Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent Univiversity, 2007.Thesis (Master's) -- Bilkent University, 2007.Includes bibliographical references leaves 68-76Automatic content extraction and classification of remotely sensed images have
become highly desired goals by the advances in satellite technology and computing
power. The usual choice for the level of processing image data has been pixelbased
analysis. However, spatial information is an important element to interpret
the land cover because pixels alone do not give much information about image
content.
Automatic segmentation of high-resolution remote sensing imagery is an important
problem in remote sensing applications because the resulting segmentations
can provide valuable spatial and structural information that are complementary
to pixel-based spectral information in classification. In this thesis, we first
present a method that combines structural information extracted by morphological
processing with spectral information summarized using principal components
analysis to produce precise segmentations that are also robust to noise. First,
principal components are computed from hyper-spectral data to obtain representative
bands. Then, candidate regions are extracted by applying connected components
analysis to the pixels selected according to their morphological profiles
computed using opening and closing by reconstruction with increasing structuring
element sizes. Next, these regions are represented using a tree, and the most
meaningful ones are selected by optimizing a measure that consists of two factors:
spectral homogeneity, which is calculated in terms of variances of spectral
features, and neighborhood connectivity, which is calculated using sizes of connected
components. Experiments on three data sets show that the method is able
to detect structures in the image which are more precise and more meaningful
than the structures detected by another approach that does not make strong use
of neighborhood and spectral information.Then, we introduce an unsupervised method that combines both spectral
and structural information for automatic object detection. First, a segmentation
hierarchy is constructed and candidate segments for object detection are
selected by the proposed segmentation method. Given the observation that different
structures appear more clearly in different principal components, we present
an algorithm that is based on probabilistic Latent Semantic Analysis (PLSA)
for grouping the candidate segments belonging to multiple segmentations and
multiple principal components. The segments are modeled using their spectral
content and the PLSA algorithm builds object models by learning the objectconditional
probability distributions. Labeling of a segment is done by computing
the similarity of its spectral distribution to the distribution of object models
using Kullback-Leibler divergence. Experiments on three data sets show that our
method is able to automatically detect, group, and label segments belonging to
the same object classes.
Finally, we present an approach for classification of remotely sensed imagery
using spatial information extracted from multi-scale segmentations. Different
structuring element size ranges are used to obtain multiple representations of an
image at different scales to capture different details inherently found in different
structures. Then, pixels at each scale are grouped into contiguous regions using
the proposed segmentation method. The resulting regions are modeled using the
statistical summaries of their spectral properties. These models are used to cluster
the regions by the proposed grouping method, and the cluster memberships
assigned to each region at multiple scales are used to classify the corresponding
pixels into land cover/land use categories. Final classification is done using decision
tree classifiers. Experiments with three ground truth data sets show the
effectiveness of the proposed approach over traditional techniques that do not
make strong use of region-based spatial information.Akçay, Hüseyin GökhanM.S