FogFool creates fog-based adversarial perturbations using Perlin noise optimization to achieve high black-box transferability (83.74% TASR) and robustness to defenses in remote sensing classification.
Remote sensing image scene classifi- cation: Benchmark and state of the art
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Sentinel2Cap provides human-annotated captions for multimodal Sentinel satellite images, with zero-shot tests showing RGB outperforming SAR and prompts helping performance.
A hybrid CNN-ViT foundation model trained only on Dutch high-resolution imagery with temporal inputs achieves competitive results on global remote sensing benchmarks despite using fewer parameters and less pretraining data than larger state-of-the-art models.
citing papers explorer
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Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification
FogFool creates fog-based adversarial perturbations using Perlin noise optimization to achieve high black-box transferability (83.74% TASR) and robustness to defenses in remote sensing classification.
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Sentinel2Cap: A Human-Annotated Benchmark Dataset for Multimodal Remote Sensing Image Captioning
Sentinel2Cap provides human-annotated captions for multimodal Sentinel satellite images, with zero-shot tests showing RGB outperforming SAR and prompts helping performance.
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Developing a foundation model for high-resolution remote sensing data of the Netherlands
A hybrid CNN-ViT foundation model trained only on Dutch high-resolution imagery with temporal inputs achieves competitive results on global remote sensing benchmarks despite using fewer parameters and less pretraining data than larger state-of-the-art models.