An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy
Authors
Keywords
-
Journal
SENSORS
Volume 22, Issue 19, Pages 7428
Publisher
MDPI AG
Online
2022-10-10
DOI
10.3390/s22197428
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data
- (2021) Amal Chakhar et al. Remote Sensing
- Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping
- (2021) Anca Dabija et al. Remote Sensing
- Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data
- (2021) Daniel Kpienbaareh et al. Remote Sensing
- A framework for registering UAV-based imagery for crop-tracking in Precision Agriculture
- (2021) Alfonso López et al. International Journal of Applied Earth Observation and Geoinformation
- Satellite-based data fusion crop type classification and mapping in Rio Grande do Sul, Brazil
- (2021) Luan Pierre Pott et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain
- (2020) María Arias et al. Remote Sensing
- Fusing Multiseasonal Sentinel-2 Imagery for Urban Land Cover Classification With Multibranch Residual Convolutional Neural Networks
- (2020) Chunping Qiu et al. IEEE Geoscience and Remote Sensing Letters
- The ongoing nutrition transition thwarts long-term targets for food security, public health and environmental protection
- (2020) Benjamin Leon Bodirsky et al. Scientific Reports
- Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection
- (2020) Wei Zhao et al. Remote Sensing
- A random forest-based framework for crop mapping using temporal, spectral, textural and polarimetric observations
- (2019) Iman Khosravi et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Association between residential greenness and exposure to volatile organic compounds
- (2019) Ray Yeager et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)
- (2019) Vittorio Mazzia et al. Applied Sciences-Basel
- Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression
- (2018) Rajasheker Pullanagari et al. Remote Sensing
- Using recursive feature elimination in random forest to account for correlated variables in high dimensional data
- (2018) Burcu F. Darst et al. BMC GENETICS
- Crop classification with WorldView-2 imagery using Support Vector Machine comparing texture analysis approaches and grey relational analysis in Jianan Plain, Taiwan
- (2018) Shiuan Wan et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands
- (2018) Fabio Castaldi et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Feature-Level Fusion of Landsat 8 Data and SAR Texture Images for Urban Land Cover Classification
- (2018) Fatemeh Tabib Mahmoudi et al. Journal of the Indian Society of Remote Sensing
- Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
- (2017) Nataliia Kussul et al. IEEE Geoscience and Remote Sensing Letters
- Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data
- (2017) Yanfei Zhong et al. Remote Sensing
- Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images
- (2016) Christoph Hütt et al. Remote Sensing
- Improved Early Crop Type Identification By Joint Use of High Temporal Resolution SAR And Optical Image Time Series
- (2016) Jordi Inglada et al. Remote Sensing
- Feature Selection with theBorutaPackage
- (2015) Miron B. Kursa et al. Journal of Statistical Software
- Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines
- (2013) F. Löw et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]
- (2010) I Arel et al. IEEE Computational Intelligence Magazine
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started