Tag: uncertain/imprecise labels

ADDRESSING ANNOTATION IMPRECISION FOR TREE CROWN DELINEATION USING THE RANDCROWNS INDEX

Abstract: Supervised methods for object delineation in remote sensing require labeled ground-truth data. Gathering sufficient high quality ground-truth data is difficult, especially when the targets are of irregular shape or difficult to distinguish from the background or neighboring objects. Tree… Read More

CLASSIFICATION WITH MULTI-IMPRECISE LABELS

Abstract: Imprecise labels or label uncertainty are common problems in many real supervised and semi-supervised learning problems. However, most of the state-of-the-art supervised learning methods in the literature rely on accurate labels. Accurate labels are often either expensive, time-consuming, or… Read More

WEAKLY-LABELED RAND INDEX ACCEPTED TO IGARSS!

Congratulations to our labmates: Dylan Stewart, Anna Hampton, Alina Zare, Jeff Dale and James Keller!  Their paper, “The Weakly-Labeled Rand Index” was recently accepted to the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). In their paper, the authors introduce… Read More

THE WEAKLY-LABELED RAND INDEX

Abstract: Synthetic Aperture Sonar (SAS) surveys produce imagery with large regions of transition between seabed types. Due to these regions, it is difficult to label and segment the imagery and, furthermore, challenging to score the image segmentations appropriately. While there… Read More

ROOT IDENTIFICATION WITH MULTIPLE INSTANCE LEARNING PUBLISHED IN MACHINE VISION AND APPLICATIONS!

Congratulations to our labmates and collaborators Guohao Yu, Alina Zare, Hudanyun Sheng, Roser Matamala, Joel Reyes-Cabrera, Felix Fritschi and Thomas Juenger! Their paper, “Root Identification in Minirhizotron Imagery with Multiple Instance Learning”, was recently published in Machine Vision and Applications!… Read More

ROOT IDENTIFICATION WITH MULTIPLE INSTANCE LEARNING ACCEPTED TO MACHINE VISION AND APPLICATIONS!

Congratulations to our labmates Guohao Yu, Alina Zare and Hudanyun Sheng, as well as collaborators, Roser Matamala, Joel Reyes-Cabrera, Felix Fritschi and Thomas Juenger! Their paper, “Root Identification in Minirhizotron Imagery with Multiple Instance Learning”, was recently accepted to Machine… Read More

Multi-Target Multiple Instance Learning for Hyperspectral Target Detection

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

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

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