Remote Sensing Image Classification
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Benchmarks
FireRisk
Most implemented
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
RSMamba: Remote Sensing Image Classification with State Space Model
MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image Classification
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification
Multimodal Fusion Transformer for Remote Sensing Image Classification
Lake Ice Monitoring with Webcams and Crowd-Sourced Images
Papers
Representative Spectral Correlation Network for Multi-source Remote Sensing Image Classification
Hyperspectral image (HSI) and SAR/LiDAR data offer complementary spectral and structural information for land-cover classification. However, their effective fusion remains challenging due to two major limitations: The sp…
Remote Sensing Image ClassificationPhysically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification
Adversarial attacks pose a severe threat to the reliability of deep learning models in remote sensing (RS) image classification. Most existing methods rely on direct pixel-wise perturbations, failing to exploit the inher…
Remote Sensing Image ClassificationThe Impact of Federated Learning on Distributed Remote Sensing Archives
Remote sensing archives are inherently distributed: Earth observation missions such as Sentinel-1, Sentinel-2, and Sentinel-3 have collectively accumulated more than 5 petabytes of imagery, stored and processed across ma…
Remote Sensing Image ClassificationFederated LearningQMC-Net: Data-Aware Quantum Representations for Remote Sensing Image Classification
Hybrid quantum-classical models offer a promising route for learning from complex data; however, their application to multi-band remote sensing imagery often relies on generic, data-agnostic quantum circuits that fail to…
Remote Sensing Image ClassificationRemote Sensing Image Classification Using Deep Ensemble Learning
Remote sensing imagery plays a crucial role in many applications and requires accurate computerized classification techniques. Reliable classification is essential for transforming raw imagery into structured and usable …
Remote Sensing Image ClassificationEnsemble LearningDemystifying KAN for Vision Tasks: The RepKAN Approach
Remote sensing image classification is essential for Earth observation, yet standard CNNs and Transformers often function as uninterpretable black-boxes. We propose RepKAN, a novel architecture that integrates the struct…
Remote Sensing Image Classification