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Remote Sensing
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Machine Learning Approaches for Semantic and Instance Segmentation in Remote...
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Machine Learning Approaches for Semantic and Instance Segmentation in Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1250

Special Issue Editors

Dr. Amin Zehtabian

E-Mail Website
Guest Editor
Department of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Berlin, Germany
Interests: pattern recognition; computer vision; remote sensing; hyperspectral image analysis; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Roozbeh Rajabi

E-Mail Website
Guest Editor
Department of Naval, Electrical, Electronic and Telecommunications Engineering, University of Genoa (UniGe), I-16145 Genoa, Italy
Interests: hyperspectral data analysis; pattern recognition; digital signal and image processing
Prof. Dr. Pedram Ghamisi

E-Mail Website
Guest Editor
1. Machine Learning Group, Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf, 09599 Freiberg, Germany
2. Institute of Advanced Research in Artificial Intelligence (IARAI), 1030 Vienna, Austria
Interests: machine and deep learning; image and signal processing; hyperspectral image analysis; multisensor data fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Keshav Singh

E-Mail Website
Guest Editor
Agriculture and Agri-Food Canada (AAFC), Lethbridge Research Centre, 5403—1 Ave. S., Lethbridge, AB, Canada
Interests: remote sensing; UAV imaging; plant phenomics; precision agriculture; crops mapping and big-data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are thrilled to announce the continuation of our successful Special Issue in the journal Remote Sensing. Building on the achievements of the first, we are excited to introduce the second volume, which aims to explore new frontiers in semantic and instance segmentation within the realm of remotely sensed images.

In the previous volume, we witnessed groundbreaking research on spectral–spatial classification, object-based image analysis, feature extraction, and machine learning. Now, we are broadening our horizons to encompass the dynamic field of remote sensing research, placing a special emphasis on machine learning approaches for semantic and instance segmentation in remote sensing.

This volume invites contributions from researchers specializing in semantic and instance segmentation for remotely sensed imagery. While we continue to appreciate the significance of integrating spectral and spatial information extraction methods for hyperspectral data analysis, this second volume embraces a broader array of methodologies and remotely sensed data types. This encompasses cutting-edge machine learning and deep learning approaches that are applicable to a wide spectrum of remote sensing data, extending beyond hyperspectral images.

Topics of Interest:

  • Semantic and instance segmentation in remote sensing data;
  • Supervised machine learning for image segmentation and object detection;
  • Deep learning techniques for multispectral/hyperspectral image analysis;
  • Novel approaches for feature extraction;
  • Insights from applying state-of-the-art deep learning scenarios.

Dr. Amin Zehtabian
Dr. Roozbeh Rajabi
Prof. Dr. Pedram Ghamisi
Dr. Keshav Singh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • semantic segmentation
  • instance segmentation
  • object detection
  • hyperspectral imagery
  • supervised machine learning
  • deep learning
  • feature extraction

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Published Papers (1 paper)

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Research

21 pages, 4751 KiB  
Article
Border-Enhanced Triple Attention Mechanism for High-Resolution Remote Sensing Images and Application to Land Cover Classification
by Guoying Wang, Jiahao Chen, Lufeng Mo, Peng Wu and Xiaomei Yi
Remote Sens. 2024, 16(15), 2814; https://doi.org/10.3390/rs16152814 - 31 Jul 2024
Cited by 1 | Viewed by 623
Abstract
With the continuous development and popularization of remote sensing technology, remote sensing images have been widely used in the field of land cover classification. Since remote sensing images have complex spatial structure and texture features, it is becoming a challenging problem to accurately [...] Read more.
With the continuous development and popularization of remote sensing technology, remote sensing images have been widely used in the field of land cover classification. Since remote sensing images have complex spatial structure and texture features, it is becoming a challenging problem to accurately categorize them. Land cover classification has practical application value in various fields, such as environmental monitoring and protection, urban and rural planning and management, and climate change research. In recent years, remote sensing image classification methods based on deep learning have been rapidly developed, in which semantic segmentation technology has become one of the mainstream methods for land cover classification using remote sensing image. Traditional semantic segmentation algorithms tend to ignore the edge information, resulting in poor classification of the edge part in land cover classification, and there are numerous attention mechanisms to make improvements for these problems. In this paper, a triple attention mechanism, BETAM (Border-Enhanced Triple Attention Mechanism), for edge feature enhancement of high-resolution remote sensing images is proposed. Furthermore, a new model on the basis of the semantic segmentation network model DeeplabV3+ is also introduced, which is called DeepBETAM. The triple attention mechanism BETAM is able to capture feature dependencies in three dimensions: position, space, and channel, respectively. Through feature importance weighting, modeling of spatial relationships, and adaptive learning capabilities, the model BETAM pays more attention to edge features, thus improving the accuracy of edge detection. A remote sensing image dataset SMCD (Subject Meticulous Categorization Dataset) is constructed to verify the robustness of the attention mechanism BETAM and the model DeepBETAM. Extensive experiments were conducted on the two self-built datasets FRSID and SMCD. Experimental results showed that the mean Intersection over Union (mIoU), mean Pixel Accuracy (mPA), and mean Recall (mRecall) of DeepBETAM are 63.64%, 71.27%, and 71.31%, respectively. These metrics are superior to DeeplabV3+, DeeplabV3+(SENet), DeeplabV3+(CBAM), DeeplabV3+(SAM), DeeplabV3+(ECANet), and DeeplabV3+(CAM), which are network models that incorporate different attention mechanisms. The reason is that BETAM has better edge segmentation results and segmentation accuracy. Meanwhile, on the basis of the self-built dataset, the four main classifications of buildings, cultivated land, water bodies and vegetation were subdivided and detected, and good experimental results were obtained, which verified the robustness of the attention mechanism BETAM and the model DeepBETAM. The method has broad application prospects and can provide favorable support for research and application in the field of surface classification. Full article
(This article belongs to the Special Issue Machine Learning Approaches for Semantic and Instance Segmentation in Remote Sensing)
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