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題名 基於深度學習之衛星圖像變遷偵測優化
Optimization of Deep Learning-based Change Detection in Satellite Images作者 陳湘淇
Chen, Hsiang-Chi貢獻者 廖文宏
Liao, Wen-Hung
陳湘淇
Chen, Hsiang-Chi關鍵詞 深度學習
卷積神經網路
轉換器
衛星影像
地景變遷偵測
Deep learning
Convolutional neural networks
Transformer
Satellite images
Change detection日期 2022 上傳時間 5-十月-2022 09:14:53 (UTC+8) 摘要 地景變遷偵測為遙測影像分析的基本應用之一，該任務須自給定之兩張同一地區、不同時間點之衛星影像，偵測出變遷部位，廣泛被運用於環境監控、災害評估、土地資源規劃等範疇。深度學習引入地景變遷偵測任務，能夠輔助資料標註人員加速工作流程；近幾年，除了在電腦視覺領域發展越趨成熟的卷積神經網路，基於轉換器的視覺任務架構大放異彩，本研究分別選用基於卷積網路、純轉換器、混合結構作為編碼的SNUNet、ChangeFormer與BIT地景變遷偵測模型進行探討，針對不同條件評估模型影響，並以此優化偵測表現。為維持模型面對不同變遷性質，或來自不同資料集之樣本的適應能力，本研究從訓練資料方面調整，增加一倍輸入時序交換的資料量或合併資料集進行訓練；另外我們也從目標函數端修改提出雙向損失，在不更動資料集之情況下，讓模型同時學習到「出現、消失」類型之變遷。上述訓練方式皆能有效提升模型泛化能力，在LEVIR-CD測試集上，IoU-1自不及0.1上升至超越0.7，達到接近基準之表現（0.7631）；在S2Looking測試集上超越基準（0.4184），從小於0.1的IoU-1提升到0.4422。
Change detection (CD), one of the fundamental applications in remote sensing (RS) image analysis, aims to identify surface changes based on bitemporal images of the same area. It is widely used in environmental monitoring, disaster assessment and land resource planning. Introducing deep learning approaches for change detection could help geographic data annotation workers improve workflow efficiency. In addition to convolutional neural network (CNN), the deep learning framework that has achieved remarkable performance on a variety of computer vision applications in recent years is transformer. To compare and improve the performance of change detection, this research investigates modern change detection models, namely, SNUNet, ChangeFormer and BIT, which are CNN-based, pure transformer-based and CNN-transformer hybrid encoding model, respectively.In this work, we attempt to maintain the adaptability of the CD model when processing input image pairs which have different changed types or are from another datasets. In terms of training data, we can either double the number of training pairs d by adding the same bitemporal images in reverse order or merge CD datasets to build a larger training data. In terms of objective function, we propose a bidirectional loss, which considers not only newly built but also demolished areas without the need for data augmentation. Experimental results show that the above approaches attain significant accuracy improvements (over 0.7 from less than 0.1 of the IoU-1 on the LEVIR-CD test sets; from below 0.1 of the IoU-1 increased to 0.4422 on the S2Looking test sets) and greatly enhance the model’s generalization capability.參考文獻 [1] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.[2] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.[3] Hubel, D. H., & Wiesel, T. N. (1959). Receptive fields of single neurons in the cat`s striate cortex. The Journal of physiology, 148(3), 574-591.[4] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.[5] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.[6] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).[7] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).[8] Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).[9] He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).[10] Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440).[11] Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12), 2481-2495.[12] Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062.[13] Yu, F., & Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122.[14] Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.[15] Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.[16] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.[17] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., & Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (pp. 1877-1901). Curran Associates, Inc..[18] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.[19] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533-536.[20] Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P., & Zhang, L. (2021). Rethinking Semantic Segmentation From a Sequence-to-Sequence Perspective With Transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6881-6890).[21] Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3213-3223).[22] Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., & Torralba, A. (2019). Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127(3), 302-321.[23] Mottaghi, R., Chen, X., Liu, X., Cho, N.G., Lee, S.W., Fidler, S., Urtasun, R., & Yuille, A. (2014). The role of context for object detection and semantic segmentation in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 891-898).[24] Valanarasu, J. M. J., Oza, P., Hacihaliloglu, I., & Patel, V. M. (2021, September). Medical transformer: Gated axial-attention for medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 36-46). Springer, Cham.[25] Xiao, X., Lian, S., Luo, Z., & Li, S. (2018, October). Weighted res-unet for high-quality retina vessel segmentation. In 2018 9th international conference on information technology in medicine and education (ITME) (pp. 327-331). IEEE.[26] Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support (pp. 3-11). Springer, Cham.[27] Fang, S., Li, K., Shao, J., & Li, Z. (2021). SNUNet-CD: A densely connected Siamese network for change detection of VHR images. IEEE Geoscience and Remote Sensing Letters, 19, 1-5.[28] Woo, S., Park, J., Lee, J. Y., & Kweon, I. S. (2018). CBAM: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV) (pp. 3-19).[29] Lebedev, M. A., Vizilter, Y. V., Vygolov, O. V., Knyaz, V. A., & Rubis, A. Y. (2018). Change detection in remote sensing images using conditional adversarial networks. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42(2).[30] Chen, H., Qi, Z., & Shi, Z. (2021). Remote sensing image change detection with transformers. IEEE Transactions on Geoscience and Remote Sensing.[31] Chen, H., & Shi, Z. (2020). A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sensing, 12(10), 1662.[32] Ji, S., Wei, S., & Lu, M. (2018). Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Transactions on Geoscience and Remote Sensing, 57(1), 574-586.[33] Zhang, C., Yue, P., Tapete, D., Jiang, L., Shangguan, B., Huang, L., & Liu, G. (2020). A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 183-200.[34] Bandara, W. G. C., & Patel, V. M. (2022). A Transformer-Based Siamese Network for Change Detection. arXiv preprint arXiv:2201.01293.[35] Shen, L., Lu, Y., Chen, H., Wei, H., Xie, D., Yue, J., Chen, R., Lv, S., & Jiang, B. (2021). S2Looking: A satellite side-looking dataset for building change detection. Remote Sensing, 13(24), 5094..[36] Dong, C., Loy, C. C., He, K., & Tang, X. (2015). Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 38(2), 295-307.[37] Khan, B., Fraz, M. M., & Mumtaz, A. (2021, December). Enhanced Super-Resolution via Squeeze-and-Residual-Excitation in Aerial Imagery. In 2021 International Conference on Frontiers of Information Technology (FIT) (pp. 19-24). IEEE.[38] imgaug.augmenters.weather.CloudLayer. Imgaug documentation. https://imgaug.readthedocs.io/en/latest/source/api_augmenters_weather.html#imgaug.augmenters.weather.CloudLayer 描述 碩士
國立政治大學
資訊科學系
109753114資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109753114 資料類型 thesis dc.contributor.advisor 廖文宏 zh_TW dc.contributor.advisor Liao, Wen-Hung en_US dc.contributor.author (作者) 陳湘淇 zh_TW dc.contributor.author (作者) Chen, Hsiang-Chi en_US dc.creator (作者) 陳湘淇 zh_TW dc.creator (作者) Chen, Hsiang-Chi en_US dc.date (日期) 2022 en_US dc.date.accessioned 5-十月-2022 09:14:53 (UTC+8) - dc.date.available 5-十月-2022 09:14:53 (UTC+8) - dc.date.issued (上傳時間) 5-十月-2022 09:14:53 (UTC+8) - dc.identifier (其他 識別碼) G0109753114 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/142123 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 109753114 zh_TW dc.description.abstract (摘要) 地景變遷偵測為遙測影像分析的基本應用之一，該任務須自給定之兩張同一地區、不同時間點之衛星影像，偵測出變遷部位，廣泛被運用於環境監控、災害評估、土地資源規劃等範疇。深度學習引入地景變遷偵測任務，能夠輔助資料標註人員加速工作流程；近幾年，除了在電腦視覺領域發展越趨成熟的卷積神經網路，基於轉換器的視覺任務架構大放異彩，本研究分別選用基於卷積網路、純轉換器、混合結構作為編碼的SNUNet、ChangeFormer與BIT地景變遷偵測模型進行探討，針對不同條件評估模型影響，並以此優化偵測表現。為維持模型面對不同變遷性質，或來自不同資料集之樣本的適應能力，本研究從訓練資料方面調整，增加一倍輸入時序交換的資料量或合併資料集進行訓練；另外我們也從目標函數端修改提出雙向損失，在不更動資料集之情況下，讓模型同時學習到「出現、消失」類型之變遷。上述訓練方式皆能有效提升模型泛化能力，在LEVIR-CD測試集上，IoU-1自不及0.1上升至超越0.7，達到接近基準之表現（0.7631）；在S2Looking測試集上超越基準（0.4184），從小於0.1的IoU-1提升到0.4422。 zh_TW dc.description.abstract (摘要) Change detection (CD), one of the fundamental applications in remote sensing (RS) image analysis, aims to identify surface changes based on bitemporal images of the same area. It is widely used in environmental monitoring, disaster assessment and land resource planning. Introducing deep learning approaches for change detection could help geographic data annotation workers improve workflow efficiency. In addition to convolutional neural network (CNN), the deep learning framework that has achieved remarkable performance on a variety of computer vision applications in recent years is transformer. To compare and improve the performance of change detection, this research investigates modern change detection models, namely, SNUNet, ChangeFormer and BIT, which are CNN-based, pure transformer-based and CNN-transformer hybrid encoding model, respectively.In this work, we attempt to maintain the adaptability of the CD model when processing input image pairs which have different changed types or are from another datasets. In terms of training data, we can either double the number of training pairs d by adding the same bitemporal images in reverse order or merge CD datasets to build a larger training data. In terms of objective function, we propose a bidirectional loss, which considers not only newly built but also demolished areas without the need for data augmentation. Experimental results show that the above approaches attain significant accuracy improvements (over 0.7 from less than 0.1 of the IoU-1 on the LEVIR-CD test sets; from below 0.1 of the IoU-1 increased to 0.4422 on the S2Looking test sets) and greatly enhance the model’s generalization capability. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究背景與動機 11.2 研究目的 21.3 論文架構 2第二章 技術背景與相關研究 32.1 深度學習框架應用於影像分割 42.1.1 基於卷積神經網路之影像分割模型 52.1.2 基於轉換器之影像分割模型 72.2 地景變遷偵測模型 112.3 評估指標 142.4 小結 17第三章 研究方法與實驗結果 183.1 地景變遷資料集介紹與實驗設計 183.2 一般地景變遷模型 203.2.1 地景變遷模型基準 203.2.2 資料端影響評估 223.2.3 目標函數端影響評估 493.2.4 其他影響評估 533.2.5 綜合條件評估 583.3 雲遮蔽地景變遷模型 62第四章 結論與未來研究方向 69參考文獻 71 zh_TW dc.format.extent 4059689 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109753114 en_US dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) 卷積神經網路 zh_TW dc.subject (關鍵詞) 轉換器 zh_TW dc.subject (關鍵詞) 衛星影像 zh_TW dc.subject (關鍵詞) 地景變遷偵測 zh_TW dc.subject (關鍵詞) Deep learning en_US dc.subject (關鍵詞) Convolutional neural networks en_US dc.subject (關鍵詞) Transformer en_US dc.subject (關鍵詞) Satellite images en_US dc.subject (關鍵詞) Change detection en_US dc.title (題名) 基於深度學習之衛星圖像變遷偵測優化 zh_TW dc.title (題名) Optimization of Deep Learning-based Change Detection in Satellite Images en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.[2] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.[3] Hubel, D. H., & Wiesel, T. N. (1959). Receptive fields of single neurons in the cat`s striate cortex. The Journal of physiology, 148(3), 574-591.[4] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.[5] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.[6] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).[7] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).[8] Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).[9] He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).[10] Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440).[11] Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12), 2481-2495.[12] Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062.[13] Yu, F., & Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122.[14] Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.[15] Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.[16] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.[17] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., & Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (pp. 1877-1901). Curran Associates, Inc..[18] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.[19] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533-536.[20] Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P., & Zhang, L. (2021). Rethinking Semantic Segmentation From a Sequence-to-Sequence Perspective With Transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6881-6890).[21] Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3213-3223).[22] Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., & Torralba, A. (2019). Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127(3), 302-321.[23] Mottaghi, R., Chen, X., Liu, X., Cho, N.G., Lee, S.W., Fidler, S., Urtasun, R., & Yuille, A. 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