Abstract: Target detection is a paramount task in remote sensing which aims to detect points of interest from a set of data. A crucial aspect attributed to the success of target detection methods is the representation of the data which… Read More
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Spatial and Texture Analysis of Root System distribution with Earth mover’s Distance (STARSEED)
Abstract: Root system architectures are complex and challenging to characterize effectively for agronomic and ecological discovery. We propose a new method, Spatial and Texture Analysis of Root System distribution with Earth mover’s Distance (STARSEED), for comparing root system distributions that… Read More
Weakly Supervised Image Segmentation with Multiple Instance Learning Neural Network
Abstract: In my dissertation, we present multiple instance learning U-net (MILUnet) algorithm and multiple instance learning class activation map (MILCAM) algorithm for weakly supervised semantic segmentation. Both the MILUnet and MILCAM algorithms requires only training images paired with image-level label… Read More
Injecting Domain Knowledge Into Deep Neural Networks for Tree Crown Delineation
Abstract: Automated individual tree crown (ITC) delineation plays an important role in forest remote sensing. Accurate ITC delineation benefits biomass estimation, allometry estimation, and species classification among other forest-related tasks, all of which are used to monitor forest health and… Read More
Connecting the Past and the Present : Histogram Layers for Texture Analysis
Abstract: Feature engineering often plays a vital role in the fields of computer vision and machine learning. A few common examples of engineered features include histogram of oriented gradients (HOG) , local binary patterns (LBP), and edge histogram descriptors (EHD).… Read More
Domain Translation and Image Registration for Multi-Look Synthetic Aperture Sonar Scene Understanding
Abstract: The domain of multi-look scene understanding problems includes scenarios where multiple passes over the same area have occurred and combining information from them is desired. For example, in remotely sensed SAS surveys, the same location on the seafloor is… Read More
Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data
Abstract: Heterogeneous data fusion can enhance the robustness and accuracy of an algorithm on a given task. However, due to the difference in various modalities, aligning the sensors and embedding their information into discriminative and compact representations is challenging. In… Read More
Continental-scale hyperspectral tree species classification in the United States National Ecological Observatory Network
Abstract: Advances in remote sensing imagery and machine learning applications unlock the potential for developing algorithms for species classification at the level of individual tree crowns at unprecedented scales. However, most approaches to date focus on site-specific applications and a… Read More
Connecting The Past And The Present: Histogram Layers For Texture Analysis
Abstract: Feature engineering often plays a vital role in the fields of computer vision and machine learning. A few common examples of engineered features include histogram of oriented gradients (HOG) (Dalal and Triggs, 2005), local binary patterns (LBP) (Ojala et… Read More
Bag-level Classification Network for Infrared Target Detection
Abstract: Aided target detection in infrared data has proven an important area of investigation for both military and civilian applications. While target detection at the object or pixel-level has been explored extensively, existing approaches require precisely-annotated data which is often… Read More