Search Results for author:
Found 16 papers, 7 papers with code
Sen2Fire: A Challenging Benchmark Dataset for Wildfire Detection using Sentinel Data
Additionally, our study underscores the positive impact of integrating Sentinel-5 aerosol data for wildfire detection.
There Are No Data Like More Data- Datasets for Deep Learning in Earth Observation
We hope to contribute to an understanding that the nature of our data is what distinguishes the Earth observation community from many other communities that apply deep learning techniques to image data, and that a detailed understanding of EO data peculiarities is among the core competencies of our discipline.
Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models
After that, a universal adversarial purification framework is developed using the forward and reverse process of the pre-trained diffusion models to purify the perturbations from adversarial samples.
Flexible Distribution Alignment: Towards Long-tailed Semi-supervised Learning with Proper Calibration
Long-tailed semi-supervised learning (LTSSL) represents a practical scenario for semi-supervised applications, challenged by skewed labeled distributions that bias classifiers.
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends
Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field.
Backdoor Attacks for Remote Sensing Data with Wavelet Transform
Despite its simplicity, the proposed method can significantly cheat the current state-of-the-art deep learning models with a high attack success rate.
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally.
Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks
To better evaluate the realism and semantic consistency of the generated images, we further conduct zero-shot classification on real remote sensing data using the classification model trained on synthesized images.
Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection
We make the multi-source landslide benchmark data (Landslide4Sense) and the tested DL models publicly available at \url{https://www. iarai. ac. at/landslide4sense}, establishing an important resource for remote sensing, computer vision, and machine learning communities in studies of image classification in general and applications to landslide detection in particular.
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark
Despite their simplicity, the proposed methods can generate transferable adversarial examples that deceive most of the state-of-the-art deep neural networks in both scene classification and semantic segmentation tasks with high success rates.
Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations
To this end, we further propose the consistency regularization strategy, where a base classifier and an expanded classifier are employed.
Self-Ensembling GAN for Cross-Domain Semantic Segmentation
In SE-GAN, a teacher network and a student network constitute a self-ensembling model for generating semantic segmentation maps, which together with a discriminator, forms a GAN.
Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer
To this end, a semantic-edge domain adaptation architecture is proposed, which uses an independent edge stream to process edge information, thereby generating high-quality semantic boundaries over the target domain.
Ranked #34 on
Synthetic-to-Real Translation
on GTAV-to-Cityscapes Labels
(using extra training data)
Robust Self-Ensembling Network for Hyperspectral Image Classification
Since the collection of pixel-level annotations for HSI is laborious and time-consuming, developing algorithms that can yield good performance in the small sample size situation is of great significance.
Fast Spatio-Temporal Residual Network for Video Super-Resolution
Recently, deep learning based video super-resolution (SR) methods have achieved promising performance.
Hyperspectral image classification via a random patches network
Due to the remarkable achievements obtained by deep learning methods in the fields of computer vision, an increasing number of researches have been made to apply these powerful tools into hyperspectral image (HSI) classification.
