Review on Convolutional Neural Networks (CNN) in vegetation remote sensing

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2021, Volume and Issue: 173, P. 24 - 49

Published: Jan. 18, 2021

Language: Английский

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Sentinel-2 Data for Land Cover/Use Mapping: A Review

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(14), P. 2291 - 2291

Published: July 16, 2020

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring Earth’s surface. development of Copernicus Programme by European Space Agency (ESA) and Union (EU) contributed effective surface producing Sentinel-2 multispectral products. satellites are second constellation ESA Sentinel missions carry onboard scanners. primary objective mission is provide high resolution data for land cover/use monitoring, climate change disaster as well complementing other such Landsat. Since launch instruments 2015, there have been many studies on classification which use images. However, no review dedicated application monitoring. Therefore, this focuses two aspects: (1) assessing contribution classification, (2) exploring performance different applications (e.g., forest, urban area natural hazard monitoring). present shows that a positive impact specifically crop, forests, areas, water resources. contemporary adoption can be attributed higher spatial (10 m) than medium images, temporal 5 days availability red-edge bands with multiple applications. ability integrate remotely sensed data, part analysis, improves overall accuracy (OA) when working free access policy drives increasing especially developing countries where financial resources acquisition limited. literature also produces accuracies (>80%) machine-learning classifiers support vector machine (SVM) Random forest (RF). maximum likelihood analysis common. Although offers opportunities challenges include mismatching Landsat OLI-8 lack thermal bands, differences among Sentinel-2. show promise potential contribute significantly towards

Language: Английский

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Computer vision technology in agricultural automation —A review

Information Processing in Agriculture, Journal Year: 2019, Volume and Issue: 7(1), P. 1 - 19

Published: Sept. 27, 2019

Computer vision is a field that involves making machine "see". This technology uses camera and computer instead of the human eye to identify, track measure targets for further image processing. With development vision, such has been widely used in agricultural automation plays key role its development. review systematically summarizes analyzes technologies challenges over past three years explores future opportunities prospects form latest reference researchers. Through analyses, it found existing can help small farming achieve advantages low cost, high efficiency precision. However, there are still major challenges. First, will continue expand into new application areas future, be more technological issues need overcome. It essential build large-scale data sets. Second, with rapid automation, demand professionals grow. Finally, robust performance related various complex environments also face analysis discussion, we believe combined intelligent as deep learning technology, applied every aspect production management based on datasets, solve current problems, better improve economic, general systems, thus promoting equipment systems direction.

Language: Английский

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Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2020, Volume and Issue: 170, P. 205 - 215

Published: Nov. 3, 2020

Language: Английский

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Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

Scientific Reports, Journal Year: 2019, Volume and Issue: 9(1)

Published: Nov. 27, 2019

Recent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies high spatial detail. Efficient methods are needed to fully harness this unpreceded source information for mapping. Deep learning algorithms Convolutional Neural Networks (CNN) currently paving new avenues field image analysis computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) combination with training directly derived from visual interpretation UAV-based RGB imagery mapping species communities. We demonstrate that indeed accurately segments maps communities (at least 84% accuracy). The fact only used suggests plant identification at very resolutions is facilitated through patterns rather than spectral information. Accordingly, presented compatible low-cost UAV systems easy operate thus applicable wide range users.

Language: Английский

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UAV & satellite synergies for optical remote sensing applications: A literature review

Science of Remote Sensing, Journal Year: 2021, Volume and Issue: 3, P. 100019 - 100019

Published: Feb. 6, 2021

Unmanned aerial vehicles (UAVs) and satellite constellations are both essential Earth Observation (EO) systems for monitoring land surface dynamics. The former is frequently used its acquisition flexibility ability to supply imagery with very high spatial resolution (VHSR); the latter interesting supplying time-series data over large areas. However, each of these sources generally separately even though they complementary have strong promising potential synergies. Data fusion a well-known technique exploit this multi-source synergy, but in practice, UAV synergies more specific, less well known need be formalized. In article, we review remote sensing studies that addressed sources. Current approaches were categorized distinguish four strategies: "data comparison", "multiscale explanation", "model calibration" fusion". Analysis literature revealed emerging trends, distinct strategies several applications allowed identify key contributions data. Finally, synergy seems currently under-exploited; therefore discussion proposed about related implications interoperability, machine learning sharing reinforce between UAVs satellites.

Language: Английский

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Remote sensing in forestry: current challenges, considerations and directions

Forestry An International Journal of Forest Research, Journal Year: 2023, Volume and Issue: 97(1), P. 11 - 37

Published: May 10, 2023

Abstract Remote sensing has developed into an omnipresent technology in the scientific field of forestry and is also increasingly used operational fashion. However, pace level uptake remote technologies forest inventory monitoring programs varies notably by geographic region. Herein, we highlight some key challenges that research can address near future to further increase acceptance, suitability integration remotely sensed data programs. We particularly emphasize three recurrent themes: (1) user uptake, (2) technical related inventories (3) map validation. Our recommendations concerning these thematic areas include a need communicate learn from success stories those regions where was successful due multi-disciplinary collaborations supported administrative incentives, shift regional case studies towards addressing ‘real world’ problems focusing on attributes match spatial scales information needs end users increased effort develop, communicate, apply best-practices for model validation including inform current scientists regarding functionalities best practices. Finally, present use monitoring, combined with possible, highlighting opportunity additional investigation.

Language: Английский

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Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature—A Case Study for Lousã Region, Portugal

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(18), P. 4585 - 4585

Published: Sept. 14, 2022

Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and threat wildfires. Remote sensing provides a powerful resource fundamental data at different spatial resolutions spectral regions, making it an essential tool for vegetation biomass management. Due ever-increasing availability free software, satellites have been predominantly used map, analyze, monitor natural resources conservation purposes. This study aimed map from Sentinel-2 (S2) complex mixed cover Lousã district Portugal. We ten multispectral bands with resolution 10 m, four indices, including Normalized Difference Index (NDVI), Green (GNDVI), Enhanced (EVI), Soil Adjusted (SAVI). After applying principal component analysis (PCA) on S2A bands, texture features, mean (ME), homogeneity (HO), correlation (CO), entropy (EN), were derived first three components. Textures obtained using Gray-Level Co-Occurrence Matrix (GLCM). As result, 26 independent variables extracted S2. defining land classes object-based approach, Random Forest (RF) classifier was applied. The accuracy evaluated by confusion matrix, metrics overall (OA), producer (PA), user (UA), kappa coefficient (Kappa). described classification methodology showed high OA 90.5% 89% mapping. Using GLCM features indices increased up 2%; however, achieved highest (92%), indicating features′ capability detecting variability species stand level. ME CO contribution among textures. GNDVI outperformed other variable importance. Moreover, only especially 11, 12, 2, potential classify 88%. that adding least one feature index into may effectively increase tree discrimination.

Language: Английский

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Ten deep learning techniques to address small data problems with remote sensing

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 125, P. 103569 - 103569

Published: Nov. 18, 2023

Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited training DL models, especially when these models represent key socio-environmental problems, such as the monitoring extreme, destructive climate events, biodiversity, sudden changes in ecosystem states. Such cases, also known small pose significant methodological challenges. This review summarises challenges RS domain possibility using emerging techniques to overcome them. We show that problem common challenge across disciplines scales results poor model generalisability transferability. then introduce an overview ten promising techniques: transfer learning, self-supervised semi-supervised few-shot zero-shot active weakly supervised multitask process-aware ensemble learning; we include validation technique spatial k-fold cross validation. Our particular contribution was develop flowchart helps users select which use given by answering few questions. hope our article facilitate applications tackle societally important environmental problems with reference data.

Language: Английский

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Review of ground and aerial methods for vegetation cover fraction (fCover) and related quantities estimation: definitions, advances, challenges, and future perspectives

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 199, P. 133 - 156

Published: April 12, 2023

Language: Английский

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