HA-Net: bare soil extraction from optical remote sensing images

Junqi Zhao; Lifu Chen; Haoda Chen; Yuchen Jin

doi:10.1117/12.3051772

18 November 2024 HA-Net: bare soil extraction from optical remote sensing images

Junqi Zhao, Lifu Chen, Haoda Chen, Yuchen Jin

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134031H (2024) https://doi.org/10.1117/12.3051772
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Bare soil will cause the soil erosion and contribute to air pollution through the generation of dust, making timely and effective monitoring of bare soil an urgent requirement for environmental management. Though there have been some researches in bare soil extraction using high-resolution remote sensing images, great challenges still need to be solved, such as complex background interference and multi-scale problem. In this regard, the Hybrid Attention Network (HA-Net) is proposed for automatic extraction of bare soil from high-resolution remote sensing images, which includes the encoder and the decoder. In the encoder, HA-Net initially utilizes BoTNet50 for primary feature extraction, producing four-level features. The extracted highest-level features are then input into the constructed Spatial Information Perception Module (SIPM) and the Channel Information Enhancement Module (CIEM), to emphasize the spatial and channel dimensions of bare soil information adequately. During the decoder, the Semantic Restructuring-based Upsampling Module (SRUM) is proposed to leverage the semantic information of input features, and compensate for the loss of detailed information during the down-sampling in the encoder. Experiment is performed based on high-resolution remote sensing images from the China-Pakistan Resources Satellite 04A. The results show that HA-Net obviously outperforms several excellent semantic segmentation networks in bare soil extraction. The IoU and Recall of HA-Net in test scene can reach 81.4% and 87.4%, respectively, which demonstrates the excellent performance of HA-Net. It embodies the powerful ability of HA-Net for suppressing the interference from complex background and solving the multi-scale issue.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Junqi Zhao, Lifu Chen, Haoda Chen, and Yuchen Jin "HA-Net: bare soil extraction from optical remote sensing images", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134031H (18 November 2024); https://doi.org/10.1117/12.3051772

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KEYWORDS

Remote sensing

Convolution

Semantics

Soil science

Deep learning

Feature extraction

Image segmentation

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