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Found 7 papers, 1 papers with code
A Comparative Study of Knowledge Transfer Methods for Misaligned Urban Building Labels
The workflow first identifies the best (with the highest evaluation scores) hyperparameters, lightweight CNNs for the Student (among 43 CNNs from Computer Vision), and encoder-decoder networks (EDNs) for both Teachers and Students.
A Novel Multi-scale Attention Feature Extraction Block for Aerial Remote Sensing Image Classification
Classification of very high-resolution (VHR) aerial remote sensing (RS) images is a well-established research area in the remote sensing community as it provides valuable spatial information for decision-making.
Benchmarking Deep Learning Architectures for Urban Vegetation Points Segmentation
To address this problem, we provide a comprehensive assessment of point-based deep learning models for semantic segmentation of vegetation class.
OSTA: One-shot Task-adaptive Channel Selection for Semantic Segmentation of Multichannel Images
In this study, the concept of pruning from a supernet is used for the first time to integrate the selection of channel combination and the training of a semantic segmentation network.
Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification
Very high-resolution (VHR) remote sensing (RS) scene classification is a challenging task due to the higher inter-class similarity and intra-class variability problems.
Dual skip connections in U-Net, ResUnet and U-Net3+ for remote extraction of buildings
The results conclude that selectively densifying feature maps and skip connections enhances network performance without a substantial increase in parameters.
Recent Advances in Scene Image Representation and Classification
For this, we, first, devise the taxonomy using the seminal existing methods proposed in the literature to this date {using deep learning (DL)-based, computer vision (CV)-based, and search engine (SE)-based methods}.
