A major issue in land cover mapping is classifier selection. Here we investigated classifier
performance under different sample sizes, reference class distribution, and scene …

A new algorithm for exploiting the nonlinear structure of hyperspectral imagery is developed
and compared against the de facto standard of linear mixing. This new approach seeks a …

The spectral features in hyperspectral imagery (HSI) contain significant structure that, if
properly characterized, could enable more efficient data acquisition and improved data …

In recent publications, we have presented a data-driven approach to representing the
nonlinear structure of hyperspectral imagery using manifold coordinates. The approach …

Hyperspectral images have emerged as a new and promising remote sensing data that can
overcome the limitations of existing optical image data. This study was designed to provide a …

Unlike conventional unsupervised classification methods, such as K‐means and ISODATA,
which are based on partitional clustering techniques, the methodology proposed in this work …

In contrast to stable inland systems, coastal landscape positions are dynamic, changing as
shorelines migrate and storms alter topography. We define landscape position by distance …

A bstract To interpret broad-scale erosion and accretion patterns and the expansion and
contraction of shrub thickets in response to sea level rise for a coastal barrier system, we …

Salt marsh vegetation density varies considerably on short spatial scales, complicating
attempts to evaluate plant characteristics using airborne remote sensing approaches. In this …

A credit assignment approach to decision-based classifier fusion is developed and applied
to the problem of land-cover classification from multiseason airborne hyperspectral imagery …