An Encoder-Decoder Network Trained with Multi-Branch Auxiliary Learning for Extracting Transverse Aeolian Ridge Morphological Parameters from High-Resolution Mars Imagery
Transverse aeolian ridges (TARs) are the most widely distributed and enigmatic aeolian landforms on the surface of Mars, holding significant research value and implications for interpreting ancient wind fields and environments, searching for water and life, and selecting landing sites. However, accu...
Saved in:
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2025-08-01
|
Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1415/2025/isprs-archives-XLVIII-G-2025-1415-2025.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
_version_ | 1839605727344197632 |
---|---|
author | J. Sun J. Sun Z. Cao Z. Cao Z. Kang Z. Kang Z. Kang J. Li J. Jiang B. Song X. Zhang |
author_facet | J. Sun J. Sun Z. Cao Z. Cao Z. Kang Z. Kang Z. Kang J. Li J. Jiang B. Song X. Zhang |
author_sort | J. Sun |
collection | DOAJ |
description | Transverse aeolian ridges (TARs) are the most widely distributed and enigmatic aeolian landforms on the surface of Mars, holding significant research value and implications for interpreting ancient wind fields and environments, searching for water and life, and selecting landing sites. However, accurately interpreting the morphological parameters of TARs, including their edge contours and ridge lines, remains a challenge. To tackle this issue, this paper proposes a Multi-branch Auxiliary Training Encoder-Decoder Network (MATED-Net) for detecting the edge contours and ridge lines of TARs on Mars. Built upon the Unet architecture, MATED-Net incorporates four auxiliary training losses to perceive features at different scales. Then, We introduce a lightweight attention mechanism to guide the fusion of multi-scale features. Finally, an edge tracing loss is introduced to enhance the distinction between edge pixels and surrounding confusing pixels, thereby accurately tracking the true positions of edges. To verify the effectiveness of the MATED-Net in detecting TARs’ contours and ridge lines, this paper constructs a dataset of TAR ridge lines based on HiRISE and HiRIC imagery. To facilitate subsequent training and testing, all images were clipped to a size of 512 × 512and converted to the VOC dataset format, resulting in a total of 1000 images and corresponding label data. The experimental results demonstrate a precision of 0.72, a recall of 0.67, and a mean Intersection over Union (MIoU) of 0.57 for edge extraction. |
format | Article |
id | doaj-art-df1db809e3774d2fb896e6f4b3a5dcc5 |
institution | Matheson Library |
issn | 1682-1750 2194-9034 |
language | English |
publishDate | 2025-08-01 |
publisher | Copernicus Publications |
record_format | Article |
series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
spelling | doaj-art-df1db809e3774d2fb896e6f4b3a5dcc52025-08-01T16:35:15ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342025-08-01XLVIII-G-20251415142010.5194/isprs-archives-XLVIII-G-2025-1415-2025An Encoder-Decoder Network Trained with Multi-Branch Auxiliary Learning for Extracting Transverse Aeolian Ridge Morphological Parameters from High-Resolution Mars ImageryJ. Sun0J. Sun1Z. Cao2Z. Cao3Z. Kang4Z. Kang5Z. Kang6J. Li7J. Jiang8B. Song9X. Zhang10College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaSubcenter of International Cooperation and Research on Lunar and Planetary Exploration, Center of Space Exploration, Ministry of Education of The People’s Republic of China, No. 29 Xueyuan Road, Haidian District, Beijing 100083, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaSubcenter of International Cooperation and Research on Lunar and Planetary Exploration, Center of Space Exploration, Ministry of Education of The People’s Republic of China, No. 29 Xueyuan Road, Haidian District, Beijing 100083, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaSchool of Land Science and Technology, China University of Geosciences, Xueyuan Road, Beijing, 100083, ChinaSubcenter of International Cooperation and Research on Lunar and Planetary Exploration, Center of Space Exploration, Ministry of Education of The People’s Republic of China, No. 29 Xueyuan Road, Haidian District, Beijing 100083, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaTransverse aeolian ridges (TARs) are the most widely distributed and enigmatic aeolian landforms on the surface of Mars, holding significant research value and implications for interpreting ancient wind fields and environments, searching for water and life, and selecting landing sites. However, accurately interpreting the morphological parameters of TARs, including their edge contours and ridge lines, remains a challenge. To tackle this issue, this paper proposes a Multi-branch Auxiliary Training Encoder-Decoder Network (MATED-Net) for detecting the edge contours and ridge lines of TARs on Mars. Built upon the Unet architecture, MATED-Net incorporates four auxiliary training losses to perceive features at different scales. Then, We introduce a lightweight attention mechanism to guide the fusion of multi-scale features. Finally, an edge tracing loss is introduced to enhance the distinction between edge pixels and surrounding confusing pixels, thereby accurately tracking the true positions of edges. To verify the effectiveness of the MATED-Net in detecting TARs’ contours and ridge lines, this paper constructs a dataset of TAR ridge lines based on HiRISE and HiRIC imagery. To facilitate subsequent training and testing, all images were clipped to a size of 512 × 512and converted to the VOC dataset format, resulting in a total of 1000 images and corresponding label data. The experimental results demonstrate a precision of 0.72, a recall of 0.67, and a mean Intersection over Union (MIoU) of 0.57 for edge extraction.https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1415/2025/isprs-archives-XLVIII-G-2025-1415-2025.pdf |
spellingShingle | J. Sun J. Sun Z. Cao Z. Cao Z. Kang Z. Kang Z. Kang J. Li J. Jiang B. Song X. Zhang An Encoder-Decoder Network Trained with Multi-Branch Auxiliary Learning for Extracting Transverse Aeolian Ridge Morphological Parameters from High-Resolution Mars Imagery The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
title | An Encoder-Decoder Network Trained with Multi-Branch Auxiliary Learning for Extracting Transverse Aeolian Ridge Morphological Parameters from High-Resolution Mars Imagery |
title_full | An Encoder-Decoder Network Trained with Multi-Branch Auxiliary Learning for Extracting Transverse Aeolian Ridge Morphological Parameters from High-Resolution Mars Imagery |
title_fullStr | An Encoder-Decoder Network Trained with Multi-Branch Auxiliary Learning for Extracting Transverse Aeolian Ridge Morphological Parameters from High-Resolution Mars Imagery |
title_full_unstemmed | An Encoder-Decoder Network Trained with Multi-Branch Auxiliary Learning for Extracting Transverse Aeolian Ridge Morphological Parameters from High-Resolution Mars Imagery |
title_short | An Encoder-Decoder Network Trained with Multi-Branch Auxiliary Learning for Extracting Transverse Aeolian Ridge Morphological Parameters from High-Resolution Mars Imagery |
title_sort | encoder decoder network trained with multi branch auxiliary learning for extracting transverse aeolian ridge morphological parameters from high resolution mars imagery |
url | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1415/2025/isprs-archives-XLVIII-G-2025-1415-2025.pdf |
work_keys_str_mv | AT jsun anencoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT jsun anencoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT zcao anencoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT zcao anencoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT zkang anencoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT zkang anencoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT zkang anencoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT jli anencoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT jjiang anencoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT bsong anencoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT xzhang anencoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT jsun encoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT jsun encoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT zcao encoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT zcao encoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT zkang encoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT zkang encoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT zkang encoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT jli encoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT jjiang encoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT bsong encoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery AT xzhang encoderdecodernetworktrainedwithmultibranchauxiliarylearningforextractingtransverseaeolianridgemorphologicalparametersfromhighresolutionmarsimagery |