{"type":"ScholarlyArticle","title":"Fusion from a Distributional Perspective: A Unified Symbiotic Diffusion Framework for Any Multisource Remote Sensing Data Classification","authors":[{"name":"Teng Yang","slug":"teng-yang","given":"Teng","family":"Yang","affiliation":"The State Key Laboratory of Integrated Service Network , Xidian University"},{"name":"Song Xiao","slug":"song-xiao","given":"Song","family":"Xiao","affiliation":"The State Key Laboratory of Integrated Service Network , Xidian University; Beijing Electronic Science and Technology Institute"},{"name":"Wenqian Dong","slug":"wenqian-dong","given":"Wenqian","family":"Dong","affiliation":"The State Key Laboratory of Integrated Service Network , Xidian University"},{"name":"Jiahui Qu","slug":"jiahui-qu","given":"Jiahui","family":"Qu","affiliation":"The State Key Laboratory of Integrated Service Network , Xidian University"},{"name":"Yueguang Yang","slug":"yueguang-yang","given":"Yueguang","family":"Yang","affiliation":"The State Key Laboratory of Integrated Service Network , Xidian University"}],"abstract":"The joint classification of multisource remote sensing data is a prominent research field. However, most of the existing works are tailored for two specific data sources, which fail to effectively address the diverse combinations of data sources in practical applications. The importance of designing a unified network with applicability has been disregarded. In this paper, we propose a unified and self-supervised Symbiotic Diffusion framework (named SymDiffuser), which achieves the joint classification of any pair of different remote sensing data sources in a single model. The SymDiffuser captures the inter-modal relationship through establishing reciprocal conditional distributions across diverse sources step by step. The fusion process of multisource data is consistently represented within the framework from a data distribution perspective. Subsequently, features under the current conditional distribution at each time step is integrated during the downstream phase to accomplish the classification task. Such joint classification methodology transcends source-specific considerations, rendering it applicable to remote sensing data from any diverse sources. The experimental results showcase the framework's potential in achieving state-of-the-art performance in multimodal fusion classification task.","doi":"10.24963/ijcai.2024/174","url":"https://ijcai.org/proceedings/2024/174","pdf":"https://ijcai.org/proceedings/2024/0174.pdf","markdown":"https://ijcai.org/proceedings/2024/174.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/174","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":174,"pages":{"first":1570,"last":1578},"keywords":["Machine Learning: ML: Classification","Computer Vision: CV: Recognition (object detection, categorization)"],"videos":[],"references":[{"position":1,"raw":"[Bao et al., 2023a] Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. In IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 22669-22679, 2023.","title":"All are worth words: A vit backbone for diffusion models","authors":"Bao","year":"2023","venue":"IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bao et al., 2023b] Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, and Jun Zhu. One transformer fits all distri- butions in multi-modal diffusion at scale. arXiv preprint arXiv:2303.06555, 2023.","title":"One transformer fits all distributions in multi-modal diffusion at scale","authors":"Bao","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2017] Yushi Chen, Chunyang Li, Pedram Ghamisi, Xiuping Jia, and Yanfeng Gu. Deep fusion of re- mote sensing data for accurate classification. IEEE Geosci. Remote Sens. Lett., 14(8):1253-1257, 2017.","title":"Deep fusion of remote sensing data for accurate classification","authors":"Yushi Chen, Chunyang Li, Pedram Ghamisi, Xiuping Jia, Yanfeng Gu","year":"2017","venue":"IEEE Geosci. Remote Sens. Lett","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Ding et al., 2022a] Jian Ding, Nan Xue, Gui-Song Xia, Xi- ang Bai, Wen Yang, Michael Ying Yang, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, and Liangpei Zhang. Object detection in aerial images: A large-scale benchmark and challenges. IEEE Trans. Pattern Anal. Mach. Intell., 44(11):7778-7796, 2022.","title":"Object detection in aerial images: A large-scale benchmark and challenges","authors":"Ding","year":"2022","venue":"IEEE Trans. Pattern Anal. Mach. Intell","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Ding et al., 2022b] Kexing Ding, Ting Lu, Wei Fu, Shutao Li, and Fuyan Ma. Global-local transformer network for hsi and lidar data joint classification. IEEE Trans. Geosci. Remote Sens., 60:1-13, 2022.","title":"Global-local transformer network for hsi and lidar data joint classification","authors":"Ding","year":"2022","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dong et al., 2022] Yanni Dong, Quanwei Liu, Bo Du, and Liangpei Zhang. Weighted feature fusion of convolutional neural network and graph attention network for hyperspec- tral image classification. IEEE Trans. Image Process., 31:1559-1572, 2022.","title":"Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification","authors":"Yanni Dong, Quanwei Liu, Bo Du, Liangpei Zhang","year":"2022","venue":"IEEE Trans. Image Process","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Dong et al., 2023] Wenqian Dong, Yufei Yang, Jiahui Qu, Song Xiao, and Yunsong Li. Local information-enhanced graph-transformer for hyperspectral image change detec- tion with limited training samples. IEEE Trans. Geosci. Remote Sens., 61:1-14, 2023.","title":"Local information-enhanced graph-transformer for hyperspectral image change detection with limited training samples","authors":"Dong","year":"2023","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Fang et al., 2021] Sheng Fang, Kaiyu Li, and Zhe Li. S 2 enet: Spatial-spectral cross-modal enhancement net- work for classification of hyperspectral and lidar data. IEEE Geoscience and Remote Sensing Letters, 19:1-5, 2021.","title":"S 2 enet: Spatial-spectral cross-modal enhancement network for classification of hyperspectral and lidar data","authors":"Sheng Fang, Kaiyu Li, Zhe Li","year":"2021","venue":"IEEE Geoscience and Remote Sensing Letters","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gao et al., 2023] Sicheng Gao, Xuhui Liu, Bohan Zeng, Sheng Xu, Yanjing Li, Xiaoyan Luo, Jianzhuang Liu, Xi- antong Zhen, and Baochang Zhang. Implicit diffusion models for continuous super-resolution. In IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 10021- 10030, 2023.","title":"Implicit diffusion models for continuous super-resolution","authors":"Sicheng Gao, Xuhui Liu, Bohan Zeng, Sheng Xu, Yanjing Li, Xiaoyan Luo, Jianzhuang Liu, Xiantong Zhen, Baochang Zhang","year":"2023","venue":"IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ho et al., 2020] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.","title":"Denoising diffusion probabilistic models","authors":"Jonathan Ho, Ajay Jain, Pieter Abbeel","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hong et al., 2021] Danfeng Hong, Lianru Gao, Naoto Yokoya, Jing Yao, Jocelyn Chanussot, Qian Du, and Bing Zhang. More diverse means better: Multimodal deep learning meets remote-sensing imagery classifica- tion. IEEE Trans. Geosci. Remote Sens., 59(5):4340- 4354, 2021.","title":"More diverse means better: Multimodal deep learning meets remote-sensing imagery classification","authors":"Danfeng Hong, Lianru Gao, Naoto Yokoya, Jing Yao, Jocelyn Chanussot, Qian Du, Bing Zhang","year":"2021","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hu et al., 2023] Meiqi Hu, Chen Wu, Bo Du, and Liang- pei Zhang. Binary change guided hyperspectral multiclass change detection. IEEE Trans. Image Process., 32:791- 806, 2023.","title":"Binary change guided hyperspectral multiclass change detection","authors":"Hu","year":"2023","venue":"IEEE Trans. Image Process","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Huo et al., 2023] Lu Huo, Jiahao Xia, Leijie Zhang, Haimin Zhang, and Min Xu. Multimodal hyperspectral image classification via interconnected fusion. arXiv preprint arXiv:2304.00495, 2023.","title":"Multimodal hyperspectral image classification via interconnected fusion","authors":"Lu Huo, Jiahao Xia, Leijie Zhang, Haimin Zhang, Min Xu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jia et al., 2023] Sen Jia, Xi Zhou, Shuguo Jiang, and Ruyan He. Collaborative contrastive learning for hyperspectral and lidar classification. IEEE Trans. Geosci. Remote Sens., 61:1-14, 2023.","title":"Collaborative contrastive learning for hyperspectral and lidar classification","authors":"Sen Jia, Xi Zhou, Shuguo Jiang, Ruyan He","year":"2023","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kawar et al., 2023] Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. Imagic: Text-based real image editing with diffusion models. In IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 6007-6017, 2023.","title":"Imagic: Text-based real image editing with diffusion models","authors":"Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, Michal Irani","year":"2023","venue":"IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kumari et al., 2023] Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. Multi- concept customization of text-to-image diffusion. In IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 1931-1941, 2023.","title":"Multiconcept customization of text-to-image diffusion","authors":"Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, Jun-Yan Zhu","year":"2023","venue":"IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2022a] Heng-Chao Li, Wen-Shuai Hu, Wei Li, Jun Li, Qian Du, and Antonio Plaza. A3 clnn: Spatial, spectral and multiscale attention convlstm neural network for mul- tisource remote sensing data classification. IEEE Trans. Neural Netw. Learn. Syst., 33(2):747-761, 2022.","title":"A3 clnn: Spatial, spectral and multiscale attention convlstm neural network for multisource remote sensing data classification","authors":"Li","year":"2022","venue":"IEEE Trans. Neural Netw. Learn. Syst","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2022b] Jiaojiao Li, Yuzhe Liu, Rui Song, Yunsong Li, Kailiang Han, and Qian Du. Sal 2 rn: A spatial-spectral salient reinforcement network for hyperspectral and lidar data fusion classification. IEEE Transactions on Geo- science and Remote Sensing, 61:1-14, 2022.","title":"Sal 2 rn: A spatial-spectral salient reinforcement network for hyperspectral and lidar data fusion classification","authors":"Li","year":"2022","venue":"IEEE Transactions on Geoscience and Remote Sensing","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2023a] Bo Li, Kaitao Xue, Bin Liu, and Yu-Kun Lai. Bbdm: Image-to-image translation with brownian bridge diffusion models. In IEEE Conf. Comput. Vis. Pat- tern Recognit. (CVPR), pages 1952-1961, 2023.","title":"Bbdm: Image-to-image translation with brownian bridge diffusion models","authors":"Li","year":"2023","venue":"IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Li et al., 2023b] Ke Li, Di Wang, Xu Wang, Gang Liu, Zili Wu, and Quan Wang. Mixing self-attention and convolu- tion: A unified framework for multi-source remote sens- ing data classification. IEEE Trans. Geosci. Remote Sens., 2023.","title":"Mixing self-attention and convolution: A unified framework for multi-source remote sensing data classification","authors":"Li","year":"2023","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lin et al., 2023] Junyan Lin, Feng Gao, Xiaochen Shi, Junyu Dong, and Qian Du. Ss-mae: Spatial-spectral masked autoencoder for multisource remote sensing image classification. IEEE Trans. Geosci. Remote Sens., 61:1- 14, 2023.","title":"Ss-mae: Spatial-spectral masked autoencoder for multisource remote sensing image classification","authors":"Junyan Lin, Feng Gao, Xiaochen Shi, Junyu Dong, Qian Du","year":"2023","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Luo et al., 2023] Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjölund, and Thomas B Schön. Refusion: En- abling large-size realistic image restoration with latent- space diffusion models. In IEEE Conf. Comput. Vis. Pat- tern Recognit. (CVPR), pages 1680-1691, 2023.","title":"Refusion: Enabling large-size realistic image restoration with latentspace diffusion models","authors":"Ziwei Luo, Zheng Fredrik K Gustafsson, Jens Zhao, Thomas B Sjölund, Schön","year":"2023","venue":"IEEE Conf. Comput. Vis. Pattern Recognit","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Meng et al., 2022] Qingfa Meng, Guoqing Ma, Taihan Wang, and Tingyi Wang. High-resolution density joint in- version method of airborne and ground gravity data with cross-constraint technique. IEEE Trans. Geosci. Remote Sens., 60:1-9, 2022.","title":"High-resolution density joint inversion method of airborne and ground gravity data with cross-constraint technique","authors":"Meng","year":"2022","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Metzger et al., 2023] Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler. Guided depth super-resolution by deep anisotropic diffusion. In IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 18237-18246, 2023.","title":"Guided depth super-resolution by deep anisotropic diffusion","authors":"Nando Metzger, Rodrigo Caye Daudt, Konrad Schindler","year":"2023","venue":"IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Mohla et al., 2020] Satyam Mohla, Shivam Pande, Biplab Banerjee, and Subhasis Chaudhuri. Fusatnet: Dual atten- tion based spectrospatial multimodal fusion network for hyperspectral and lidar classification. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 416-425, 2020.","title":"Fusatnet: Dual attention based spectrospatial multimodal fusion network for hyperspectral and lidar classification","authors":"Satyam Mohla, Shivam Pande, Biplab Banerjee, Subhasis Chaudhuri","year":"2020","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Rombach et al., 2022] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 10684-10695, 2022.","title":"High-resolution image synthesis with latent diffusion models","authors":"Rombach","year":"2022","venue":"IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Roy et al., 2023] Swalpa Kumar Roy, Ankur Deria, Dan- feng Hong, Behnood Rasti, Antonio Plaza, and Jocelyn Chanussot. Multimodal fusion transformer for remote sensing image classification. IEEE Trans. Geosci. Remote Sens., 61:1-20, 2023.","title":"Multimodal fusion transformer for remote sensing image classification","authors":"Roy","year":"2023","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Ruan et al., 2023] Ludan Ruan, Yiyang Ma, Huan Yang, Huiguo He, Bei Liu, Jianlong Fu, Nicholas Jing Yuan, Qin Jin, and Baining Guo. Mm-diffusion: Learning multi- modal diffusion models for joint audio and video gen- eration. In IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 10219-10228, 2023.","title":"Mm-diffusion: Learning multimodal diffusion models for joint audio and video generation","authors":"Ruan","year":"2023","venue":"IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2022a] Junjie Wang, Wei Li, Yunhao Gao, Mengmeng Zhang, Ran Tao, and Qian Du. Hyperspec- tral and sar image classification via multiscale interactive fusion network. IEEE Trans. Neural Netw. Learn. Syst., pages 1-15, 2022.","title":"Hyperspectral and sar image classification via multiscale interactive fusion network","authors":"Wang","year":"2022","venue":"IEEE Trans. Neural Netw. Learn. Syst","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2022b] Yinhuai Wang, Jiwen Yu, and Jian Zhang. Zero-shot image restoration using denoising diffu- sion null-space model. arXiv preprint arXiv:2212.00490, 2022.","title":"Zero-shot image restoration using denoising diffusion null-space model","authors":"Wang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang et al., 2023] Meng Wang, Feng Gao, Junyu Dong, Heng-Chao Li, and Qian Du. Nearest neighbor-based con- trastive learning for hyperspectral and lidar data classifica- tion. IEEE Trans. Geosci. Remote Sens., 61:1-16, 2023.","title":"Nearest neighbor-based contrastive learning for hyperspectral and lidar data classification","authors":"Meng Wang, Feng Gao, Junyu Dong, Heng-Chao Li, Qian Du","year":"2023","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wu et al., 2021] Xin Wu, Danfeng Hong, and Jocelyn Chanussot. Convolutional neural networks for multimodal remote sensing data classification. IEEE Trans. Geosci. Remote Sens., 60:1-10, 2021.","title":"Convolutional neural networks for multimodal remote sensing data classification","authors":"Xin Wu, Danfeng Hong, Jocelyn Chanussot","year":"2021","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Xiu et al., 2022] Di Xiu, Zongxu Pan, Yirong Wu, and Yuxin Hu. Mage: Multisource attention network with dis- criminative graph and informative entities for classifica- tion of hyperspectral and lidar data. IEEE Trans. Geosci. Remote Sens., 60:1-14, 2022.","title":"Mage: Multisource attention network with discriminative graph and informative entities for classification of hyperspectral and lidar data","authors":"Di Xiu, Zongxu Pan, Yirong Wu, Yuxin Hu","year":"2022","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xu et al., 2018] Xiaodong Xu, Wei Li, Qiong Ran, Qian Du, Lianru Gao, and Bing Zhang. Multisource remote sensing data classification based on convolutional neural network. IEEE Trans. Geosci. Remote Sens., 56(2):937- 949, 2018.","title":"Multisource remote sensing data classification based on convolutional neural network","authors":"Xiaodong Xu, Wei Li, Qiong Ran, Qian Du, Lianru Gao, Bing Zhang","year":"2018","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Xu et al., 2023a] Jiale Xu, Xintao Wang, Weihao Cheng, Yan-Pei Cao, Ying Shan, Xiaohu Qie, and Shenghua Gao. Dream3d: Zero-shot text-to-3d synthesis using 3d shape prior and text-to-image diffusion models. In IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 20908- 20918, 2023.","title":"Dream3d: Zero-shot text-to-3d synthesis using 3d shape prior and text-to-image diffusion models","authors":"Xu","year":"2023","venue":"IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Xu et al., 2023b] Lin Xu, Hao Zhu, Licheng Jiao, Wenhao Zhao, Xiaotong Li, Biao Hou, Zhongle Ren, and Wen- ping Ma. A dual-stream transformer with diff-attention for multispectral and panchromatic classification. IEEE Trans. Geosci. Remote Sens., 61:1-14, 2023.","title":"A dual-stream transformer with diff-attention for multispectral and panchromatic classification","authors":"Xu","year":"2023","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Xue et al., 2022] Zhixiang Xue, Xiong Tan, Xuchu Yu, Bing Liu, Anzhu Yu, and Pengqiang Zhang. Deep hi- erarchical vision transformer for hyperspectral and lidar data classification. IEEE Trans. Image Process., 31:3095- 3110, 2022.","title":"Deep hierarchical vision transformer for hyperspectral and lidar data classification","authors":"Zhixiang Xue, Xiong Tan, Xuchu Yu, Bing Liu, Anzhu Yu, Pengqiang Zhang","year":"2022","venue":"IEEE Trans. Image Process","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yang et al., 2022] Yi Yang, Daoye Zhu, Tengteng Qu, Qiangyu Wang, Fuhu Ren, and Chengqi Cheng. Single- stream cnn with learnable architecture for multisource re- mote sensing data. IEEE Trans. Geosci. Remote Sens., 60:1-18, 2022.","title":"Singlestream cnn with learnable architecture for multisource remote sensing data","authors":"Yi Yang, Daoye Zhu, Tengteng Qu, Qiangyu Wang, Fuhu Ren, Chengqi Cheng","year":"2022","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Yao et al., 2023a] Jing Yao, Danfeng Hong, Haipeng Wang, Hao Liu, and Jocelyn Chanussot. Ucsl: Towards unsuper- vised common subspace learning for cross-modal image classification. IEEE Trans. Geosci. Remote Sens., 2023.","title":"Ucsl: Towards unsupervised common subspace learning for cross-modal image classification","authors":"Yao","year":"2023","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Yao et al., 2023b] Jing Yao, Bing Zhang, Chenyu Li, Dan- feng Hong, and Jocelyn Chanussot. Extended vision trans- former (exvit) for land use and land cover classification: A multimodal deep learning framework. IEEE Trans. Geosci. Remote Sens., 2023.","title":"Extended vision transformer (exvit) for land use and land cover classification: A multimodal deep learning framework","authors":"Yao","year":"2023","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhang et al., 2020] Mengmeng Zhang, Wei Li, Qian Du, Lianru Gao, and Bing Zhang. Feature extraction for clas- sification of hyperspectral and lidar data using patch-to- patch cnn. IEEE Trans. Cybern., 50(1):100-111, 2020.","title":"Feature extraction for classification of hyperspectral and lidar data using patch-topatch cnn","authors":"Mengmeng Zhang, Wei Li, Qian Du, Lianru Gao, Bing Zhang","year":"2020","venue":"IEEE Trans. Cybern","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhang et al., 2023] Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang, Chongyang Ma, Weiming Dong, and Changsheng Xu. Inversion-based style transfer with diffu- sion models. In IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 10146-10156, 2023.","title":"Inversion-based style transfer with diffusion models","authors":"Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang, Chongyang Ma, Weiming Dong, Changsheng Xu","year":"2023","venue":"IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhao et al., 2020] Xudong Zhao, Ran Tao, Wei Li, Heng- Chao Li, Qian Du, Wenzhi Liao, and Wilfried Philips. Joint classification of hyperspectral and lidar data using hi- erarchical random walk and deep cnn architecture. IEEE Trans. Geosci. Remote Sens., 58(10):7355-7370, 2020.","title":"Joint classification of hyperspectral and lidar data using hierarchical random walk and deep cnn architecture","authors":"Xudong Zhao, Ran Tao, Wei Li, Heng-Chao Li, Qian Du, Wenzhi Liao, Wilfried Philips","year":"2020","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhao et al., 2022] Guangrui Zhao, Qiaolin Ye, Le Sun, Ze- bin Wu, Chengsheng Pan, and Byeungwoo Jeon. Joint classification of hyperspectral and lidar data using a hier- archical cnn and transformer. IEEE Trans. Geosci. Remote Sens., 61:1-16, 2022.","title":"Joint classification of hyperspectral and lidar data using a hierarchical cnn and transformer","authors":"Zhao","year":"2022","venue":"IEEE Trans. Geosci. Remote Sens","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhu et al., 2023] Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiezhang Cao, Bihan Wen, Radu Timofte, and Luc Van Gool. Denoising diffusion models for plug-and-play image restoration. In IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 1219-1229, 2023.","title":"Denoising diffusion models for plug-and-play image restoration","authors":"Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiezhang Cao, Bihan Wen, Radu Timofte, Luc Van Gool","year":"2023","venue":"IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}