Aggregated Context Network For Semantic Segmentation Of Aerial Images

With the considerable advancement of remote sensing technology and computer vision, automatic scene understanding for very high-resolution aerial (VHR) imagery became a necessary research topic. Semantic segmentation of VHR imagery is an important task where context information plays a crucial role....

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Vydáno v:Proceedings - International Conference on Image Processing s. 1526 - 1530
Hlavní autoři: Chouhan, Avinash, Sur, Arijit, Chutia, Dibyajyoti
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 16.10.2022
Témata:
Benchmark testing
Computer architecture
Computer vision
Convolution
Feature extraction
multi-context block
residual attentive connection
Semantic segmentation
Semantics
Task analysis
ISSN:2381-8549
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Abstract With the considerable advancement of remote sensing technology and computer vision, automatic scene understanding for very high-resolution aerial (VHR) imagery became a necessary research topic. Semantic segmentation of VHR imagery is an important task where context information plays a crucial role. Adequate feature delineation is difficult due to high-class imbalance in remotely sensed data. In this work, we proposed a variant of encoder-decoder-based architecture where residual attentive skip connections are incorporated. We added a multi-context block in each of the encoder units to capture multi-scale and multi-context features and used dense connections for effective feature extraction. A comprehensive set of experiments reveal that the proposed scheme outperformed recently published work by 3% in overall accuracy and F1 score for ISPRS Vaihingen and ISPRS Potsdam benchmark datasets.
AbstractList With the considerable advancement of remote sensing technology and computer vision, automatic scene understanding for very high-resolution aerial (VHR) imagery became a necessary research topic. Semantic segmentation of VHR imagery is an important task where context information plays a crucial role. Adequate feature delineation is difficult due to high-class imbalance in remotely sensed data. In this work, we proposed a variant of encoder-decoder-based architecture where residual attentive skip connections are incorporated. We added a multi-context block in each of the encoder units to capture multi-scale and multi-context features and used dense connections for effective feature extraction. A comprehensive set of experiments reveal that the proposed scheme outperformed recently published work by 3% in overall accuracy and F1 score for ISPRS Vaihingen and ISPRS Potsdam benchmark datasets.
Author Chutia, Dibyajyoti
Chouhan, Avinash
Sur, Arijit
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  givenname: Arijit
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  organization: Indian Institute of Technology,Guwahati,Assam,India,781039
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  givenname: Dibyajyoti
  surname: Chutia
  fullname: Chutia, Dibyajyoti
  organization: North Eastern Space Applications Centre,Umiam,Meghalaya,India,793103
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Snippet With the considerable advancement of remote sensing technology and computer vision, automatic scene understanding for very high-resolution aerial (VHR) imagery...
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SubjectTerms Benchmark testing
Computer architecture
Computer vision
Convolution
Feature extraction
multi-context block
residual attentive connection
Semantic segmentation
Semantics
Task analysis
Title Aggregated Context Network For Semantic Segmentation Of Aerial Images
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