Abstract: Semi-supervised learning attempts to take advantage of the large amount of unlabeled information present in many datasets. However, unlabeled data will often contain samples outside the classes of interest. Many existing semi-supervised learning methods do not address this issue.… Read More
PublicationPublication
Capturing long-tailed individual tree diversity using an airborne imaging and a multi-temporal hierarchical model
Abstract: Measuring forest biodiversity using terrestrial surveys is expensive and can only capture common species abundance in large heterogeneous landscapes. In contrast, combining airborne imagery with computer vision can generate individual tree data at the scales of hundreds of thousands… Read More
Discriminative Feature Learning with Imprecise, Uncertain, and Ambiguous Data
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
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