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
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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
Comparison of Hand-held WEMI Target Detection Algorithms
Abstract: Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a number of years in subsurface detection of explosive hazards. While WEMI sensors have proven effective at localizing objects exhibiting large magnetic responses, detecting objects lacking or containing very low… Read More
Overcoming Small Minirhizotron Datasets Using Transfer Learning
Abstract: Minirhizotron technology is widely used for studying the development of roots. Such systems collect visible-wavelength color imagery of plant roots in-situ by scanning an imaging system within a clear tube driven into the soil. Automated analysis of root systems… Read More
RhizoVision Crown: An Integrated Hardware and Software Platform for Root Crown Phenotyping
Abstract: Root crown phenotyping measures the top portion of crop root systems and can be used for marker-assisted breeding, genetic mapping, and understanding how roots influence soil resource acquisition. Several imaging protocols and image analysis programs exist, but they are… Read More
Root Identification in Minirhizotron Imagery with Multiple Instance Learning
Abstract: In this paper, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed. Root and soil characteristics vary from location to location, thus, supervised machine learning approaches that… Read More
Three dimensional reconstruction of plant roots via low energy X-ray computed tomography
Abstract: Plant roots are vital organs for water and nutrient uptake. The structure and spatial distribution of plant roots in the soil affects a plant’s physiological functions such as soil-based resource acquisition, yield and its ability to live under abiotic… Read More