205 citations · 683 across the 16 of their papers we have counts for
16 papers
On the Adversarial Robustness of Remote Sensing Semantic Change Detection
Weikang Yu, Yonghao Xu, Pedram Ghamisi
Semantic change detection (SCD) is a bitemporal dense-prediction task that jointly identifies changed regions and their semantic states before and after change. Unlike single-image…
Toward Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion
Yonghao Xu, Pedram Ghamisi, Qihao Weng
Recent years have witnessed the remarkable success of deep learning in remote sensing image interpretation, driven by the availability of large-scale benchmark datasets. However, t…
Multi-Target Unsupervised Domain Adaptation for Semantic Segmentation without External Data
Yonghao Xu, Pedram Ghamisi, Yannis Avrithis
Multi-target unsupervised domain adaptation (UDA) aims to learn a unified model to address the domain shift between multiple target domains. Due to the difficulty of obtaining anno…
There Are No Data Like More Data- Datasets for Deep Learning in Earth Observation
Michael Schmitt, Seyed Ali Ahmadi, Yonghao Xu +4
Carefully curated and annotated datasets are the foundation of machine learning, with particularly data-hungry deep neural networks forming the core of what is often called Artific…
Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models
Weikang Yu, Yonghao Xu, Pedram Ghamisi
Deep neural networks (DNNs) have risen to prominence as key solutions in numerous AI applications for earth observation (AI4EO). However, their susceptibility to adversarial exampl…
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends
Yonghao Xu, Tao Bai, Weikang Yu +3
Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-bas…
Backdoor Attacks for Remote Sensing Data with Wavelet Transform
Nikolaus Dräger, Yonghao Xu, Pedram Ghamisi
Recent years have witnessed the great success of deep learning algorithms in the geoscience and remote sensing realm. Nevertheless, the security and robustness of deep learning mod…
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
Omid Ghorbanzadeh, Yonghao Xu, Hengwei Zhao +12
The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the Institute of Advanced Research in Artificial Intelligence (IARAI) are presented here. The obj…
Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks
Yonghao Xu, Weikang Yu, Pedram Ghamisi +2
The synthesis of high-resolution remote sensing images based on text descriptions has great potential in many practical application scenarios. Although deep neural networks have ac…
Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection
Omid Ghorbanzadeh, Yonghao Xu, Pedram Ghamisi +2
This study introduces \textit{Landslide4Sense}, a reference benchmark for landslide detection from remote sensing. The repository features 3,799 image patches fusing optical layers…
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark
Yonghao Xu, Pedram Ghamisi
Deep neural networks have achieved great success in many important remote sensing tasks. Nevertheless, their vulnerability to adversarial examples should not be neglected. In this…
Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations
Yonghao Xu, Pedram Ghamisi
Deep learning algorithms have obtained great success in semantic segmentation of very high-resolution (VHR) images. Nevertheless, training these models generally requires a large a…
Self-Ensembling GAN for Cross-Domain Semantic Segmentation
Yonghao Xu, Fengxiang He, Bo Du +2
Deep neural networks (DNNs) have greatly contributed to the performance gains in semantic segmentation. Nevertheless, training DNNs generally requires large amounts of pixel-level…
Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer
Hongruixuan Chen, Chen Wu, Yonghao Xu +1
Unsupervised domain adaptation for semantic segmentation aims to make models trained on synthetic data (source domain) adapt to real images (target domain). Previous feature-level…
Robust Self-Ensembling Network for Hyperspectral Image Classification
Yonghao Xu, Bo Du, Liangpei Zhang
Recent research has shown the great potential of deep learning algorithms in the hyperspectral image (HSI) classification task. Nevertheless, training these models usually requires…
Fast Spatio-Temporal Residual Network for Video Super-Resolution
Sheng Li, Fengxiang He, Bo Du +3
Recently, deep learning based video super-resolution (SR) methods have achieved promising performance. To simultaneously exploit the spatial and temporal information of videos, emp…