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publications
Style transformation-based change detection using adversarial learning with object boundary constraints
Published in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021
This paper proposes an object-level boundary-preserving generative adversarial network (BPGAN) for style transformation-based CD of bi-temporal images.
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A hybrid model-based and data-driven approach for cloud removal in satellite imagery using multi-scale distortion-aware networks
Published in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021
This paper proposes a novel Cloud Aware and Feature Extraction (CAFE) module by incorporating the physical model of cloud distortion considering atmosphere light, cloud reflectance light and cloud transmission.
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Cloud removal in optical remote sensing imagery using multiscale distortion-aware networks
Published in IEEE Geoscience and Remote Sensing Letters, 2022
This paper proposes a multiscale distortion-aware cloud removal (MSDA-CR) network consisting of multiple cloud-distortion-aware representation learning (CDARL) modules combined in a multiscale grid architecture.
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Multilevel Deformable Attention-Aggregated Networks for Change Detection in Bitemporal Remote Sensing Imagery
Published in IEEE Transactions on Geoscience and Remote Sensing, 2022
This paper proposes multilevel deformable attention-aggregated networks (MLDANets) to effectively learn long-range dependencies across multiple levels of bitemporal convolutional features for multiscale context aggregation.
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Federated Deep Learning With Prototype Matching for Object Extraction From Very-High-Resolution Remote Sensing Images
Published in IEEE Transactions on Geoscience and Remote Sensing, 2023
This paper proposes a novel federated learning scheme with prototype matching (FedPM) to collaboratively learn a richer DCNN model by leveraging remote sensing data distributed among multiple clients.
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Cross-domain landslide mapping from large-scale remote sensing images using prototype-guided domain-aware progressive representation learning
Published in ISPRS Journal of Photogrammetry and Remote Sensing, 2023
This paper proposes a prototype-guided domain-aware progressive representation learning (PG-DPRL) method for cross-domain landslide mapping from large-scale remote sensing images based on the multitarget domain adaptation (MTDA) technique.
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Weakly Supervised Local-Global Anchor Guidance Network for Landslide Extraction With Image-Level Annotations
Published in IEEE Geoscience and Remote Sensing Letters, 2023
This paper proposes a local–global anchor guidance network (LGAGNet) for weakly supervised landslide extraction.
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AI Security for Geoscience and Remote Sensing: Challenges and future trends
Published in IEEE Geoscience and Remote Sensing Magazine, 2023
This paper provides a systematic review of AI security-related research in the geoscience and remote sensing community.
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Txt2Img-MHN: Remote sensing image generation from text using modern Hopfield networks
Published in IEEE Transactions on Image Processing, 2023
This paper proposes a novel text-to-image modern Hopfield network (Txt2Img-MHN) to conduct hierarchical prototype learning on both text and image embeddings with modern Hopfield layers.
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Responsible AI for Earth Observation
Published in arxiv preprint, 2024
This paper comprehensively reviews the responsibility of current AI for earth observation schemes, with considerations of privacy, ethical concerns, security, and unfair biases, and the obejct of AI for social good.
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MaskCD: A Remote Sensing Change Detection Network Based on Mask Classification
Published in IEEE Transactions on Geoscience and Remote Sensing, 2024
This paper develops a change detection network based on mask classification.
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MineNetCD: A Benchmark for Global Mining Change Detection on Remote Sensing Imagery
Published in arxiv preprint, 2024
This paper develops a mining change detection benchmark consists of a mining change detection dataset, a ChangeFFT-based model, and a unified change detection framework.
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Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models
Published in International Journal of Applied Earth Observation and Geoinformation, 2024
This paper is about the universal adversarial defense in remote sensing, which utilized a DDPM model to defend the deep learning models against the adversarial perturbations.
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talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching Assistant at the CUHK Shenzhen
Undergraduate course, The Chinese University of Hong Kong, Shenzhen, School of Science and Engineering, 2023
I served as a teaching assistant and gave several tutorials for the course “CSC1002: Computational Laboratory” at the Chinese University of Hong Kong, Shenzhen, during the spring semester of 2021. I was awarded a prize for Outstanding Teaching Assistant for the quality of my tutorials.