Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tensor Representation and Manifold Learning Methods for Remote Sensing
  Images   One of the main purposes of earth observation is to extract interested
information and knowledge from remote sensing (RS) images with high efficiency
and accuracy. However, with the development of RS technologies, RS system
provide images with higher spatial and temporal resolution and more spectral
channels than before, and it is inefficient and almost impossible to manually
interpret these images. Thus, it is of great interests to explore automatic and
intelligent algorithms to quickly process such massive RS data with high
accuracy. This thesis targets to develop some efficient information extraction
algorithms for RS images, by relying on the advanced technologies in machine
learning. More precisely, we adopt the manifold learning algorithms as the
mainline and unify the regularization theory, tensor-based method, sparse
learning and transfer learning into the same framework. The main contributions
of this thesis are as follows.

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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph