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Land Use Classification in Remote Sensing Images by Convolutional Neural Networks

arXiv 1508.00092 ↗First posted 1 Aug 2015cs.CVcited by 19

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Abstract (arXiv)

We explore the use of convolutional neural networks for the semantic classification of remote sensing scenes. Two recently proposed architectures, CaffeNet and GoogLeNet, are adopted, with three different learning modalities. Besides conventional training from scratch, we resort to pre-trained networks that are only fine-tuned on the target data, so as to avoid overfitting problems and reduce design time. Experiments on two remote sensing datasets, with markedly different characteristics, testify on the effectiveness and wide applicability of the proposed solution, which guarantees a significant performance improvement over all state-of-the-art references.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Aug 2015.

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Citing papers per year: 2021: 3; 2022: 7; 2023: 5; 2024: 0; 2025: 4.

19 papers.

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  1. 2022From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Datacited by 38
  2. 2022Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classificationcited by 18
  3. 2023FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learningcited by 13
  4. 2021Adversarial Attacks against a Satellite-borne Multispectral Cloud Detectorcited by 11
  5. 2023Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classificationcited by 4
  6. 2022Relict landslide detection using Deep-Learning architectures for image segmentation in rainforest areas: A new frameworkcited by 2
  7. 2025Urban Computing in the Era of Large Language Modelscited by 2
  8. 2021Airplane Detection Based on Mask Region Convolution Neural Networkcited by 1
  9. 2022Multi-Label Classification on Remote-Sensing Imagescited by 1
  10. 2022Forensic Dental Age Estimation Using Modified Deep Learning Neural Networkcited by 1
  11. 2022Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Modelscited by 1
  12. 2023Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite Imagerycited by 1
7 more
  1. 2025On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensingcited by 1
  2. 2021A Hierarchical Approach to Remote Sensing Scene Classificationcited by 0
  3. 2022DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classificationcited by 0
  4. 2023Creating Ensembles of Classifiers through UMDA for Aerial Scene Classificationcited by 0
  5. 2023The identification of garbage dumps in the rural areas of Cyprus through the application of deep learning to satellite imagerycited by 0
  6. 2025Practical GPU Choices for Earth Observation: ResNet-50 Training Throughput on Integrated, Laptop, and Cloud Acceleratorscited by 0
  7. 2025Hierarchical Fusion of Local and Global Visual Features with Mixture-of-Experts for Remote Sensing Image Scene Classificationcited by 0