Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

By John E. Ball, Derek T. Anderson, Chee Seng Chan. 2017

653 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/5d819d0949692635e73368f8879a3b2ab0462974

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Citations in the corpus

A review of deep learning methods for semantic segmentation of remote sensing imagery (2021, methodology): “A large number of channels make it difficult if not impossible to apply the existing deep learning frameworks for semantic segmentation (Ball et al., 2017; Signoroni, Savardi, Baronio, & Benini, 2019).”
Deep learning in environmental remote sensing: Achievements and challenges (2020, background): “…sensing have been published, these papers mainly focus on the preprocessing and classi-ﬁ cation problem from the image processing perspective (Ball et al., 2017; Ma et al., 2019; Zhang et al., 2016b; Zhu et al., 2017) or the DL-based quantitative remote sensing analysis in a speci ﬁ c ﬁ eld…”
A Review on Deep Learning in UAV Remote Sensing (2021, background): “A complete and systematic review was presented by Ball et al. [12] in a survey describing DL theories, tools, and its challenges in dealing with remote sensing data.”
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends (2020, methodology): “Using these insights, DL models can be better optimised for the properties of the data of interest [8] instead of solely relying on the findings made in computer vision, which uses different kinds of data.”
Deep Learning for Anomaly Detection: A Survey (2019, methodology): “Sensor networks anomaly detection has been reviewed by Ball et al. [2017].”
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals (2021, methodology): “ANOMALY detection has been one of the core research areas in machine learning for decades, with wide applications such as cyber-intrusion detection [1], medical care [2], sensor networks [3], video anomaly detection [4] and so”
Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, background): “One paper [22] published in reputable journals has focused on DL-based methods for remote sensing.”
Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery (2018, background): “This is because these advanced machine learning algorithms address the primary limitations of the conventional shallow-structured machine learning tools, such as Support Vector Machine (SVM) and Random Forest (RF) [19].”
Review on remote sensing methods for landslide detection using machine and deep learning (2020, methodology): “18 According to Reference 84, deep learning models for analyzing land cover usually depends on the sparse autoencoder (SAE), 84 Deep belief network (DBN), 71 Convolution neural network (CNN) 84 as seen in Figure 4A-C, among all these techniques CNN (ie, convolution neural network) is the most…”
Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges (2020, background): “be transferred as useful features in the new domain [13].”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, background): “[3] J.”
A Decade Survey of Transfer Learning (2010–2020) (2020, neutral): “Furthermore,thereare somesurveys[8]–[10],[10]–[12]particularlyconcentrateon TLapplicationsindifferentﬁelds:heathcaresystems,sentiment analysis,remotesensing,recommendationsystems,andsignal processing.”
In-domain representation learning for remote sensing (2019, background): “Reviews of some of these approaches can be found for instance in (Ball et al., 2017; Zhu et al., 2017; 2019).”
Deep learning and process understanding for data-driven Earth system science (2019, background): “Applications to problems in geosciences are in their infancy, but across the key problems (classification, anomaly detection, regression, space-or time dependent state prediction) there are promising examples arising (Table 1, Supplementary Box 1) 39,40 .”