The lab focuses on advancing research in machine learning, computer vision and big data analytics for remote sensing applications to address the state-of-the-art technical and application challenges.
Rapid advances in sensor technology, field robotics, unmanned aerial systems (UAS), and computing power have facilitated exponential growth in remote sensing applications. Meanwhile, processing complex, multiscale, and multidimensional data from UAS, environmental sensors and climate model simulations has become increasingly difficult for both scientists and public to summarize and visualize the large amount of data for agricultural and environmental assessments with direct applications to education, training and decision-making. Data generated by thousands of images collected in a single UAS flight is almost impossible to manually analyze. There exist both numerous opportunities and challenges with broader usage of multi-sensor remote sensing data, mostly attributed to processing algorithms of ultra-high resolution imagery, translating the abundant spectral and spatial data to information useful for decision-making. Machine Learning, as a multidisciplinary field, seeks to mimic human brain system for automatic extraction, analysis, and interpreting useful information from images or a sequence of video frames.
- Sidike, P., Sagan, V., et al., (2018). dPEN: deep Progressively Expanded Network for mapping of heterogeneous agricultural landscape using WorldView-3 imagery. (in submission).
- Peterson, K.T., Sagan, V., Sidike, P., Hasenmueller, E., Sloan, J., Knouft, J. (2018). Machine learning based ensemble prediction of water quality variables with proximal remote sensing using feature-level and decision-level fusion. (under review)
- Sidike, P., Asari, V., Sagan, V. (2018). Progressively Expanded Neural Network (PEN Net) for hyperspectral image classification: a new neural network paradigm for remote sensing image analysis. (under review)
- Maimaitiyiming, M., Sagan, V., Sidike, P., Kwasniewski, M. (2018). Dual activation functions-based Extreme Learning Machine (ELM) for estimating grapevine berry yield and quality under different irrigation treatments and rootstocks conditions. (under review)
- Albalooshi, F., Sidike, P., Sagan, V., Albastaki Y., and Asari, V. (2018). Deep Belief Active Contours (DBAC) with Its Application to Oil Spill Segmentation from Remotely Sensed Aerial Imagery. (under review)