Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

Abstract

Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering the rapid evolution of this field, this paper provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers. To be specific, we discuss the main challenges of remote sensing image scene classification and survey (1) Autoencoder-based remote sensing image scene classification methods, (2) Convolutional Neural Network-based remote sensing image scene classification methods, and (3) Generative Adversarial Network-based remote sensing image scene classification methods. In addition, we introduce the benchmarks used for remote sensing image scene classification and summarize the performance of more than two dozen of representative algorithms on three commonly-used benchmark data sets. Finally, we discuss the promising opportunities for further research.

This manuscript is the accepted version for IEEE JSTARS

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• An Empirical Study of Remote Sensing Pretraining
• BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval
• Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification
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• Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks
• All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification
• Universal Domain Adaptation for Remote Sensing Image Scene Classification
• Quantum Transfer Learning for Real-World, Small, and High-Dimensional Datasets
• Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene Classification
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• False: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image
• MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images
• A Novel Technique for Robust Training of Deep Networks With Multisource Weak Labeled Remote Sensing Data
• AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning
• FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2
• On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
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• Survey on Remote Sensing Scene Classification: From Traditional Methods to Large Generative AI Models