In this talk Ni-Bin Chang, director at the Stormwater Management Academy and professor in Environmental Systems Engineering at the University of Central Florida, focuses on the challenges and forefronts in optical remote sensing for earth observations using case studies of Lake Erie and Lake Nicaragua.
Coffee and light refreshments will be provided.
Key topics covered
Contemporary challenges in optical remote sensing for earth observations include:
- Complexity in data/image fusion or merging for higher spatial and temporal resolution
- Feature extraction of different environmental quality images utilizing machine learning techniques
- Cloud contamination, image reconstruction and cross-mission data merging with the aid of machine learning
- The design of integrated decision support systems
The recent regime shift of machine learning techniques from regular learning to deep learning to fast learning has triggered a renewed interest in remote sensing image processing. This presentation will focus on these forefronts and challenges using case studies of Lake Erie and Lake Nicaragua based on both multispectral and hyperspectral remote sensing imageries.
Monitoring Chlorophyll-a, total phosphorus and total nitrogen concentrations as well as the algal toxin – mycrocystins – will be discussed for lake eutrophication assessment. The latest advances in Integrated Data Fusion and Mining (IDFM) by fusing images collected from two satellites will be introduced. To recover the missing information caused by cloud contamination, SMart Information Reconstruction (SMIR) and Spectral Information Adaptation and Synthesis Scheme (SIASS) will be presented for merging cross-mission consistent ocean color reflectance observations. A decision support system is described for Cross-mission Data Merging with Image Reconstruction and Mining (CDMIM) with application to images of Lake Nicaragua. Future topics related to the next regime shift will be discussed in the conclusions.
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