Enhancing Remote Sensing for Agriculture Using Small Unmanned Aerial Systems: San Diego, CA, as a Test Case

The development of small Global Positioning System (GPS) antennas and microprocessors has propelled the advancement of affordable Small Unmanned Aerial Systems (SUASs), which will dramatically expand the remote sensing field, making timely, high-resolution imagery readily available. The low cost and simple operation of SUASs makes them an attractive option for agriculture. Flying a SUAS 400 ft above ground level (AGL) in a flight path that allows for significant image overlap can yield sub- 5cm resolution imagery, which in turn can be mosaicked and used for multispectral imagery analysis. With results rivaling the most advanced commercial imaging sensors, SUASs can be used to identify stressed vegetation and aid in decision making that ultimately leads to more efficient farming practices and consistent yields. Furthermore, minimal operating costs promote reduced revisit times and enable persistent collection to monitor changes over time

An Integrated Enhancement Solution for 24-hour Colorful Imaging

The current industry practice for 24-hour outdoor imaging is to use a silicon camera supplemented with near-infrared (NIR) illumination. This will result in color images with poor contrast at daytime and absence of chrominance at nighttime. For this dilemma, all existing solutions try to capture RGB and NIR images separately. However, they need additional hardware support and suffer from various drawbacks, including short service life, high price, specific usage scenario, etc. In this paper, we propose a novel and integrated enhancement solution that produces clear color images, whether at abundant sunlight daytime or extremely low-light nighttime. Our key idea is to separate the VIS and NIR information from mixed signals, and enhance the VIS signal adaptively with the NIR signal as assistance. To this end, we build an optical system to collect a new VIS-NIR-MIX dataset and present a physically meaningful image processing algorithm based on CNN. Extensive experiments show outstanding results, which demonstrate the effectiveness of our solution.Comment: AAAI 2020 (Oral

Heterogeneous Multi-Sensor Camera

Today's security cameras typically capture either color images based on visible light or grayscale images based on both visible and near-infrared (NIR) light. This means that the performance is limited in situations where it is unclear which kind of image to capture. At dusk, for example, the visible light may not be sufficient to capture good-quality color images. On the other hand, capturing grayscale images at dusk means that useful color information is disregarded. Thus, there is a need for a hybrid-mode camera which can utilize the best of both the color and the grayscale images. In order to build a prototype camera which can achieve just this, a dichroic beam-splitter was placed in between the lens and the image sensor of a conventional camera. This way, the incident light is split up into its visible and NIR components which then can be detected separately by two image sensors. Different approaches on how to merge color and grayscale images into one superior image were investigated, where the basic idea was to extract the overall intensity from the color image and partly replace it with the intensity of the grayscale image. The prototype camera developed proved to outperform conventional security cameras in certain situations, such as in low light conditions as well as when the illumination is highly varying. While more development is needed, the technique looks promising overall and should offer new imaging capabilities to the security camera market