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Found 8 papers, 4 papers with code
A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking
Surprisingly, there has been a lack of comprehensive studies on the robustness of RS tasks, prompting us to undertake a thorough survey and benchmark on the robustness of image classification and object detection in RS.
CBA: Contextual Background Attack against Optical Aerial Detection in the Physical World
To further strengthen the attack performance, the adversarial patches are forced to be outside targets during training, by which the detected objects of interest, both on and outside patches, benefit the accumulation of attack efficacy.
Contextual adversarial attack against aerial detection in the physical world
We propose an innovative contextual attack method against aerial detection in real scenarios, which achieves powerful attack performance and transfers well between various aerial object detectors without smearing or blocking the interested objects to hide.
Benchmarking Adversarial Patch Against Aerial Detection
DNNs are vulnerable to adversarial examples, which poses great security concerns for security-critical systems.
Multi-level Adversarial Spatio-temporal Learning for Footstep Pressure based FoG Detection
The proposed scheme also sheds light on improving subject-level clinical studies from other scenarios as it can be integrated with many existing deep architectures.
Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images
Spectral unmixing (SU) expresses the mixed pixels existed in hyperspectral images as the product of endmember and abundance, which has been widely used in hyperspectral imagery analysis.
Superpixel-guided Discriminative Low-rank Representation of Hyperspectral Images for Classification
Specifically, by utilizing the local spatial information and incorporating the predictions from a typical classifier, the first module segments pixels of an input HSI (or its restoration generated by the second module) into superpixels.
Hyperspectral Image Classification via Sparse Representation With Incremental Dictionaries
In this letter, we propose a new sparse representation (SR)-based method for hyperspectral image (HSI) classification, namely SR with incremental dictionaries (SRID).
