Tag: classification

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

Master’s Defenses!

Congratulations to our labmates, Hudanyun Sheng and Princess Lyons, for successful Master’s defenses!   Hudanyun conducted work on “Switchgrass Genotype Classification using Hyperspectral Imagery”, while Princess investigated  “Anomaly and Target Detection in Synthetic Aperture Sonar”. Great job, you two!

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

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

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

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

A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems

Abstract: Context information is rarely used in the object-based landcover classification. Previous models that attempted to utilize this information usually required the user to input empirical values for critical model parameters, leading to less optimal performance. Multi-view image information is… Read More