Land Cover Classification from Multi-temporal, Multi-spectral Remotely Sensed Imagery using Patch-Based Recurrent Neural Networks
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Summary
This work proposes a new patch-based recurrent neural network (PB-RNN) system tailored for classifying multi-temporal remote sensing data that achieves a significant improvement in the classification accuracy.
- Type
- letter
- Published
- 2017-08-02
- Cited by
- 97
- References
- 55
- Access
- Open access
- OpenAlex
- https://openalex.org/W2742982421
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:10551087
Keywords
Computer science, Pixel, Land cover, Artificial intelligence, Remote sensing
References
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- A comprehensive change detection method for updating the National Land Cover Database to circa 2011
- Multi-Temporal Land-Cover Classification of Agricultural Areas in Two European Regions with High Resolution Spotlight TerraSAR-X Data
- Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products
- Cover- and density-based vegetation classifications of the Sonoran Desert using Landsat TM and ERS-1 SAR imagery
- Long Short-Term Memory
- GLC2000: a new approach to global land cover mapping from Earth observation data
- The application of artificial neural networks to the analysis of remotely sensed data
- Contribution of multispectral and multitemporal information from MODIS images to land cover classification
Cited by
- Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders
- A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China
- An Entropy and MRF Model-Based CNN for Large-Scale Landsat Image Classification
- Hyperspectral Image Classification Using Similarity Measurements-Based Deep Recurrent Neural Networks
- A Novel Spatio-Temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images
- Meta-analysis of deep neural networks in remote sensing: A comparative study of mono-temporal classification to support vector machines
- Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models
- Recurrent feedback CNN for water region estimation from multitemporal satellite images
- Self-Attention for Raw Optical Satellite Time Series Classification
- Mapping Miscanthus Using Multi-Temporal Convolutional Neural Network and Google Earth Engine
- Deep learning classifiers for hyperspectral imaging: A review
- SHADOW PROCESSING TECHNOLOGY OF AGRICULTURAL PLANT VIDEO IMAGE BASED ON PROBABLE LEARNING PIXEL CLASSIFICATION
- Machine Learning Classification Ensemble of Multitemporal Sentinel-2 Images: The Case of a Mixed Mediterranean Ecosystem
- Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture
- Monitoring of agricultural areas by using Sentinel 2 image time series and deep learning techniques
- A study of the robustness of the long short-term memory classifier to cloudy time series of multispectral images
- Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city
- Deep learning for classification of time series spectral images using combined multi-temporal and spectral features.
- Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran
- Manifold Preserving CNN for Pixel-Based Object Labelling in Images for High Dimensional Feature spaces
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