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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Veröffentlicht in:Proceedings - International Conference on Image Processing S. 1526 - 1530
Hauptverfasser: Chouhan, Avinash, Sur, Arijit, Chutia, Dibyajyoti
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 16.10.2022
Schlagworte:
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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Zusammenfassung: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.
ISSN:2381-8549
DOI:10.1109/ICIP46576.2022.9898016