Environment and Agriculture

The focus of the Environment and Agriculture Theme is to apply the new methods and algorithms for computer vision to address problems in remote sensing, support of agriculture in developing countries and environmental research. The theme was funded from the Global Challenges Strategy Fund to work closely with Argentina.

Research Projects

2018 - 2021	Co-I and Theme Lead, EPSRC New Approaches to Data Science Programme, Data science of the natural environment, £2.5m, of which FEC 10% to Atkinson.
2018	PI, NERC India-UK Water Centre (IUKWC), International workshop: Advancing drought monitoring, prediction and management capabilities, £30,000.
2015 - 2016	Co-I, STFC-administered Newton Fund, Remote sensing for sustainable intensification in China through improved farm decision-making, £500,000 of which £82,500 to Atkinson.
2015 - 2016	Co-I, Hong Kong Research Council, Spatio-temporal sub-pixel mapping for continuous land-cover/land-use monitoring, $HK1,123,400, of which $HK105,000 to Atkinson.
2015	Co-PI, Ordnance Survey, ImageLearn: Deep Learning in Ordnance Survey Datasets (includes 2% FEC to Atkinson) £62,374.

Publications

Edited volumes and conference proceedings.

• Zhang, C., I. Sargent, X. Pan, H. Li, A. Gardiner, J. Hare, P.M. Atkinson, 2019, Joint deep learning for land cover and land use classification, Remote Sensing of Environment, 221; 173-187.
• Li, H., Zhang, C., S. Zhang and P.M. Atkinson, 2018, A hybrid MCNN-SVM classifier for crop classification using polarimetric SAR, International Journal of Applied Earth Observation and Geoinformation (in press).
• Ghamisi, P., B. Rasti, N. Yokoya, Q. Wang, L. Bruzzone, F. Bovolo, M. Chi, K. Anders, R. Gloaguen, P.M. Atkinson and J.A. Benediktsson, 2018, Multisource and multitemporal data fusion in remote sensing, IEEE Geoscience and Remote Sensing Magazine(in press).
• Li, H., Zhang, C., S. Zhang and P.M. Atkinson, 2019, Full-year crop monitoring and separability assessment with fully-polarimetric L-band UAVSAR: a case study in the Sacramento Valley, California, International Journal of Applied Earth Observation and Geoinformation, 74; 45-56.
• Parsons, S., M. Weal, N. O’Grady and P.M. Atkinson, 2019, Social media in emergency management: exploring twitter use by emergency responders in the UK, International Journal of Emergency Management (in press).
• Wang, Q. and P.M. Atkinson, 2018, Spatio-temporal fusion for daily Sentinel-2 images, Remote Sensing of Environment, 204; 31-42.
• Wang, Q., W. Shi, P.M. Atkinson, 2018, Enhancing spectral unmixing by considering the point spread function effect, Spatial Statistics, 28; 271-283.
• Zhang, Y., G.M. Foody, F. Ling, X. Li, Y. Ge, Y. Du, P.M. Atkinson, 2018, Spatial-temporal fraction map fusion with multi-scale remotely sensed images, Remote Sensing of Environment, 213; 162-181.
• Zhang, C., I. Sargent, X. Pan, A. Gardiner, J. Hare, P.M. Atkinson, 2018, An object-based convolutional neural network (OCNN) for urban land use classification, Remote Sensing of Environment, 216; 57-70.
• Zhang, C., I. Sargent, X. Pan, A. Gardiner, J. Hare, P.M. Atkinson, 2018, VPRS-based regional decision fusion of CNN and MRF classifications for very fine resolution remotely sensed images, IEEE Transactions on Geoscience and Remote Sensing, 56; 4507-4521.
• Zhang, C., X. Pan, H. Li, A. Gardiner, I. Sargent, J. Hare, P.M. Atkinson, 2018, A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification, ISPRS Journal of Photogrammetry and Remote Sensing, 140; 133-144.
• Johnson, M.A., P. Caragea, W. Meiring, C. Jeganathan and P.M. Atkinson, 2018, Bayesian dynamic linear models for estimation of phenological events from remote sensing data, Journal of Agricultural, Biological and Environmental Statistics, 10.1007/s13253-018-00338-y.
• Huang, G-H., P.M. Atkinson and C.-K. Wang, 2017, Quantifying the scales of spatial variation in gravel beds using terrestrial and airborne laser scanning data, Open Geosciences, 10; 607-617.
• Gu, X., P.P. Angelov, C. Zhang and P.M. Atkinson, 2018, A massively parallel deep rule-based ensemble classifier for remote sensing scenes, IEEE Geoscience and Remote Sensing Letters, 15; 345-349.
• Jin, Y., J. Wang, Y. Chen, G.B.M. Heuvelink, P.M. Atkinson, and Y. Ge, 2018, Downscaling AMSR-2 soil moisture data with area-to-area geographically weighted regression kriging, IEEE Transactions on Geoscience and Remote Sensing, 56; 2362-2376.
• Fan, L. and P.M. Atkinson, 2017, A new multi-resolution based method for estimating surface roughness from point-clouds, ISPRS Journal of Photogrammetry and Remote Sensing, 144; 369-378