Overview
Remote sensing applications involve a broad set of technical issues, from handling large data sets, to multichannel image processing. MathWorks tools help you manage and manipulate multispectral and hyperspectral data, isolate remotely-sensed objects, and calculate derived data such as vegetation indices or identifying of hazards on lunar surface. While traditional techniques for satellite or aerial images tend to be brittle, deep learning based techniques could be more robust and accurate.
Whether you are new to deep learning or an expert, MATLAB® can help you while you work on these complex applications. In this webinar we will talk about
- Automating preparation and labeling of training data
- Interoperability with open source deep learning frameworks
- Training deep neural networks for vision applications
- Tuning hyper-parameters to accelerate training time and increase network accuracy
- Generating multi-target code for NVIDIA®, Intel,® and ARM®
About the Presenter
Rishu Gupta is a senior application engineer at MathWorks. He primarily focuses on image processing, computer vision and deep learning applications. Rishu has an experience of over 9 years working on applications related to visual contents. He previously worked as a scientist at LG soft India, research and development unit. He has published and reviewed papers in multiple peer-reviewed conferences and journals. Rishu holds bachelor’s degree in electronics and communication engineering from BIET Jhansi. Master’s in visual contents from Dongseo University, South Korea, working on the application of computer vision. PhD in electrical engineering from University Technology Petronas, Malaysia with focus on Biomedical Image Processing using ultrasound images.
