Papers Remote Sensing Image Classification
“Remote Sensing Image Classification” 태그가 달린 논문 109편 · 필터 해제
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 ClassificationAdaptive Multi-Scale Correlation Meta-Network for Few-Shot Remote Sensing Image Classification
Few-shot learning in remote sensing remains challenging due to three factors: the scarcity of labeled data, substantial domain shifts, and the multi-scale nature of geospatial objects. To address these issues, we introdu…
Remote Sensing Image ClassificationFew-Shot LearningNoise-Adaptive Regularization for Robust Multi-Label Remote Sensing Image Classification
The development of reliable methods for multi-label classification (MLC) has become a prominent research direction in remote sensing (RS). As the scale of RS data continues to expand, annotation procedures increasingly r…
Remote Sensing Image ClassificationMulti-Label ClassificationReconstruction Guided Few-shot Network For Remote Sensing Image Classification
Few-shot remote sensing image classification is challenging due to limited labeled samples and high variability in land-cover types. We propose a reconstruction-guided few-shot network (RGFS-Net) that enhances generaliza…
Remote Sensing Image ClassificationImage ReconstructionLeveraging Membership Inference Attacks for Privacy Measurement in Federated Learning for Remote Sensing Images
Federated Learning (FL) enables collaborative model training while keeping training data localized, allowing us to preserve privacy in various domains including remote sensing. However, recent studies show that FL models…
Remote Sensing Image ClassificationScene ClassificationFederated LearningFrequency-Aware Vision-Language Multimodality Generalization Network for Remote Sensing Image Classification
The booming remote sensing (RS) technology is giving rise to a novel multimodality generalization task, which requires the model to overcome data heterogeneity while possessing powerful cross-scene generalization ability…
Remote Sensing Image ClassificationNeighborhood Feature Pooling for Remote Sensing Image Classification
In this work, we introduce Neighborhood Feature Pooling (NFP), a novel pooling layer designed to enhance texture-aware representation learning for remote sensing image classification. The proposed NFP layer captures rela…
Remote Sensing Image ClassificationComputational EfficiencyRepresentation LearningA Spatial-Spectral-Frequency Interactive Network for Multimodal Remote Sensing Classification
Deep learning-based methods have achieved significant success in remote sensing Earth observation data analysis. Numerous feature fusion techniques address multimodal remote sensing image classification by integrating gl…
Remote Sensing Image ClassificationSemantics-Aware Hierarchical Consensus Learning for Remote Sensing Image Classification
Deep learning has become increasingly important in remote sensing image classification due to its ability to extract semantic information from complex data. Classification tasks often include predefined label hierarchies…
Remote Sensing Image ClassificationTime Series ClassificationCSFMamba: Cross State Fusion Mamba Operator for Multimodal Remote Sensing Image Classification
Multimodal fusion has made great progress in the field of remote sensing image classification due to its ability to exploit the complementary spatial-spectral information. Deep learning methods such as CNN and Transforme…
Remote Sensing Image ClassificationL-MCAT: Unpaired Multimodal Transformer with Contrastive Attention for Label-Efficient Satellite Image Classification
We propose the Lightweight Multimodal Contrastive Attention Transformer (L-MCAT), a novel transformer-based framework for label-efficient remote sensing image classification using unpaired multimodal satellite data. L-MC…
Remote Sensing Image ClassificationSatellite Image ClassificationMVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture
Hyperspectral image (HSI) classification faces challenges such as high-dimensional data, limited training samples, and spectral redundancy, which often lead to overfitting and insufficient generalization capability. This…
Computational EfficiencyHyperspectral Image Classificationimage-classificationImage Classification+2Remote Sensing Image Classification with Decoupled Knowledge Distillation
To address the challenges posed by the large number of parameters in existing remote sensing image classification models, which hinder deployment on resource-constrained devices, this paper proposes a lightweight classif…
Classificationimage-classificationImage ClassificationKnowledge Distillation+1A Diff-Attention Aware State Space Fusion Model for Remote Sensing Classification
Multispectral (MS) and panchromatic (PAN) images describe the same land surface, so these images not only have their own advantages, but also have a lot of similar information. In order to separate these similar informat…
image-classificationImage ClassificationMambaRemote Sensing Image ClassificationA Multi-Modal Federated Learning Framework for Remote Sensing Image Classification
Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients) without sharing the local data of the clients. Most of the existing FL methods assume …
Federated Learningimage-classificationImage ClassificationMulti-Label Classification+2