Aerial Scene Classification
11 papers with code • 6 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Aerial Scene Classification
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GDIT
Most implemented papers
Attention-based Deep Multiple Instance Learning
AMLab-Amsterdam/AttentionDeepMIL
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ICML 2018
Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances.
An Empirical Study of Remote Sensing Pretraining
To this end, we train different networks from scratch with the help of the largest RS scene recognition dataset up to now -- MillionAID, to obtain a series of RS pretrained backbones, including both convolutional neural networks (CNN) and vision transformers such as Swin and ViTAE, which have shown promising performance on computer vision tasks.
Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
Large-scale vision foundation models have made significant progress in visual tasks on natural images, with vision transformers being the primary choice due to their good scalability and representation ability.
AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
The goal of AID is to advance the state-of-the-arts in scene classification of remote sensing images.
Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles
Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote and difficult to access areas.
A multiple-instance densely-connected ConvNet for aerial scene classification
BiQiWHU/Attention-based-Multi-instance-CNN
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IEEE Transactions on Image Processing 2020
It regards aerial scene classification as a multiple-instance learning problem so that local semantics can be further investigated.
Local semantic enhanced convnet for aerial scene recognition
BiQiWHU/LSENet
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IEEE Transactions on Image Processing 2021
Our LSE-Net consists of a context enhanced convolutional feature extractor, a local semantic perception module and a classification layer.
All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification
biqiwhu/agos
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IEEE Transactions on Geoscience and Remote Sensing 2022
Finally, our SSF allows our framework to learn the same scene scheme from multi-grain instance representations and fuses them, so that the entire framework is optimized as a whole.
All grains, one scheme (AGOS): Learning multigrain instance representation for aerial scene classification
biqiwhu/agos
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IEEE Transactions on Geoscience and Remote Sensing 2022
Finally, our SSF module allows our framework to learn the same scene scheme from multigrain instance representations and fuses them, so that the entire framework is optimized as a whole.
