Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End
  Hyperspectral Image Classification   Deep learning techniques have provided significant improvements in
hyperspectral image (HSI) classification. The current deep learning based HSI
classifiers follow a patch-based learning framework by dividing the image into
overlapping patches. As such, these methods are local learning methods, which
have a high computational cost. In this paper, a fast patch-free global
learning (FPGA) framework is proposed for HSI classification. In FPGA, an
encoder-decoder based FCN is utilized to consider the global spatial
information by processing the whole image, which results in fast inference.
However, it is difficult to directly utilize the encoder-decoder based FCN for
HSI classification as it always fails to converge due to the insufficiently
diverse gradients caused by the limited training samples. To solve the
divergence problem and maintain the abilities of FCN of fast inference and
global spatial information mining, a global stochastic stratified sampling
strategy is first proposed by transforming all the training samples into a
stochastic sequence of stratified samples. This strategy can obtain diverse
gradients to guarantee the convergence of the FCN in the FPGA framework. For a
better design of FCN architecture, FreeNet, which is a fully end-to-end network
for HSI classification, is proposed to maximize the exploitation of the global
spatial information and boost the performance via a spectral attention based
encoder and a lightweight decoder. A lateral connection module is also designed
to connect the encoder and decoder, fusing the spatial details in the encoder
and the semantic features in the decoder. The experimental results obtained
using three public benchmark datasets suggest that the FPGA framework is
superior to the patch-based framework in both speed and accuracy for HSI
classification. Code has been made available at:
https://github.com/Z-Zheng/FreeNet.

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The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex