Image segmentation persists as a major statistical problem, with the volume
and complexity of data expanding alongside new technologies. Land cover
classification, one of the most studied problems in Remote Sensing, provides an
important example of image segmentation whose needs transcend the choice of
a particular classification method. That is, the challenges associated with
land cover classification pervade the analysis process from data
pre-processing to estimation of a final land cover map. Many of the same
challenges also plague the task of land cover change detection.
Multispectral, multitemporal data with inherent spatial relationships have
hardly received adequate treatment due to the large size of the data and
the presence of missing values.
In this work we propose a novel, concerted application of methods which
provide a unified way to estimate model parameters, impute missing data,
reduce dimensionality, classify land cover, and detect land cover changes.
This comprehensive analysis adopts a Bayesian approach which incorporates
prior knowledge to improve the interpretability, efficiency, and versatility
of land cover classification and change detection. We explore a parsimonious,
parametric model that allows for a natural application of principal components
analysis to isolate important spectral characteristics while preserving
temporal information. Moreover, it allows us to impute missing data and
estimate parameters via expectation-maximization (EM). A significant byproduct
of our framework includes a suite of training data assessment tools. To
classify land cover, we employ a spanning tree approximation to a lattice
Potts prior to incorporate spatial relationships in a judicious way and more
efficiently access the posterior distribution of pixel labels. We then achieve
exact inference of the labels via the centroid estimator. To detect land
cover changes, we develop a new EM algorithm based on the same parametric model.
We perform simulation studies to validate our models and methods, and
conduct an extensive continental scale case study using MODIS data. The results
show that we successfully classify land cover and recover the spatial patterns
present in large scale data. Application of our change point method
to an area in the Amazon successfully identifies the progression of
deforestation through portions of the region
