2 research outputs found
Integration of Spatial and Spectral Information for Hyperspectral Image Classification
Hyperspectral imaging has become a powerful tool in biomedical and agriculture fields in the recent years and the interest amongst researchers has increased immensely. Hyperspectral imaging combines conventional imaging and spectroscopy to acquire both spatial and spectral information from an object. Consequently, a hyperspectral image data contains not only spectral information of objects, but also the spatial arrangement of objects. Information captured in neighboring locations may provide useful supplementary knowledge for analysis. Therefore, this dissertation investigates the integration of information from both the spectral and spatial domains to enhance hyperspectral image classification performance.
The major impediment to the combined spatial and spectral approach is that most spatial methods were only developed for single image band. Based on the traditional singleimage based local Geary measure, this dissertation successfully proposes a Multidimensional Local Spatial Autocorrelation (MLSA) for hyperspectral image data. Based on the proposed spatial measure, this research work develops a collaborative band selection strategy that combines both the spectral separability measure (divergence) and spatial homogeneity measure (MLSA) for hyperspectral band selection task. In order to calculate the divergence more efficiently, a set of recursive equations for the calculation of divergence with an additional band is derived to overcome the computational restrictions.
Moreover, this dissertation proposes a collaborative classification method which integrates the spectral distance and spatial autocorrelation during the decision-making process. Therefore, this method fully utilizes the spatial-spectral relationships inherent in the data, and thus improves the classification performance.
In addition, the usefulness of the proposed band selection and classification method is evaluated with four case studies. The case studies include detection and identification of tumor on poultry carcasses, fecal on apple surface, cancer on mouse skin and crop in agricultural filed using hyperspectral imagery. Through the case studies, the performances of the proposed methods are assessed. It clearly shows the necessity and efficiency of integrating spatial information for hyperspectral image processing
High dimensional land cover inference using remotely sensed MODIS data
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