method and high-resolution remote sensing images," Science of The Total Environment, vol. 872, p. 162229, 2023. [DOI:10.1016/j.scitotenv.2023.162229]
21. M. E. Aghili, M. Imani, and H. Ghassemian, "Clustering based background learning for hyperspectral anomaly detection," The Egyptian Journal of Remote Sensing and Space Sciences, vol. 26, no. 3, pp. 477-489, 2023/12/01/ 2023, doi: https://doi.org/10.1016/j.ejrs.2023.06.001 [DOI:10.1016/j.ejrs.2023.06.001.]
22. Q. Liu, Y. Tian, L. Zhang, and B. Chen, "Urban Surface Water Mapping from VHR Images Based on Superpixel Segmentation and Target Detection," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 5339-5356, 2022. [DOI:10.1109/JSTARS.2022.3181720]
23. Q. Yuan, Y. Wei, X. Meng, H. Shen, and L. Zhang, "A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 3, pp. 978-989, 2018. [DOI:10.1109/JSTARS.2018.2794888]
24. S. Song et al., "Intelligent object recognition of urban water bodies based on deep learning for multi-source and multi-temporal high spatial resolution remote sensing imagery," Sensors, vol. 20, no. 2, p. 397, 2020. [DOI:10.3390/s20020397]
25. Y. Long, Y. Gong, Z. Xiao, and Q. Liu, "Accurate object localization in remote sensing images based on convolutional neural networks," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 5, pp. 2486-2498, 2017. [DOI:10.1109/TGRS.2016.2645610]
26. S. Ghaffarian, J. Valente, M. Van Der Voort, and B. Tekinerdogan, "Effect of attention mechanism in deep learning-based remote sensing image processing: A systematic literature review," Remote Sensing, vol. 13, no. 15, p. 2965, 2021. [DOI:10.3390/rs13152965]
27. J. Astola and P. Kuosmanen, Fundamentals of nonlinear digital filtering. CRC press, 2020. [DOI:10.1201/9781003067832]
28. Y. Rao, W. Zhao, Z. Zhu, J. Lu, and J. Zhou, "Global filter networks for image classification," Advances in neural information processing systems, vol. 34, pp. 980-993, 2021.
29. Y. Shi, H. Cui, Y. Yin, H. Song, Y. Li, and P. Gamba, "Transfer Learning With Nonlinear Spectral Synthesis for Hyperspectral Target Detection," IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-17, 2023. [DOI:10.1109/TGRS.2023.3336688]
30. S. K. Köse, S. Ergünay, B. Ott, P. Wellig, and Y. Leblebici, "Target detection with deep learning in polarimetric imaging," in Target and Background Signatures IV, 2018, vol. 10794: SPIE, pp. 212-220. [DOI:10.1117/12.2325358]
31. P. Ghamisi et al., "New frontiers in spectral-spatial hyperspectral image classification: The latest advances based on mathematical morphology, Markov random fields, segmentation, sparse representation, and deep learning," IEEE geoscience and remote sensing magazine, vol. 6, no. 3, pp. 10-43, 2018. [DOI:10.1109/MGRS.2018.2854840]
32. Y. Liu, M. Chang, and J. Xu, "High-resolution remote sensing image information extraction and target recognition based on multiple information fusion," IEEE access, vol. 8, pp. 121486-121500, 2020. [DOI:10.1109/ACCESS.2020.3006288]
33. K. Yu et al., "A Parallel Algorithm for Hyperspectral Target Detection Based on Weighted Alternating Direction Method of Multiplier," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023. [DOI:10.1109/JSTARS.2023.3312523]
34. Y. Zhang and G. Hong, "A wavelet integrated image fusion approach for target detection in very high resolution satellite imagery," in Signal Processing, Sensor Fusion, and Target Recognition XIV, 2005, vol. 5809: SPIE, pp. 330-340. [DOI:10.1117/12.606410]
35. Y. Gu, Y. Wang, and Y. Li, "A survey on deep learning-driven remote sensing image scene understanding: Scene classification, scene retrieval and scene-guided object detection," Applied Sciences, vol. 9, no. 10, p. 2110, 2019. [DOI:10.3390/app9102110]
36. A. Kaul and M. Kumari, "A literature review on remote sensing scene categorization based on convolutional neural networks," International Journal of Remote Sensing, vol. 44, no. 8, pp. 2611-2642, 2023. [DOI:10.1080/01431161.2023.2204200]
37. Z. Dong, M. Wang, Y. Wang, Y. Zhu, and Z. Zhang, "Object detection in high resolution remote sensing imagery based on convolutional neural networks with suitable object scale features," IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 3, pp. 2104-2114, 2019. [DOI:10.1109/TGRS.2019.2953119]
38. نیما فرهادی، عباس کیانی، و حمید عبادی، "توسعه مدلی مبتنی بر تشدید گرادیان در شبکه های کانولوشنی عمیق به ‌منظور شناسایی اهداف در تصاویر سنجش ازدوری"، نشریه علمی علوم و فنون نقشه برداری، دوره 11، شماره 1، صفحات 35-50، 1400.
39. R. P. Loibl, "Target Detection using Convolutional Neural Networks," 2018.
40. M. Dahmane, S. Foucher, M. Beaulieu, F. Riendeau, Y. Bouroubi, and M. Benoit, "Object detection in pleiades images using deep features," in 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016: IEEE, pp. 1552-1555. [DOI:10.1109/IGARSS.2016.7729396]
41. X. Zhang, Y. n. Zhou, and J. Luo, "Deep learning for processing and analysis of remote sensing big data: A technical review," Big Earth Data, vol. 6, no. 4, pp. 527-560, 2022. [DOI:10.1080/20964471.2021.1964879]
42. Y. Jia et al., "Caffe: Convolutional architecture for fast feature embedding," in Proceedings of the 22nd ACM international conference on Multimedia, 2014, pp. 675-678. [DOI:10.1145/2647868.2654889]
43. J. Deng, W. Dong, R. Socher, L. J. Li, L. Kai, and F.-F. Li, "ImageNet: A large-scale hierarchical image database," in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 20-25 June 2009 2009, pp. 248-255, doi: 10.1109/CVPR.2009.5206848. [DOI:10.1109/CVPR.2009.5206848]
44. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge: Cambridge University Press, 2000. [DOI:10.1017/CBO9780511801389]
45. C. L. Zitnick and P. Dollár, "Edge boxes: Locating object proposals from edges," in Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, 2014: Springer, pp. 391-405. [DOI:10.1007/978-3-319-10602-1_26]
46. W. Yuan, W. Ran, X. Shi, and R. Shibasaki, "Multi-Constraint Transformer based Automatic Building Extraction from High Resolution Remote Sensing Images," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023. [DOI:10.1109/JSTARS.2023.3319826]
47. B. Kalantar, S. B. Mansor, A. A. Halin, H. Z. M. Shafri, and M. Zand, "Multiple moving object detection from UAV videos using trajectories of matched regional adjacency graphs," IEEE Transactions on geoscience and remote sensing, vol. 55, no. 9, pp. 5198-5213, 2017. [DOI:10.1109/TGRS.2017.2703621]
48. Y. Tan, S. Xiong, and Y. Li, "Automatic extraction of built-up areas from panchromatic and multispectral remote sensing images using double-stream deep convolutional neural networks," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 11, pp. 3988-4004, 2018. [DOI:10.1109/JSTARS.2018.2871046]
49. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818-2826. [DOI:10.1109/CVPR.2016.308]
50. J. Wei, Y. Liu, L. Li, W. Xie, S. Zhao, and Z. Zhao, "Improved YOLO X with Bilateral Attention for Small Object Detection," in 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), 2023: IEEE, pp. 1-6. [DOI:10.1109/ICAISC58445.2023.10200089]
51. X. Ji, L. Huang, B.-H. Tang, G. Chen, and F. Cheng, "A Superpixel Spatial Intuitionistic Fuzzy C-Means Clustering Algorithm for Unsupervised Classification of High Spatial Resolution Remote Sensing Images," Remote Sensing, vol. 14, no. 14, p. 3490, 2022. [DOI:10.3390/rs14143490]
52. L. Shuxin, Z. Zhilong, and L. Biao, "A plane target detection algorithm in remote sensing images based on deep learning network technology," in Journal of Physics: Conference Series, 2018, vol. 960, no. 1: IOP Publishing, p. 012025. [DOI:10.1088/1742-6596/960/1/012025]
53. G. Prathap and I. Afanasyev, "Deep learning approach for building detection in satellite multispectral imagery," in 2018 International Conference on Intelligent Systems (IS), 2018: IEEE, pp. 461-465. [DOI:10.1109/IS.2018.8710471]
54. O. Ronneberger, P. Fischer, and T. Brox, "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, 2015: Springer, pp. 234-241. [DOI:10.1007/978-3-319-24574-4_28]
55. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, "SLIC Superpixels Compared to State-of-the-Art Superpixel Methods," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274-2282, 2012, doi: 10.1109/TPAMI.2012.120. [DOI:10.1109/TPAMI.2012.120]
56. S. Crommelinck, R. Bennett, M. Gerke, M. Koeva, M. Yang, and G. Vosselman, "SLIC superpixels for object delineation from UAV data," ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 4, pp. 9-16, 2017. [DOI:10.5194/isprs-annals-IV-2-W3-9-2017]
57. L. Zhang et al., "Structure-feature based graph self-adaptive pooling," in Proceedings of The Web Conference 2020, 2020, pp. 3098-3104. [DOI:10.1145/3366423.3380083]
58. Y. Zhong, Z. Zheng, A. Ma, X. Lu, and L. Zhang, "COLOR: cycling, offline learning, and online representation framework for airport and airplane detection using GF-2 satellite images," IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 12, pp. 8438-8449, 2020. [DOI:10.1109/TGRS.2020.2987907]
59. نیما فرهادی، عباس کیانی، و حمید عبادی، "شناسایی اهداف در تصاویر سنجش ازدوری با قدرت تفکیک بالا با استفاده از روش های یادگیری عمیق"، سنجش از دور و GIS ایران، دوره 11، شماره 1، صفحات 33-48، 1398.
60. Q. Zheng, L. Zheng, Y. Bai, H. Liu, J. Deng, and Y. Li, "Boundary-aware network with two-stage partial decoders for salient object detection in remote sensing images," IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-13, 2023. [DOI:10.1109/TGRS.2023.3260825]
61. Q. Lin, L. Xia, S. Li, and W. Chen, "Two‐stage local attention network for salient object detection in remote sensing images," IET Image Processing, vol. 17, no. 3, pp. 849-861, 2023. [DOI:10.1049/ipr2.12677]
62. P. Bharati and A. Pramanik, "Deep learning techniques-R-CNN to mask R-CNN: a survey," Computational Intelligence in Pattern Recognition: Proceedings of CIPR 2019, pp. 657-668, 2020. [DOI:10.1007/978-981-13-9042-5_56]
63. Z. Zou and Z. Shi, "Ship Detection in Spaceborne Optical Image With SVD Networks," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 10, pp. 5832-5845, 2016, doi: 10.1109/TGRS.2016.2572736. [DOI:10.1109/TGRS.2016.2572736]
64. K. Kanatani, "Singular Value Decomposition," in Computer Vision: A Reference Guide: Springer, 2021, pp. 1174-1177. [DOI:10.1007/978-3-030-63416-2_802]
65. Q. Li, L. Mou, Q. Liu, Y. Wang, and X. X. Zhu, "HSF-Net: Multiscale Deep Feature Embedding for Ship Detection in Optical Remote Sensing Imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 12, pp. 7147-7161, 2018, doi: 10.1109/TGRS.2018.2848901. [DOI:10.1109/TGRS.2018.2848901]
66. S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," Advances in neural information processing systems, vol. 28, 2015.
67. N. Wang, B. Li, Q. Xu, and Y. Wang, "Automatic ship detection in optical remote sensing images based on anomaly detection and SPP-PCANet," Remote Sensing, vol. 11, no. 1, p. 47, 2018. [DOI:10.3390/rs11010047]
68. Z. Lu, T. Xu, K. Liu, Z. Liu, F. Zhou, and Q. Liu, "5M-Building: A large-scale high-resolution building dataset with CNN based detection analysis," in 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019: IEEE, pp. 1385-1389. [DOI:10.1109/ICTAI.2019.00194]
69. K. He, G. Gkioxari, P. Dollár, and R. Girshick, "Mask r-cnn," in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2961-2969. [DOI:10.1109/ICCV.2017.322]
70. J. Redmon and A. Farhadi, "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.
71. G. Othman and D. Q. Zeebaree, "The applications of discrete wavelet transform in image processing: A review," Journal of Soft Computing and Data Mining, vol. 1, no. 2, pp. 31-43, 2020.
72. H. Wang and F. Miao, "Building extraction from remote sensing images using deep residual U-Net," European Journal of Remote Sensing, vol. 55, no. 1, pp. 71-85, 2022. [DOI:10.1080/22797254.2021.2018944]
73. L. Chen, W. Shi, C. Fan, L. Zou, and D. Deng, "A novel coarse-to-fine method of ship detection in optical remote sensing images based on a deep residual dense network," Remote Sensing, vol. 12, no. 19, p. 3115, 2020. [DOI:10.3390/rs12193115]
74. F. Chen, S. Lou, and Y. Song, "Improving Object Detection of Remotely Sensed Multispectral Imagery Via Pan-sharpening," in Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition, 2020, pp. 136-140. [DOI:10.1145/3436369.3437446]
75. J. Choi, K. Yu, and Y. Kim, "A new adaptive component-substitution-based satellite image fusion by using partial replacement," IEEE transactions on geoscience and remote sensing, vol. 49, no. 1, pp. 295-309, 2010. [DOI:10.1109/TGRS.2010.2051674]
76. A. Sekrecka, M. Kedzierski, and D. Wierzbicki, "Pre-processing of panchromatic images to improve object detection in pansharpened images," Sensors, vol. 19, no. 23, p. 5146, 2019. [DOI:10.3390/s19235146]
77. J. Liu, "Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details," International Journal of Remote Sensing, vol. 21, no. 18, pp. 3461-3472, 2000. [DOI:10.1080/014311600750037499]
78. P. Chavez, S. C. Sides, and J. A. Anderson, "Comparison of three different methods to merge multiresolution and multispectral data- Landsat TM and SPOT panchromatic," Photogrammetric Engineering and remote sensing, vol. 57, no. 3, pp. 295-303, 1991.
79. A. Garzelli, L. Capobianco, and F. Nencini, "On the effects of pan-sharpening to target detection," in 2009 IEEE international geoscience and remote sensing symposium, 2009, vol. 2: IEEE, pp. II-136-II-139. [DOI:10.1109/IGARSS.2009.5418022]
80. T.-M. Tu, S.-C. Su, H.-C. Shyu, and P. S. Huang, "A new look at IHS-like image fusion methods," Information fusion, vol. 2, no. 3, pp. 177-186, 2001. [DOI:10.1016/S1566-2535(01)00036-7]
81. X. Otazu, M. González-Audícana, O. Fors, and J. Núñez, "Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 10, pp. 2376-2385, 2005. [DOI:10.1109/TGRS.2005.856106]
82. A. Garzelli, F. Nencini, and L. Capobianco, "Optimal MMSE Pan Sharpening of Very High Resolution Multispectral Images," IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 1, pp. 228-236, 2008, doi: 10.1109/TGRS.2007.907604. [DOI:10.1109/TGRS.2007.907604]
83. Q. Tan, J. Ling, J. Hu, X. Qin, and J. Hu, "Vehicle Detection in High Resolution Satellite Remote Sensing Images Based on Deep Learning," IEEE Access, vol. 8, pp. 153394-153402, 2020, doi: 10.1109/ACCESS.2020.3017894. [DOI:10.1109/ACCESS.2020.3017894]
84. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, vol. 25, 2012.
85. X. Nie, M. Duan, H. Ding, B. Hu, and E. K. Wong, "Attention mask R-CNN for ship detection and segmentation from remote sensing images," Ieee Access, vol. 8, pp. 9325-9334, 2020. [DOI:10.1109/ACCESS.2020.2964540]
86. T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature pyramid networks for object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2117-2125. [DOI:10.1109/CVPR.2017.106]
87. Z. Li, E. Li, T. Xu, A. Samat, and W. Liu, "Feature alignment fpn for oriented object detection in remote sensing images," IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023. [DOI:10.1109/LGRS.2023.3234267]
88. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778. [DOI:10.1109/CVPR.2016.90]
89. Y. Ma, S. Chen, S. Ermon, and D. B. Lobell, "Transfer learning in environmental remote sensing," Remote Sensing of Environment, vol. 301, p. 113924, 2024. [DOI:10.1016/j.rse.2023.113924]
90. F. Huang, L. Xu, M. Li, and M. Tang, "High-resolution remotely sensed small target detection by imitating fly visual perception mechanism," Computational and mathematical methods in medicine, vol. 2012, 2012. [DOI:10.1155/2012/789429]
91. Q. Cheng, S. Zhang, S. Bo, D. Chen, and H. Zhang, "Augmented reality dynamic image recognition technology based on deep learning algorithm," IEEE Access, vol. 8, pp. 137370-137384, 2020. [DOI:10.1109/ACCESS.2020.3012130]
92. L. Zhang, K. Yang, and H. Li, "Regions of interest detection in panchromatic remote sensing images based on multiscale feature fusion," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 12, pp. 4704-4716, 2014. [DOI:10.1109/JSTARS.2014.2319736]
93. S. Karim, Y. Zhang, S. Yin, I. Bibi, and A. A. Brohi, "A brief review and challenges of object detection in optical remote sensing imagery," Multiagent and Grid Systems, vol. 16, no. 3, pp. 227-243, 2020. [DOI:10.3233/MGS-200330]
94. L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," IEEE Transactions on pattern analysis and machine intelligence, vol. 20, no. 11, pp. 1254-1259, 1998. [DOI:10.1109/34.730558]
95. J. Harel, C. Koch, and P. Perona, "Graph-based visual saliency," Advances in neural information processing systems, vol. 19, 2006. [DOI:10.7551/mitpress/7503.003.0073]
96. X. Hou and L. Zhang, "Saliency detection: A spectral residual approach," in 2007 IEEE Conference on computer vision and pattern recognition, 2007: Ieee, pp. 1-8. [DOI:10.1109/CVPR.2007.383267]
97. S. Gui, S. Song, R. Qin, and Y. Tang, "Remote Sensing Object Detection in the Deep Learning Era-A Review," Remote Sensing, vol. 16, no. 2, p. 327, 2024. [DOI:10.3390/rs16020327]
98. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
99. Z. Huang et al., "Building Detection From Panchromatic and Multispectral Images With Dual-Stream Asymmetric Fusion Networks," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 3364-3377, 2023. [DOI:10.1109/JSTARS.2023.3261866]
100. Z. Zhao et al., "Lightweight Target Detection in High Resolution Remote Sensing Images," in International Conference on Autonomous Unmanned Systems, 2022: Springer, pp. 3252-3260. [DOI:10.1007/978-981-99-0479-2_299]
101. C. Corbane, F. Marre, and M. Petit, "Using SPOT-5 HRG Data in Panchromatic Mode for Operational Detection of Small Ships in Tropical Area," Sensors, vol. 8, no. 5, pp. 2959-2973, 2008. [DOI:10.3390/s8052959]
102. J. Tang, C. Deng, G.-B. Huang, and B. Zhao, "Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine," IEEE transactions on geoscience and remote sensing, vol. 53, no. 3, pp. 1174-1185, 2014. [DOI:10.1109/TGRS.2014.2335751]
103. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006. [DOI:10.1016/j.neucom.2005.12.126]
104. N. Wang, B. Li, X. Wei, Y. Wang, and H. Yan, "Ship detection in spaceborne infrared image based on lightweight CNN and multisource feature cascade decision," IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 5, pp. 4324-4339, 2020. [DOI:10.1109/TGRS.2020.3008993]
105. I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.
106. C. Shorten and T. M. Khoshgoftaar, "A survey on image data augmentation for deep learning," Journal of big data, vol. 6, no. 1, pp. 1-48, 2019. [DOI:10.1186/s40537-019-0197-0]
107. U. Muhammad, W. Wang, S. P. Chattha, and S. Ali, "Pre-trained VGGNet architecture for remote-sensing image scene classification," in 2018 24th International Conference on Pattern Recognition (ICPR), 2018: IEEE, pp. 1622-1627. [DOI:10.1109/ICPR.2018.8545591]
108. C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, "A survey on deep transfer learning," in Artificial Neural Networks and Machine Learning-ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, 2018: Springer, pp. 270-279. [DOI:10.1007/978-3-030-01424-7_27]
109. L. Shen, Y. Sun, Z. Yu, L. Ding, X. Tian, and D. Tao, "On Efficient Training of Large-Scale Deep Learning Models: A Literature Review," arXiv preprint arXiv:2304.03589, 2023.
110. W. Yu, J. Li, Z. Wang, and Z. Yu, "Boosting SAR Aircraft Detection Performance with Multi-Stage Domain Adaptation Training," Remote Sensing, vol. 15, no. 18, p. 4614, 2023. [DOI:10.3390/rs15184614]
111. Y. Li, J. Xue, and K. Lu, "Semi-supervised Object Detection for Remote Sensing Images via Pseudo Labeling and Consistency Learning: RS-PCL for SSOD in Remote Sensing Images," in Proceedings of the 15th International Conference on Digital Image Processing, 2023, pp. 1-7. [DOI:10.1145/3604078.3604114]
112. D. Wittich, "Deep domain adaptation by weighted entropy minimization for the classification of aerial images," ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences; 5, 2, vol. 5, no. 2, pp. 591-598, 2020. [DOI:10.5194/isprs-annals-V-2-2020-591-2020]
113. S. Qiu, G. Wen, Z. Deng, J. Liu, and Y. Fan, "Accurate non-maximum suppression for object detection in high-resolution remote sensing images," Remote Sensing Letters, vol. 9, no. 3, pp. 237-246, 2018. [DOI:10.1080/2150704X.2017.1415473]
114. H. Choi, H.-J. Lee, H.-J. You, S.-Y. Rhee, and W.-S. Jeon, "Comparative analysis of generalized intersection over :union:," Sensors and Materials, vol. 31, no. 11, pp. 3849-3858, 2019. [DOI:10.18494/SAM.2019.2584]
115. G.-S. Xia et al., "DOTA: A large-scale dataset for object detection in aerial images," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 3974-3983. [DOI:10.1109/CVPR.2018.00418]
116. B. Cheng, Z. Li, B. Xu, X. Yao, Z. Ding, and T. Qin, "Structured object-level relational reasoning CNN-based target detection algorithm in a remote sensing image," Remote Sensing, vol. 13, no. 2, p. 281, 2021. [DOI:10.3390/rs13020281]
117. A. Van Etten, D. Lindenbaum, and T. M. Bacastow, "Spacenet: A remote sensing dataset and challenge series," arXiv preprint arXiv:1807.01232, 2018.
118. M.-R. Hsieh, Y.-L. Lin, and W. H. Hsu, "Drone-based object counting by spatially regularized regional proposal network," in Proceedings of the IEEE international conference on computer vision, 2017, pp. 4145-4153. [DOI:10.1109/ICCV.2017.446]
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Aghili M E, Aghabalaei A. Target Detection in Panchromatic Remotely Sensed Images Using Deep Learning Algorithms: A Review. JGST 2024; 14 (1) : 6
URL: http://jgst.issgeac.ir/article-1-1179-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 14, Issue 1 (9-2024) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology