Review on Convolutional Neural Networks (CNN) in vegetation remote sensing
By Teja Kattenborn, Jens Leitloff, Felix Schiefer, Stefan Hinz. Isprs Journal of Photogrammetry and Remote Sensing, 2021
1,587 citations recorded by the source corpus.
Abstract Identifying and characterizing vascular plants in time and space is required in various disciplines, e.g. in forestry, conservation and agriculture. Remote sensing emerged as a key technology revealing both spatial and temporal vegetation patterns. Harnessing the ever growing streams of remote sensing data for the increasing demands on vegetation assessments and monitoring requires efficient, accurate and flexible methods for data analysis. In this respect, the use of deep learning methods is trend-setting, enabling high predictive accuracy, while learning the relevant data features independently in an end-to-end fashion. Very recently, a series of studies have demonstrated that the deep learning method of Convolutional Neural Networks (CNN) is very effective to represent spatial patterns enabling to extract a wide array of vegetation properties from remote sensing imagery. This review introduces the principles of CNN and distils why they are particularly suitable for vegetation remote sensing. The main part synthesizes current trends and developments, including considerations about spectral resolution, spatial grain, different sensors types, modes of reference data generation, sources of existing reference data, as well as CNN approaches and architectures. The literature review showed that CNN can be applied to various problems, including the detection of individual plants or the pixel-wise segmentation of vegetation classes, while numerous studies have evinced that CNN outperform shallow machine learning methods. Several studies suggest that the ability of CNN to exploit spatial patterns particularly facilitates the value of very high spatial resolution data. The modularity in the common deep learning frameworks allows a high flexibility for the adaptation of architectures, whereby especially multi-modal or multi-temporal applications can benefit. An increasing availability of techniques for visualizing features learned by CNNs will not only contribute to interpret but to learn from such models and improve our understanding of remotely sensed signals of vegetation. Although CNN has not been around for long, it seems obvious that they will usher in a new era of vegetation remote sensing.
Subfields: Ecological Modeling, Ecology, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/5aadc8c5638bc86516dadd66d841e5b8ac3b4f03
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Citations in the corpus
- Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R-CNN (2023, background): “…convolutional neural networks (CNNs) is revolutionizing vegetation science through its ability to exploit spatial structures and automatically extract high-level features from image data (e.g. analyses of satellite imagery) (Kattenborn et al 2021; Mugabowindekwe et al., 2022; Zhu et al., 2017).”
- A high-resolution canopy height model of the Earth (2022, neutral): “Deep learning is revolutionizing fields ranging from medicine 20 to weather forecasting 21 and has great potential to advance environmental monitoring 22,23 , but its application to global remote sensing is technically challenging due to the large data volume 22,24 .”