| An object-based convolutional neural network (OCNN) for urban land use classification C Zhang, I Sargent, X Pan, H Li, A Gardiner, J Hare, PM Atkinson Remote sensing of environment 216, 57-70, 2018 | 336 | 2018 |
| A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification C Zhang, X Pan, H Li, A Gardiner, I Sargent, J Hare, PM Atkinson ISPRS Journal of Photogrammetry and Remote Sensing 140, 133-144, 2018 | 317 | 2018 |
| Joint Deep Learning for land cover and land use classification C Zhang, I Sargent, X Pan, H Li, A Gardiner, J Hare, PM Atkinson Remote sensing of environment 221, 173-187, 2019 | 300 | 2019 |
| VPRS-based regional decision fusion of CNN and MRF classifications for very fine resolution remotely sensed images C Zhang, I Sargent, X Pan, A Gardiner, J Hare, PM Atkinson IEEE Transactions on Geoscience and Remote Sensing 56 (8), 4507-4521, 2018 | 77 | 2018 |
| Scale Sequence Joint Deep Learning (SS-JDL) for land use and land cover classification C Zhang, PA Harrison, X Pan, H Li, I Sargent, PM Atkinson Remote Sensing of Environment 237, 111593, 2020 | 74 | 2020 |
| Quality assessment of 3D building data D Akca, M Freeman, I Sargent, A Gruen The Photogrammetric Record 25 (132), 339-355, 2010 | 68 | 2010 |
| Data quality in 3D: Gauging quality measures from users’ requirements I Sargent, J Harding, M Freeman International Archives of Photogrammetry, Remote Sensing and Spatial …, 2007 | 33 | 2007 |
| Exploring the geostatistical method for estimating the signal-to-noise ratio of images PM Atkinson, IM Sargent, GM Foody, J Williams Photogrammetric Engineering & Remote Sensing 73 (7), 841-850, 2007 | 21 | 2007 |
| Interpreting image-based methods for estimating the signal-to-noise ratio PM Atkinson, IM Sargent, GM Foody, J Williams International Journal of Remote Sensing 26 (22), 5099-5115, 2005 | 19 | 2005 |
| Thematic labelling from hyperspectral remotely sensed imagery: trade-offs in image properties GM Foody, IMJ Sargent, PM Atkinson, JW Williams International Journal of Remote Sensing 25 (12), 2337-2363, 2004 | 13 | 2004 |
| Moving towards 3D: from a National Mapping Agency perspective D Capstick, G Heathcote, J Horgan, I Sargent The Cartographic Journal 44 (3), 233-238, 2007 | 9 | 2007 |
| Quality assessment of 3D building data by 3D surface matching D Akca, M Freeman, A Gruen, I Sargent The International Archives of the Photogr ammetry, Remote Sensing and …, 2008 | 8 | 2008 |
| Fast quality control of 3D city models D Akca, A Gruen, M Freeman, I Sargent The International LIDAR Mapping Forum, New Orleans, Louisiana, USA, January …, 2009 | 7 | 2009 |
| SAR imagery for flood monitoring and assessment P Aplin, PM Atkinson, AR Tatnall, ME Cutler, I Sargent Remote Sensing Society, 1999 | 7 | 1999 |
| Joint deep learning for land cover and land use classification I Sargent, C Zhang, PM Atkinson US Patent 10,984,532, 2021 | 6 | 2021 |
| The building blocks of user-focused 3D city models I Sargent, D Holland, J Harding ISPRS International Journal of Geo-Information 4 (4), 2890-2904, 2015 | 6 | 2015 |
| Quantifying and visualising the uncertainty in 3D building model walls using terrestrial lidar data M Freeman, I Sargent Proc. of the Remote Sensing and Photogrammetry Society Conference, 15-17.9, 2008 | 6 | 2008 |
| Object-based convolutional neural network for land use classification I Sargent, C Zhang, PM Atkinson US Patent 10,922,589, 2021 | 5 | 2021 |
| Opportunities for machine learning and artificial intelligence in national mapping agencies: enhancing ordnance survey workflow J Murray, I Sargent, D Holland, A Gardiner, S Coupland, J Hare, C Zhang, ... International Archives of the Photogrammetry Remote Sensing and Spatial …, 2020 | 4 | 2020 |
| Land cover classification from hyperspectral remotely sensed data: an investigation of spectral, spatial and noise issues GM Foody, IMJ Sargent, PM Atkinson, JW Williams IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings …, 2001 | 4 | 2001 |
Peter M AtkinsonDistinguished Professor and Executive Dean, Faculty of Science and Technology, Lancaster UniversityE-mailová adresa ověřena na: lancaster.ac.uk
