Results 11 to 20 of about 74,098 (264)
Spatio-Temporal Crop Classification On Volumetric Data [PDF]
2021 IEEE International Conference on Image Processing (ICIP), 2021 Large-area crop classification using multi-spectral imagery is a widely studied problem for several decades and is generally addressed using classical Random Forest classifier. Recently, deep convolutional neural networks (DCNN) have been proposed. However, these methods only achieved results comparable with Random Forest.
Muhammad Usman Qadeer +3 more
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XAI for Early Crop Classification
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023 We propose an approach for early crop classification through identifying important timesteps with eXplainable AI (XAI) methods. Our approach consists of training a baseline crop classification model to carry out layer-wise relevance propagation (LRP) so that the salient time step can be identified.
Ayshah Chan +2 more
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Crop classification by polarimetric SAR [PDF]
IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293), 2003 Polarimetric SAR-data of agricultural fields have been acquired by the Danish polarimetric L- and C-band SAR (EMISAR) during a number of missions at the Danish agricultural test site Foulum during 1995. The data are used to study the classification potential of polarimetric SAR data using the Wishart distributed covariance matrix.
Skriver, H. +3 more
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GraphCrop: Subgraph Cropping for Graph Classification
CoRR, 2020 We present a new method to regularize graph neural networks (GNNs) for better generalization in graph classification. Observing that the omission of sub-structures does not necessarily change the class label of the whole graph, we develop the \textbf{GraphCrop} (Subgraph Cropping) data augmentation method to simulate the real-world noise of sub ...
Yiwei Wang 0001 +4 more
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Time-Series of Sentinel-1 Interferometric Coherence and Backscatter for Crop-Type Mapping
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 The potential use of the interferometric coherence measured with Sentinel-1 satellites as input feature for crop classification is explored in this study.
Alejandro Mestre-Quereda +4 more
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Deep Learning-Based Virtual Optical Image Generation and Its Application to Early Crop Mapping
Applied Sciences, 2023 This paper investigates the potential of cloud-free virtual optical imagery generated using synthetic-aperture radar (SAR) images and conditional generative adversarial networks (CGANs) for early crop mapping, which requires cloud-free optical imagery at
No-Wook Park +3 more
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Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine
Remote Sensing, 2021 The purpose of this study was to evaluate the feasibility and applicability of object-oriented crop classification using Sentinel-1 images in the Google Earth Engine (GEE).
Chong Luo +6 more
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Remote Sensing, 2019 In order to discuss potential sustainability issues of expanding silage maize cultivation in Rhineland-Palatinate, spatially explicit monitoring is necessary. Publicly available statistical records are often not a sufficient basis for extensive research,
Mario Gilcher +3 more
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Remote Sensing, 2019 Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop ...
Ya’nan Zhou +3 more
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Crop Classification Based on Temporal Information Using Sentinel-1 SAR Time-Series Data
Remote Sensing, 2018 With the increasing temporal resolution of space-borne SAR, large amounts of intensity data are now available for continues land observations.
Lu Xu +4 more
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