111 research outputs found
Novel pattern recognition methods for classification and detection in remote sensing and power generation applications
Novel pattern recognition methods for classification and detection in remote sensing and power generation application
Hyper-Spectral Image Analysis with Partially-Latent Regression and Spatial Markov Dependencies
Hyper-spectral data can be analyzed to recover physical properties at large
planetary scales. This involves resolving inverse problems which can be
addressed within machine learning, with the advantage that, once a relationship
between physical parameters and spectra has been established in a data-driven
fashion, the learned relationship can be used to estimate physical parameters
for new hyper-spectral observations. Within this framework, we propose a
spatially-constrained and partially-latent regression method which maps
high-dimensional inputs (hyper-spectral images) onto low-dimensional responses
(physical parameters such as the local chemical composition of the soil). The
proposed regression model comprises two key features. Firstly, it combines a
Gaussian mixture of locally-linear mappings (GLLiM) with a partially-latent
response model. While the former makes high-dimensional regression tractable,
the latter enables to deal with physical parameters that cannot be observed or,
more generally, with data contaminated by experimental artifacts that cannot be
explained with noise models. Secondly, spatial constraints are introduced in
the model through a Markov random field (MRF) prior which provides a spatial
structure to the Gaussian-mixture hidden variables. Experiments conducted on a
database composed of remotely sensed observations collected from the Mars
planet by the Mars Express orbiter demonstrate the effectiveness of the
proposed model.Comment: 12 pages, 4 figures, 3 table
Automatic detection of geospatial objects using multiple hierarchical segmentations
Cataloged from PDF version of article.The object-based analysis of remotely sensed imagery
provides valuable spatial and structural information that
is complementary to pixel-based spectral information in classi-
fication. In this paper, we present novel methods for automatic
object detection in high-resolution images by combining spectral
information with structural information exploited by using
image segmentation. The proposed segmentation algorithm uses
morphological operations applied to individual spectral bands
using structuring elements in increasing sizes. These operations
produce a set of connected components forming a hierarchy of
segments for each band. A generic algorithm is designed to select
meaningful segments that maximize a measure consisting
of spectral homogeneity and neighborhood connectivity. Given
the observation that different structures appear more clearly at
different scales in different spectral bands, we describe a new
algorithm for unsupervised grouping of candidate segments belonging
to multiple hierarchical segmentations to find coherent
sets of segments that correspond to actual objects. The segments
are modeled by using their spectral and textural content, and
the grouping problem is solved by using the probabilistic latent
semantic analysis algorithm that builds object models by learning
the object-conditional probability distributions. The automatic
labeling of a segment is done by computing the similarity of its
feature distribution to the distribution of the learned object models
using the Kullback–Leibler divergence. The performances of the
unsupervised segmentation and object detection algorithms are
evaluated qualitatively and quantitatively using three different
data sets with comparative experiments, and the results show that
the proposed methods are able to automatically detect, group, and
label segments belonging to the same object classes
Automated Remote Sensing Image Interpretation with Limited Labeled Training Data
Automated remote sensing image interpretation has been investigated for more than a decade. In early years, most work was based on the assumption that there are sufficient labeled samples to be used for training. However, ground-truth collection is a very tedious and time-consuming task and sometimes very expensive, especially in the field of remote sensing that usually relies on field surveys to collect ground truth. In recent years, as the development of advanced machine learning techniques, remote sensing image interpretation with limited ground-truth has caught the attention of researchers in the fields of both remote sensing and computer science.
Three approaches that focus on different aspects of the interpretation process, i.e., feature extraction, classification, and segmentation, are proposed to deal with the limited ground truth problem. First, feature extraction techniques, which usually serve as a pre-processing step for remote sensing image classification are explored. Instead of only focusing on feature extraction, a joint feature extraction and classification framework is proposed based on ensemble local manifold learning. Second, classifiers in the case of limited labeled training data are investigated, and an enhanced ensemble learning method that outperforms state-of-the-art classification methods is proposed. Third, image segmentation techniques are investigated, with the aid of unlabeled samples and spatial information. A semi-supervised self-training method is proposed, which is capable of expanding the number of training samples by its own and hence improving classification performance iteratively. Experiments show that the proposed approaches outperform state-of-the-art techniques in terms of classification accuracy on benchmark remote sensing datasets.4 month
A markovian approach to unsupervised change detection with multiresolution and multimodality SAR data
In the framework of synthetic aperture radar (SAR) systems, current satellite missions make it possible to acquire images at very high and multiple spatial resolutions with short revisit times. This scenario conveys a remarkable potential in applications to, for instance, environmental monitoring and natural disaster recovery. In this context, data fusion and change detection methodologies play major roles. This paper proposes an unsupervised change detection algorithmfor the challenging case of multimodal SAR data collected by sensors operating atmultiple spatial resolutions. The method is based on Markovian probabilistic graphical models, graph cuts, linear mixtures, generalized Gaussian distributions, Gram-Charlier approximations, maximum likelihood and minimum mean squared error estimation. It benefits from the SAR images acquired at multiple spatial resolutions and with possibly different modalities on the considered acquisition times to generate an output change map at the finest observed resolution. This is accomplished by modeling the statistics of the data at the various spatial scales through appropriate generalized Gaussian distributions and by iteratively estimating a set of virtual images that are defined on the pixel grid at the finest resolution and would be collected if all the sensors could work at that resolution. A Markov random field framework is adopted to address the detection problem by defining an appropriate multimodal energy function that is minimized using graph cuts
Hyperspectral image representation and processing with binary partition trees
The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, under the title Hyperspectral image representation and Processing with Binary Partition Trees, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the Binary Partition Tree succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Based on region-merging techniques, the construction of BPT is investigated in this work by studying hyperspectral region models and the associated similarity metrics. As a matter of fact, the very high dimensionality and the complexity of the data require the definition of specific region models and similarity measures. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. Accordingly, some pruning techniques are proposed and discussed
according to different applications. This Ph.D is focused in particular on segmentation, object detection and classification of hyperspectral imagery. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representatio
Learnable Reconstruction Methods from RGB Images to Hyperspectral Imaging: A Survey
Hyperspectral imaging enables versatile applications due to its competence in
capturing abundant spatial and spectral information, which are crucial for
identifying substances. However, the devices for acquiring hyperspectral images
are expensive and complicated. Therefore, many alternative spectral imaging
methods have been proposed by directly reconstructing the hyperspectral
information from lower-cost, more available RGB images. We present a thorough
investigation of these state-of-the-art spectral reconstruction methods from
the widespread RGB images. A systematic study and comparison of more than 25
methods has revealed that most of the data-driven deep learning methods are
superior to prior-based methods in terms of reconstruction accuracy and quality
despite lower speeds. This comprehensive review can serve as a fruitful
reference source for peer researchers, thus further inspiring future
development directions in related domains
Hierarchical segmentation, object detection and classification in remotely sensed images
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
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