2020
DOI: 10.3390/rs12060959
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Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland

Abstract: Deep learning has already been proved as a powerful state-of-the-art technique for many image understanding tasks in computer vision and other applications including remote sensing (RS) image analysis. Unmanned aircraft systems (UASs) offer a viable and economical alternative to a conventional sensor and platform for acquiring high spatial and high temporal resolution data with high operational flexibility. Coastal wetlands are among some of the most challenging and complex ecosystems for land cover prediction… Show more

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Cited by 69 publications
(53 citation statements)
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“…Thanks to the increasing availability of large data sets and affordable computational power, deep‐learning algorithms can now model complex nonlinear relationships in the earth sciences (Hernández et al ., 2016; Scher, 2018; Gagne II et al ., 2019; Lagerquist et al ., 2019; Kamangir et al ., 2018; Pashaei et al ., 2020; Reichstein et al ., 2019). The introduction of DLNNs in 2006 (Hinton et al ., 2006) has led to large changes in artificial intelligence (AI) and ML in many areas of research.…”
Section: Introductionmentioning
confidence: 99%
“…Thanks to the increasing availability of large data sets and affordable computational power, deep‐learning algorithms can now model complex nonlinear relationships in the earth sciences (Hernández et al ., 2016; Scher, 2018; Gagne II et al ., 2019; Lagerquist et al ., 2019; Kamangir et al ., 2018; Pashaei et al ., 2020; Reichstein et al ., 2019). The introduction of DLNNs in 2006 (Hinton et al ., 2006) has led to large changes in artificial intelligence (AI) and ML in many areas of research.…”
Section: Introductionmentioning
confidence: 99%
“…Machine learning, particularly deep learning, has been increasingly used to discover new patterns from data. For example, machine learning has benefited the areas of plant specialized metabolism (Moore et al., 2019), plant–pathogen interactions (Sperschneider, 2019), biomass estimation (Anderson et al., 2018; Zhao, Popescu, Meng, Pang, & Agca, 2011), wetland mapping (Pashaei, Kamangir, Starek, & Tissot, 2020), and geoscience (Reichstein et al., 2019). The challenges and opportunities of applying machine learning in those research areas offer important lessons for global change biology.…”
Section: Methods To Extract Knowledge From Big Datamentioning
confidence: 99%
“…, biomass estimation (Anderson et al, 2018; Zhao, Popescu, Meng, Pang, & Agca, 2011), wetland mapping(Pashaei, Kamangir, Starek, & Tissot, 2020), and geoscience(Reichstein et al, 2019). The challenges and opportunities of applying machine learning in those re-search areas offer important lessons for global change biology.…”
mentioning
confidence: 99%
“…Although the above methods based on deep learning greatly enhance the accuracy and efficiency of remote sensing image segmentation [ 24 , 25 , 26 ], a robust model usually requires relevant experts to spend a lot of time and energy to complete it. Feature extraction and fusion are key for robust and effective image processing in remote sensing [ 27 ].…”
Section: Introductionmentioning
confidence: 99%