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 with no code
Achieving Rotation Invariance in Convolution Operations: Shifting from Data-Driven to Mechanism-Assured
Based on various types of non-learnable operators, including gradient, sort, local binary pattern, maximum, etc., this paper designs a set of new convolution operations that are natually invariant to arbitrary rotations.
Leveraging feature communication in federated learning for remote sensing image classification
In the realm of Federated Learning (FL) applied to remote sensing image classification, this study introduces and assesses several innovative communication strategies.
On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications
The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision.
Learning transformer-based heterogeneously salient graph representation for multimodal fusion classification of hyperspectral image and LiDAR data
Data collected by different modalities can provide a wealth of complementary information, such as hyperspectral image (HSI) to offer rich spectral-spatial properties, synthetic aperture radar (SAR) to provide structural information about the Earth's surface, and light detection and ranging (LiDAR) to cover altitude information about ground elevation.
Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification
To this end, we initially provide a systematic review of the FL algorithms presented in the computer vision community for image classification problems, and select several state-of-the-art FL algorithms based on their effectiveness with respect to training data heterogeneity across clients (known as non-IID data).
FedSN: A Novel Federated Learning Framework over LEO Satellite Networks
To this end, we propose FedSN as a general FL framework to tackle the above challenges, and fully explore data diversity on LEO satellites.
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.
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification
A common approach in practice to SSL pre-training is utilizing standard pre-training datasets, such as ImageNet.
Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions
So when applied to large-scale real-world GPS coordinate datasets, which require distance metric learning on the spherical surface, both types of models can fail due to the map projection distortion problem (2D) and the spherical-to-Euclidean distance approximation error (3D).
Transformer-based Multi-Modal Learning for Multi Label Remote Sensing Image Classification
In this paper, we introduce a novel Synchronized Class Token Fusion (SCT Fusion) architecture in the framework of multi-modal multi-label classification (MLC) of remote sensing (RS) images.
