3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images
By Shunping Ji, Chi Zhang, Anjian Xu, Yun Shi, Yulin Duan. Remote Sensing, 2018
351 citations recorded by the source corpus.
This study describes a novel three-dimensional (3D) convolutional neural networks (CNN) based method that automatically classifies crops from spatio-temporal remote sensing images. First, 3D kernel is designed according to the structure of multi-spectral multi-temporal remote sensing data. Secondly, the 3D CNN framework with fine-tuned parameters is designed for training 3D crop samples and learning spatio-temporal discriminative representations, with the full crop growth cycles being preserved. In addition, we introduce an active learning strategy to the CNN model to improve labelling accuracy up to a required threshold with the most efficiency. Finally, experiments are carried out to test the advantage of the 3D CNN, in comparison to the two-dimensional (2D) CNN and other conventional methods. Our experiments show that the 3D CNN is especially suitable in characterizing the dynamics of crop growth and outperformed the other mainstream methods.
Subfields: Analytical Chemistry, Ecology, Plant Science
View Paper: https://www.semanticscholar.org/paper/e577179db1d10d524d193eeaf67c232ff205088a
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In Analytical Chemistry
In Ecology
- Matteo Matteucci
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- Yuan Yuan
- Lei Lin
Citations in the corpus
- Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, methodology): “Existing studies are, for example, investigating the estimation of crop yield using CNN and multispectral images together with climate data [197], plant disease detection using CNN and smartphone‐acquired images [198], crop classification using 3‐D CNN and multi‐temporal multispectral image”
- Spotting Insects from Satellites: Modeling the Presence of Culicoides Imicola Through Deep CNNs (2019, background): “As observed in [13], the authors claimed that exploiting all bands leads to better results.”
- Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, methodology): “In the case of remote sensing, 3D CNNs are used to extract spectral-spatial [114,115] and spatial-temporal [113] features.”
- The color out of space: learning self-supervised representations for Earth Observation imagery (2020, background): “Indeed, a synergy between Earth Observation and Deep Learning techniques led to promising results, as highlighted by recent advances in land use and land cover classification [6], image fusion [7], and semantic segmentation [8].”
- Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series (2018, background): “An interested reader could refer to [41] for a first exploration of that topic.”