685 research outputs found
Pattern Recognition in High-Dimensional Data
Vast amounts of data are produced all the time. Yet this data does not easily equate to useful information: extracting information from large amounts of high dimensional data is nontrivial. People are simply drowning in data. A recent and growing source of high-dimensional data is hyperspectral imaging. Hyperspectral images allow for massive amounts of spectral information to be contained in a single image. In this thesis, a robust supervised machine learning algorithm is developed to efficiently perform binary object classification on hyperspectral image data by making use of the geometry of Grassmann manifolds. This algorithm can consistently distinguish between a large range of even very similar materials, returning very accurate classification results with very little training data. When distinguishing between dissimilar locations like crop fields and forests, this algorithm consistently classifies more than 95 percent of points correctly. On more similar materials, more than 80 percent of points are classified correctly. This algorithm will allow for very accurate information to be extracted from these large and complicated hyperspectral images
More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification
Classification and identification of the materials lying over or beneath the
Earth's surface have long been a fundamental but challenging research topic in
geoscience and remote sensing (RS) and have garnered a growing concern owing to
the recent advancements of deep learning techniques. Although deep networks
have been successfully applied in single-modality-dominated classification
tasks, yet their performance inevitably meets the bottleneck in complex scenes
that need to be finely classified, due to the limitation of information
diversity. In this work, we provide a baseline solution to the aforementioned
difficulty by developing a general multimodal deep learning (MDL) framework. In
particular, we also investigate a special case of multi-modality learning (MML)
-- cross-modality learning (CML) that exists widely in RS image classification
applications. By focusing on "what", "where", and "how" to fuse, we show
different fusion strategies as well as how to train deep networks and build the
network architecture. Specifically, five fusion architectures are introduced
and developed, further being unified in our MDL framework. More significantly,
our framework is not only limited to pixel-wise classification tasks but also
applicable to spatial information modeling with convolutional neural networks
(CNNs). To validate the effectiveness and superiority of the MDL framework,
extensive experiments related to the settings of MML and CML are conducted on
two different multimodal RS datasets. Furthermore, the codes and datasets will
be available at https://github.com/danfenghong/IEEE_TGRS_MDL-RS, contributing
to the RS community
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Hyperspectral imaging, also known as image spectrometry, is a landmark
technique in geoscience and remote sensing (RS). In the past decade, enormous
efforts have been made to process and analyze these hyperspectral (HS) products
mainly by means of seasoned experts. However, with the ever-growing volume of
data, the bulk of costs in manpower and material resources poses new challenges
on reducing the burden of manual labor and improving efficiency. For this
reason, it is, therefore, urgent to develop more intelligent and automatic
approaches for various HS RS applications. Machine learning (ML) tools with
convex optimization have successfully undertaken the tasks of numerous
artificial intelligence (AI)-related applications. However, their ability in
handling complex practical problems remains limited, particularly for HS data,
due to the effects of various spectral variabilities in the process of HS
imaging and the complexity and redundancy of higher dimensional HS signals.
Compared to the convex models, non-convex modeling, which is capable of
characterizing more complex real scenes and providing the model
interpretability technically and theoretically, has been proven to be a
feasible solution to reduce the gap between challenging HS vision tasks and
currently advanced intelligent data processing models
Investigation of feature extraction algorithms and techniques for hyperspectral images.
Doctor of Philosophy (Computer Engineering). University of KwaZulu-Natal. Durban, 2017.Hyperspectral images (HSIs) are remote-sensed images that are characterized
by very high spatial and spectral dimensions and nd applications, for example,
in land cover classi cation, urban planning and management, security and food
processing. Unlike conventional three bands RGB images, their high
dimensional data space creates a challenge for traditional image processing
techniques which are usually based on the assumption that there exists
su cient training samples in order to increase the likelihood of high
classi cation accuracy. However, the high cost and di culty of obtaining
ground truth of hyperspectral data sets makes this assumption unrealistic and
necessitates the introduction of alternative methods for their processing.
Several techniques have been developed in the exploration of the rich spectral
and spatial information in HSIs. Speci cally, feature extraction (FE)
techniques are introduced in the processing of HSIs as a necessary step before
classi cation. They are aimed at transforming the high dimensional data of the
HSI into one of a lower dimension while retaining as much spatial and/or
spectral information as possible. In this research, we develop semi-supervised
FE techniques which combine features of supervised and unsupervised
techniques into a single framework for the processing of HSIs. Firstly, we
developed a feature extraction algorithm known as Semi-Supervised Linear
Embedding (SSLE) for the extraction of features in HSI. The algorithm
combines supervised Linear Discriminant Analysis (LDA) and unsupervised
Local Linear Embedding (LLE) to enhance class discrimination while also
preserving the properties of classes of interest. The technique was developed
based on the fact that LDA extracts features from HSIs by discriminating
between classes of interest and it can only extract C 1 features provided there
are C classes in the image by extracting features that are equivalent to the
number of classes in the HSI. Experiments show that the SSLE algorithm
overcomes the limitation of LDA and extracts features that are equivalent to
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the number of classes in HSIs. Secondly, a graphical manifold dimension
reduction (DR) algorithm known as Graph Clustered Discriminant Analysis
(GCDA) is developed. The algorithm is developed to dynamically select labeled
samples from the pool of available unlabeled samples in order to complement
the few available label samples in HSIs. The selection is achieved by entwining
K-means clustering with a semi-supervised manifold discriminant analysis.
Using two HSI data sets, experimental results show that GCDA extracts
features that are equivalent to the number of classes with high classi cation
accuracy when compared with other state-of-the-art techniques. Furthermore,
we develop a window-based partitioning approach to preserve the spatial
properties of HSIs when their features are being extracted. In this approach,
the HSI is partitioned along its spatial dimension into n windows and the
covariance matrices of each window are computed. The covariance matrices of
the windows are then merged into a single matrix through using the Kalman
ltering approach so that the resulting covariance matrix may be used for
dimension reduction. Experiments show that the windowing approach achieves
high classi cation accuracy and preserves the spatial properties of HSIs. For
the proposed feature extraction techniques, Support Vector Machine (SVM)
and Neural Networks (NN) classi cation techniques are employed and their
performances are compared for these two classi ers. The performances of all
proposed FE techniques have also been shown to outperform other
state-of-the-art approaches
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