Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series

IEEE Transactions on Geoscience and Remote Sensing

Volumn 54, Issue 11, 2016, Pages 6563-6573

  Bose, Pritam ^a   Kasabov, Nikola K ^b   Bruzzone, Lorenzo ^a   Hartono, Reggio N ^b  

^a UNIVERSITY OF TRENTO   (Italy)

^b AUCKLAND UNIVERSITY OF TECHNOLOGY   (New Zealand)

Author keywords

Crop yield forecasting; estimation; machine learning; Moderate Resolution Imaging Spectroradiometer (MODIS); normalized difference vegetation index (NDVI); remote sensing; Spiking neural networks (SNNs)

Indexed keywords

AGRICULTURE; CROPS; FORECASTING; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; LOW POWER ELECTRONICS; NEURAL NETWORKS; RADIOMETERS; REMOTE SENSING; TIME SERIES;

CORRELATION COEFFICIENT; DEVELOPMENT AND TESTING; METHODOLOGICAL FRAMEWORKS; MODERATE RESOLUTION IMAGING SPECTRORADIOMETER; NORMALIZED DIFFERENCE VEGETATION INDEX IMAGE; SPATIOTEMPORAL ANALYSIS; SPIKING NEURAL NETWORKS; WINTER WHEAT (TRITICUM AESTIVUM L.);

TIME SERIES ANALYSIS;

ARTIFICIAL NEURAL NETWORK; CROP YIELD; ESTIMATION METHOD; FORECASTING METHOD; IMAGE ANALYSIS; IMAGE RESOLUTION; MACHINE LEARNING; MODIS; NDVI; REMOTE SENSING; SATELLITE DATA; TIME SERIES ANALYSIS; WHEAT;

CHINA; SHANDONG;

TRITICUM AESTIVUM;

EID: 84979916681     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2016.2586602     Document Type: Article

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