Remote Sensing Image Classification
30 papers with code • 1 benchmarks • 8 datasets
Datasets
AID
Million-AID
WHU-RS19
OpenStreetMap Multi-Sensor Scene Classification
FireRisk
RTI Rwanda Drone Crop Types
Latest papers
Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification
zh-hike/SAFF
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IEEE Geoscience and Remote Sensing Letters ( Volume: 18 ) 2022
In this letter, we propose a new method, called self-attention-based deep feature fusion (SAFF), to aggregate deep layer features and emphasize the weights of the complex objects of remote sensing scene images for remote sensing scene classification.
Multimodal Fusion Transformer for Remote Sensing Image Classification
ankurderia/mft
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Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared to convolutional neural networks (CNNs).
DKDFN: Domain Knowledge-Guided deep collaborative fusion network for multimodal unitemporal remote sensing land cover classification
The multibranch decoder enables land cover classification in a multitask learning setup, performing semantic seg mentation and reconstructing multimodal remote sensing indices, which are selected as representatives of domain knowledge.
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study
Efficiently implementing remote sensing image classification with high spatial resolution imagery can provide a significant value in Land Use and Land Cover (LULC) classification.
A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels
The proposed CCML identifies, ranks and corrects training images with noisy multi-labels through four main modules: 1) discrepancy module; 2) group lasso module; 3) flipping module; and 4) swap module.
A Multi-Task Deep Learning Framework for Building Footprint Segmentation
burakekim/MTL_homoscedastic_SRB
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The task of building footprint segmentation has been well-studied in the context of remote sensing (RS) as it provides valuable information in many aspects, however, difficulties brought by the nature of RS images such as variations in the spatial arrangements and in-consistent constructional patterns require studying further, since it often causes poorly classified segmentation maps.
Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding
We show that, for the downstream task of remote sensing image classification, using self-supervised pre-training on remote sensing images can give better results than using supervised pre-training on images of natural scenes.
Remote Sensing Image Classification with the SEN12MS Dataset
Using that, we provide results for several baseline models based on two standard CNN architectures and different input data configurations.
Attention-Based Second-Order Pooling Network for Hyperspectral Image Classification
ZhaohuiXue/A-SPN-release
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IEEE Transactions on Geoscience and Remote Sensing 2021
On the other hand, the optimization of complex hyperparameters (e. g., the layer number and convolutional kernel size) is time-consuming and a very tough task, making the designed DL framework unexplainable.
Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification
GatorSense/DREN
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In order to better represent statistical texture information for remote-sensing image classification, in this paper, we investigate performing joint dimensionality reduction and classification using a novel histogram neural network.
