Abstract: A critical component for enhancing productivity and quality of food and fiber is the ability to quickly detect and monitor plant diseases in order to prevent or minimize losses to agricultural and forest products (Mahlein 2016). The earlier (prior to… Read More
PublicationPublication
Classifying California plant species temporally using airborne hyperspectral imagery
Abstract: Accurate knowledge of seasonal and inter-annual distributions of plant species is required for many research and management agendas that track ecosystem health. Airborne imaging spectroscopy data have been used successfully to map plant species, but often only in a… Read More
Plant species’ spectral emissivity and temperature using the hyperspectral thermal emission spectrometer (HyTES) sensor
Abstract: The thermal domain (TIR; 2.5–15 μm) delivers unique measurements of plant characteristics that are not possible in other parts of the electromagnetic spectrum. However, these TIR measurements have largely been restricted to laboratory leaf level or coarse spatial resolutions… Read More
The ECOSTRESS spectral library version 1.0. Remote Sensing of Environment
Abstract: In June 2018, the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission was launched to measure plant temperatures and better understand how they respond to stress. While the ECOSTRESS mission delivers imagery with ~60 m spatial resolution,… Read More
Developing Spectral Libraries Using Multiple Target Multiple Instance Adaptive Cosine/Coherence Estimator
Abstract: Traditional methods of developing spectral libraries for unmixing hyperspectral images tend to require domain knowledge of the study area and the material’s spectra. In this paper, we propose using the Multiple Target Multiple Instance Adaptive Cosine/Coherence Estimator (Multi-Target MI-ACE)… Read More
Temporal Mapping of Hyperspectral Data
Abstract: The increasing popularity of hyperspectral sensors is dramatically increasing the temporal availability of data. To date, algorithms struggle to compare hyperspectral data collected across dates due to different environmental conditions during collection. In this work, we develop a temporal… Read More
LEARNING MULTIPLE TARGET CONCEPTS FROM UNCERTAIN, AMBIGUOUS DATA USING THE ADAPTIVE COSINE ESTIMATOR AND SPECTRAL MATCH FILTER
Abstract: The Multiple Instance Adaptive Cosine Estimator and the Multiple Instance Subspace Match Filter are algorithms used in target detection, where a target class of interest is attempted to be detected amongst a non-target, background class. These algorithms learn a… Read More
Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks
Abstract: Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns in true color, or… Read More
Investigation of Initialization Strategies for the Multiple Instance Adaptive Cosine Estimator
Abstract: Sensors which use electromagnetic induction (EMI) to excite a response in conducting bodies have long been investigated for subsurface explosive hazard detection. In particular, EMI sensors have been used to discriminate between different types of objects, and to detect… Read More
Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation
Abstract: Synthetic aperture sonar (SAS) imagery can generate high resolution images of the seafloor. Thus, segmentation algorithms can be used to partition the images into different seafloor environments. In this paper, we compare two possibilistic segmentation approaches. Possibilistic approaches allow… Read More