This conference presentation was prepared for SPIE Sensors + Imaging, 2024.

Automated methods for extracting water features from multispectral and hyperspectral imagery. Automated or AI based techniques can be applied to scientifically detect subsurface bottom types and water column properties using fast computational algorithms and image processing techniques based upon synthetic channels created from multiband sensing systems. In this research a review of image analysis techniques is presented and described with the context of modern real time methods which are called artificial techniques. One basis and description of these techniques relies upon generating synthetic channels using wavelet imaging techniques in combination with multiple wavelength contrast algorithms. In this paper and presentation techniques are demonstrated using multispectral-hyperspectral mages flown over Space Coast Florida waters. The results demonstrate the value of modern image analysis approaches to examine environmental and ecologically relevant diversity indices useful for characterizing the quality of marine habitats.

Sun glint in satellite imagery of the water surface contaminates the upwelling signal received by a detector. Many models exist that attempt to correct for this wave facet effect and phenomena. In this work a model for sun glint correction is creating using the comparison of image transects between two nearly simultaneously collected images of the same area, although with differing sensor geometry. One image utilized in this research is almost entirely glint free while the other is contaminated by water wave facet glint. Although many models for removing sun glint exist based on various techniques, none are completely accurate, and there is always a need to improve our understanding of this phenomena and to decontaminate the sun glint pixels. The model developed in this research is based on the statistical properties of the images related to azimuth angles, fetch distances, wind speed and direction, and other factors in attempt to test a new mathematical model for sun glint removal.

This conference presentation was prepared for SPIE Sensors + Imaging, 2024.

This conference presentation was prepared for SPIE Sensors + Imaging, 2024.

This conference presentation was prepared for SPIE Sensors + Imaging, 2024.

This conference presentation was prepared for SPIE Sensors + Imaging, 2024.

This conference presentation was prepared for SPIE Sensors + Imaging, 2024.

Potato (Solanum tuberosum L.) is an important staple food, adapted to a wide range of environments. The use of remoting sensing can help to understand the relationship between canopy development and crop performance, and to improve yield in local areas. One of the preliminary results shows that the green area fell the least at the end of the season, obtained the highest yields, among them a variety with a short development cycle.