Papers Remote Sensing Image Classification
“Remote Sensing Image Classification” 태그가 달린 논문 109편 · 필터 해제
Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques
This study investigates the classification of aerial images depicting transmission towers, forests, farmland, and mountains. To complete the classification job, features are extracted from input photos using a Convolutio…
image-classificationImage ClassificationRemote Sensing Image ClassificationTransfer LearningA synergistic CNN-transformer network with pooling attention fusion for hyperspectral image classification
In the hyperspectral image (HSI) classification task, each pixel is categorized into a specific land-cover category or material. Convolutional neural networks (CNNs) and transformers have been widely used to extract loca…
Hyperspectral Image Classificationimage-classificationImage ClassificationRemote Sensing Image Classification+1Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image Classification
Federated learning (FL) is a decentralized machine learning paradigm in which multiple clients collaboratively train a global model by exchanging only model updates with the central server without sharing the local data …
Federated Learningimage-classificationImage ClassificationRemote Sensing Image ClassificationFedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models
Remote sensing data is often distributed across multiple institutions, and due to privacy concerns and data-sharing restrictions, leveraging large-scale datasets in a centralized training framework is challenging. Federa…
Federated Learningimage-classificationImage ClassificationPrompt Learning+3RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image Classification
Since high resolution remote sensing image classification often requires a relatively high computation complexity, lightweight models tend to be practical and efficient. Model pruning is an effective method for model com…
Classificationimage-classificationImage ClassificationModel Compression+2Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification
Cross-scene image classification aims to transfer prior knowledge of ground materials to annotate regions with different distributions and reduce hand-crafted cost in the field of remote sensing. However, existing approa…
DiversityDomain Generalizationimage-classificationImage Classification+3Cloud Adversarial Example Generation for Remote Sensing Image Classification
Most existing adversarial attack methods for remote sensing images merely add adversarial perturbations or patches, resulting in unnatural modifications. Clouds are common atmospheric effects in remote sensing images. Ge…
Adversarial AttackAdversarial Defenseimage-classificationImage Classification+1Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models
This study focuses on identifying the most effective pre-trained model for land use classification in onboard satellite processing, emphasizing achieving high accuracy, computational efficiency, and robustness against no…
Computational EfficiencyEarth Observationimage-classificationImage Classification+2Low Saturation Confidence Distribution-based Test-Time Adaptation for Cross-Domain Remote Sensing Image Classification
Unsupervised Domain Adaptation (UDA) has emerged as a powerful technique for addressing the distribution shift across various Remote Sensing (RS) applications. However, most UDA approaches require access to source data, …
Domain Adaptationimage-classificationImage ClassificationMarketing+4MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image Classification
In the field of multi-source remote sensing image classification, remarkable progress has been made by using Convolutional Neural Network (CNN) and Transformer. Recently, Mamba-based methods built upon the State Space Mo…
Computational Efficiencyimage-classificationImage ClassificationMamba+1Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks
A significant amount of remotely sensed data is generated daily by many Earth observation (EO) spaceborne and airborne sensors over different countries of our planet. Different applications use those data, such as natura…
Earth Observationimage-classificationImage ClassificationRemote Sensing Image ClassificationGradient-Guided Multiscale Focal Attention Network for Remote Sensing Scene Classification
Remote sensing scene classification (RSSC) aims to understand and analyze the semantic information at the scene level with complex geographical properties. Despite the profound success of advanced deep models in automati…
Aerial Scene ClassificationRemote Sensing Image ClassificationScene ClassificationTransformer-based Federated Learning for Multi-Label Remote Sensing Image Classification
Federated learning (FL) aims to collaboratively learn deep learning model parameters from decentralized data archives (i.e., clients) without accessing training data on clients. However, the training data across clients …
Federated Learningimage-classificationImage ClassificationMulti-Label Classification+2A Label Propagation Strategy for CutMix in Multi-Label Remote Sensing Image Classification
The development of supervised deep learning-based methods for multi-label scene classification (MLC) is one of the prominent research directions in remote sensing (RS). Yet, collecting annotations for large RS image arch…
Data Augmentationimage-classificationImage ClassificationRemote Sensing Image Classification+1SOAR: Advancements in Small Body Object Detection for Aerial Imagery Using State Space Models and Programmable Gradients
Small object detection in aerial imagery presents significant challenges in computer vision due to the minimal data inherent in small-sized objects and their propensity to be obscured by larger objects and background noi…
image-classificationImage ClassificationMambaobject-detection+5Achieving Rotation Invariance in Convolution Operations: Shifting from Data-Driven to Mechanism-Assured
Achieving rotation invariance in deep neural networks without relying on data has always been a hot research topic. Intrinsic rotation invariance can enhance the model's feature representation capability, enabling better…
Data Augmentationimage-classificationImage ClassificationObject Recognition+1FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery
Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical, and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implem…
Objectobject-detectionObject DetectionObject Detection In Aerial Images+3RSMamba: Remote Sensing Image Classification with State Space Model
Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation. The recent advancements of Convolutional Neural Networks (CNNs) …
Classificationimage-classificationImage ClassificationMamba+2Leveraging 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. Our exploration includes feature-centric communicati…
ClassificationFederated Learningimage-classificationImage Classification+2ViGEO: an Assessment of Vision GNNs in Earth Observation
Satellite missions and Earth Observation (EO) systems represent fundamental assets for environmental monitoring and the timely identification of catastrophic events, long-term monitoring of both natural resources and hum…
Earth Observationimage-classificationImage ClassificationLand Cover Classification+1