Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SCAttNet: Semantic Segmentation Network with Spatial and Channel
  Attention Mechanism for High-Resolution Remote Sensing Images   High-resolution remote sensing images (HRRSIs) contain substantial ground
object information, such as texture, shape, and spatial location. Semantic
segmentation, which is an important task for element extraction, has been
widely used in processing mass HRRSIs. However, HRRSIs often exhibit large
intraclass variance and small interclass variance due to the diversity and
complexity of ground objects, thereby bringing great challenges to a semantic
segmentation task. In this paper, we propose a new end-to-end semantic
segmentation network, which integrates lightweight spatial and channel
attention modules that can refine features adaptively. We compare our method
with several classic methods on the ISPRS Vaihingen and Potsdam datasets.
Experimental results show that our method can achieve better semantic
segmentation results. The source codes are available at
https://github.com/lehaifeng/SCAttNet.

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The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: physics
10
The categories of this paper are: q-bio
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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: eess