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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…
Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection
Shizhen Chang, Michael Kopp, Pedram Ghamisi
In recent years, multi-view subspace learning has been garnering increasing attention. It aims to capture the inner relationships of the data that are collected from multiple sourc…
Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector
Puhong Duan, Xudong Kang, Pedram Ghamisi
Oil spill detection has attracted increasing attention in recent years since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal in…
EOD: The IEEE GRSS Earth Observation Database
Michael Schmitt, Pedram Ghamisi, Naoto Yokoya +1
In the era of deep learning, annotated datasets have become a crucial asset to the remote sensing community. In the last decade, a plethora of different datasets was published, eac…
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…
Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery
Laura E. C. La Rosa, Dario A. B. Oliveira, Pedram Ghamisi
The application of deep learning algorithms to Earth observation (EO) in recent years has enabled substantial progress in fields that rely on remotely sensed data. However, given t…
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…
Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives
Jun Yue, Leyuan Fang, Pedram Ghamisi +4
In recent years, supervised learning has been widely used in various tasks of optical remote sensing image understanding, including remote sensing image classification, pixel-wise…
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package
Daniel Coquelin, Behnood Rasti, Markus Götz +3
As with any physical instrument, hyperspectral cameras induce different kinds of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial step for analyzing hype…
Nonnegative-Constrained Joint Collaborative Representation with Union Dictionary for Hyperspectral Anomaly Detection
Shizhen Chang, Pedram Ghamisi
Recently, many collaborative representation-based (CR) algorithms have been proposed for hyperspectral anomaly detection. CR-based detectors approximate the image by a linear combi…
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…
Asymmetric Hash Code Learning for Remote Sensing Image Retrieval
Weiwei Song, Zhi Gao, Renwei Dian +3
Remote sensing image retrieval (RSIR), aiming at searching for a set of similar items to a given query image, is a very important task in remote sensing applications. Deep hashing…
NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery
Ming Lu, Leyuan Fang, Muxing Li +3
The use of deep learning for water extraction requires precise pixel-level labels. However, it is very difficult to label high-resolution remote sensing images at the pixel level.…