Deep Learning Algorithms for Satellite Imagery
This book provides key insights into the world of Deep Learning pertaining to satellite image understanding. It highlights what differentiates satellite image datasets from other natural or synthetic images and how to tackle problems specific to these imagery data. From answering questions like how to select optimal training data to weekly supervised and unsupervised learning and how to tackle loosely labeled data, it is a valuable source of information for anyone interested in understanding the theory behind satellite image analytics and provides key insights on the application of various state-of-the-art Deep Learning algorithms on these datasets.
| Publisher Name | CRC Press |
|---|---|
| Author Name | Hagendorf, Col |
| Format | Audio |
| Bisac Subject Major | COM |
| Language | NG |
| Isbn 10 | 1138493651 |
| Isbn 13 | 9781138493650 |
| Target Age Group | min:NA, max:NA |
| Dimensions | 00.00" H x 00.00" L x 00.00" W |
| Page Count | 225 |
Saikat Basu is working as a research scientist in the Facebook Maps team in Boston. He received his PhD in Computer Science from Louisiana State University in 2016. He received his Bachelor of Technology in Computer Science and Engineering from National Institute of Technology, Durgapur, India in 2011. During his doctoral program, he has been doing research on the analysis of various kinds of imagery data using Computer Vision and Deep Learning algorithms for the analysis of satellite imagery data. During his PhD, he has worked as a research associate at NASA Ames Research Center, Moffett Field, California and an intern at the Facebook Maps team in Boston.
