Deep learning in remote sensing applications: A meta-analysis and review

By Lei Ma, Yu Liu, Xueliang Zhang, Yuanxin Ye, Gaofei Yin, B. A. Johnson. Isprs Journal of Photogrammetry and Remote Sensing, 2019

1,955 citations recorded by the source corpus.

Abstract Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. In this study, the major DL concepts pertinent to remote-sensing are introduced, and more than 200 publications in this field, most of which were published during the last two years, are reviewed and analyzed. Initially, a meta-analysis was conducted to analyze the status of remote sensing DL studies in terms of the study targets, DL model(s) used, image spatial resolution(s), type of study area, and level of classification accuracy achieved. Subsequently, a detailed review is conducted to describe/discuss how DL has been applied for remote sensing image analysis tasks including image fusion, image registration, scene classification, object detection, land use and land cover (LULC) classification, segmentation, and object-based image analysis (OBIA). This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping. Finally, a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.

View Paper: https://www.semanticscholar.org/paper/2d0fd6eb86f2378262f2c2767e1362fc6a2e27fa

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Citations in the corpus

Mapping dominant leaf type based on combined Sentinel-1/-2 data – Challenges for mountainous countries (2021, background): “(2006), there has been increasing interest in using deep learning in earth observations (Zhu et al., 2017; Rammer and Seidl, 2019; Ma et al., 2019; Hoeser and Kuenzer, 2020), and lately also for classifying tree species (e.”
Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast (2020, methodology): “Most available DL-based image characterization methods in the RS field rely on a supervised learning scheme, in which a large amount of labeled scenes is required to properly train the models and prevent overfitting [17].”
A review of deep learning methods for semantic segmentation of remote sensing imagery (2021, background): “Ma et al. (2019) reviewed deep learning methods for remote sensing in terms of study targets, deep learning models, image resolution, type of study area, and level of classification accuracy.”
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery (2021, unknown): “Since then, CNN-based methods have dominated the semantic segmentation task in the remote sensing field (Kemker et al., 2018; Kotaridis and Lazaridou, 2021; Ma et al., 2019; Tong et al., 2020; Zhao and Du, 2016; Zhu et al., 2017).”
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, background): “The third one (Green) shows the use of “deep learning” techniques in LULC classification with all types of remote sensing data, emerged in the last years (interested readers can find a review on such publications in [26]).”
Transformers in Remote Sensing: A Survey (2022, background): “[16] review major deep learning concepts in remote sensing with respect to image resolution and study area.”
Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review (2020, methodology): “An alternative is the use of unsupervised classification methods [133].”
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “[81] provided a review of the applications of deep learning in remote sensing image analysis.”
Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, background): “Because of their tremendous modeling and learning capabilities, deep learning approaches represent the link between the image object and its real-world geographical elements as closely as possible, allowing for more real-world change information [20,21].”
A Review on Deep Learning in UAV Remote Sensing (2021, methodology): “We considered DL, but added CNN, as it is one of the main DL-based architectures used in remote sensing applications [128].”
Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review (2020, background): “…radial basis function (RBF), decision tree (DT), multilayer perception (MLP), naive Bayes (NB), maximum likelihood classiﬁer (MLC), and fuzzy logic [45,46], while the unsupervised classiﬁcation techniques include A ﬃ nity Propagation (AP) cluster algorithm, fuzzy c-means algorithms, K-means…”
Review on remote sensing methods for landslide detection using machine and deep learning (2020, methodology): “33 DL performs more superior to traditional classifier like SVM and RF for LULC classification.”
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, background): “For instance, three typical models of deep learning algorithms, namely deep belief network, convolutional neural networks, and stacked auto-encoder, were analyzed in [62].”