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Found 8 papers, 2 papers with code
Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities
Deep learning has taken by storm all fields involved in data analysis, including remote sensing for Earth observation.
ADRMX: Additive Disentanglement of Domain Features with Remix Loss
To this end, most of existing studies focus on extracting domain invariant features across the available source domains in order to mitigate the effects of inter-domain distributional changes.
Domain Generalized Object Detection for Remote Sensing Images
In this paper, we proposed a domain generalization method to address domain shift at the instance and image level for roof type detection from remote sensing images.
Unsupervised Domain Adaptation for Semantic Segmentation using One-shot Image-to-Image Translation via Latent Representation Mixing
Domain adaptation is one of the prominent strategies for handling both domain shift, that is widely encountered in large-scale land use/land cover map calculation, and the scarcity of pixel-level ground truth that is crucial for supervised semantic segmentation.
Domain Generalisation for Object Detection
Domain generalisation aims to promote the learning of domain-invariant features while suppressing domain specific features, so that a model can generalise well on previously unseen target domains.
Classification of remote sensing images using attribute profiles and feature profiles from different trees: a comparative study
The motivation of this paper is to conduct a comparative study on remote sensing image classification using the morphological attribute profiles (APs) and feature profiles (FPs) generated from different types of tree structures.
Recent Developments from Attribute Profiles for Remote Sensing Image Classification
Morphological attribute profiles (APs) are among the most effective methods to model the spatial and contextual information for the analysis of remote sensing images, especially for classification task.
GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data
This synthetic data is sampled from a GMM fitted to each class of the limited training data.
Classification Of Hyperspectral Images
General Classification
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