Abstract: In remote sensing, it is often challenging to acquire or collect a large dataset that is accurately labeled. This difficulty is usually due to several issues, including but not limited to the study site’s spatial area and accessibility, errors… Read More
PublicationPublication
RhizoVision Crown Accepted to Plant Phenomics!
Machine Learning and Sensing Lab Alumni, Anand Seethepalli, and collaborators recently had a paper accepted to Plant Phenomics. The article discusses an innovative platform to help collect consistent images of root crowns for phenotyping. Check it out here!
Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
Abstract: The spectral signatures of the materials contained in hyperspectral images (HI), also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an HI. Traditional spectral unmixing (SU) algorithms neglect the… Read More
SWITCHGRASS GENOTYPE CLASSIFICATION USING HYPERSPECTRAL IMAGERY
Abstract: The adoption of remote sensing techniques in plant science enables noninvasive or minimally invasive measurement, which is also time and labor saving when compared to traditional field measurements. In this thesis, a method to distinguish switchgrass genotypes with the… Read More
ANOMALY AND TARGET DETECTION IN SYNTHETIC APERTURE SONAR
Abstract: Automated anomaly and target detection are commonly used as a prescreening step within a larger target detection and target classification framework to find regions of interest for further analysis. Many anomaly and target detection algorithms in the literature have… Read More
Peanut Maturity Classification using Hyperspectral Imagery
Abstract: Seed maturity in peanut ( Arachis hypogaea L.) determines economic return to a producer because of its impact on seed weight, and critically influences seed vigor and other quality characteristics. During seed development, the inner mesocarp layer of the… Read More
Hybrid data-driven physics model-based framework for enhanced cyber-physical smart grid security
Abstract: This paper presents a hybrid data-driven physics model-based framework for real time monitoring in smart grids. As the power grid transitions to the use of smart grid technology, it’s real time monitoring becomes more vulnerable to cyber attacks like… Read More
Cross-site learning in deep learning RGB tree crown detection
Abstract: Tree detection is a fundamental task in remote sensing for forestry and ecosystem ecology applications. While many individual tree segmentation algorithms have been proposed, the development and testing of these algorithms is typically site specific, with few methods evaluated… Read More
Du Accepted to The 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019)
Congratulations to Gatorsense alumna, Xiaoxiao Du! Her paper, titled “Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels”, was recently accepted for publication with The 2019 IEEE Symposium Series on Computational Intelligence (IEEE… Read More
Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels
Abstract: Classifier fusion methods integrate complementary information from multiple classifiers or detectors and can aid remote sensing applications such as target detection and hyperspectral image analysis. The Choquet integral (CI), parameterized by fuzzy measures (FMs), has been widely used in… Read More