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Advances in Methods and Techniques for Satellite Image Processing and Analysis

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 May 2025 | Viewed by 10295

Special Issue Editor

Dr. Ali Jamali

E-Mail Website
Guest Editor
Department of Geography, Simon Fraser University, 8888 University Dr W, Burnaby, BC V5A 1S6, Canada
Interests: deep learning; remote sensing; machine learning; image processing; computer vision

Special Issue Information

Dear Colleagues,

As large-scale datasets are becoming more accessible and because high-performance computing devices and effective training methods are available, machine learning-based techniques have been introduced in a variety of applications, such as in the analysis of remote sensing images. Advanced machine learning algorithms have emerged as a powerful tool for analyzing satellite imagery in recent years. These models can be used for various tasks, such as classification, forecasting, regression, and clustering. Unseen challenges arise when applying computer vision-developed machine learning techniques to large-scale, multivariate, noisy, irregularly collected remote sensing data.

This Special Issue aims to publish studies covering different uses of machine learning models, as well as the utilization and fusion of various sensors and platforms in remote sensing.

Review and research papers on cutting-edge CNNs and vision transformer-based methods for machine learning, architectures, and structures for applications in remote sensing will be published in this Special Issue, with an emphasis on tasks that address the problems in this field.

Potential topics of interest include, but are not limited to, the following:

  1. Shallow and deep learning remote sensing image interpretation and analysis (image classification, pan-sharpening, image enhancement, object detection, semantic segmentation, and change detection).
  2. Graphic, adversarial, unsupervised, semi-supervised, self-supervised, active, and transfer learning for dealing with limited and/or low-quality data.
  3. Knowledge acquisition of deep learning models for remote sensing imagery.
  4. Novel benchmark datasets for remote sensing image analysis.
  5. Applications of Vision Transformers (ViTs) in remote sensing.

Dr. Ali Jamali
Guest Editor

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

  • deep learning
  • computer vision
  • machine learning
  • image processing
  • remote sensing
  • image analysis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

17 pages, 45843 KiB  
Article
How to Learn More? Exploring Kolmogorov–Arnold Networks for Hyperspectral Image Classification
by Ali Jamali, Swalpa Kumar Roy, Danfeng Hong, Bing Lu and Pedram Ghamisi
Remote Sens. 2024, 16(21), 4015; https://doi.org/10.3390/rs16214015 - 29 Oct 2024
Cited by 15 | Viewed by 3333
Abstract
Convolutional neural networks (CNNs) and vision transformers (ViTs) have shown excellent capability in complex hyperspectral image (HSI) classification. However, these models require a significant number of training data and are computational resources. On the other hand, modern Multi-Layer Perceptrons (MLPs) have demonstrated a [...] Read more.
Convolutional neural networks (CNNs) and vision transformers (ViTs) have shown excellent capability in complex hyperspectral image (HSI) classification. However, these models require a significant number of training data and are computational resources. On the other hand, modern Multi-Layer Perceptrons (MLPs) have demonstrated a great classification capability. These modern MLP-based models require significantly less training data compared with CNNs and ViTs, achieving state-of-the-art classification accuracy. Recently, Kolmogorov–Arnold networks (KANs) were proposed as viable alternatives for MLPs. Because of their internal similarity to splines and their external similarity to MLPs, KANs are able to optimize learned features with remarkable accuracy, in addition to being able to learn new features. Thus, in this study, we assessed the effectiveness of KANs for complex HSI data classification. Moreover, to enhance the HSI classification accuracy obtained by the KANs, we developed and proposed a hybrid architecture utilizing 1D, 2D, and 3D KANs. To demonstrate the effectiveness of the proposed KAN architecture, we conducted extensive experiments on three newly created HSI benchmark datasets: QUH-Pingan, QUH-Tangdaowan, and QUH-Qingyun. The results underscored the competitive or better capability of the developed hybrid KAN-based model across these benchmark datasets over several other CNN- and ViT-based algorithms, including 1D-CNN, 2DCNN, 3D CNN, VGG-16, ResNet-50, EfficientNet, RNN, and ViT. Full article
(This article belongs to the Special Issue Advances in Methods and Techniques for Satellite Image Processing and Analysis)
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16 pages, 6697 KiB  
Article
Semantic Segmentation and Classification of Active and Abandoned Agricultural Fields through Deep Learning in the Southern Peruvian Andes
by James Zimmer-Dauphinee and Steven A. Wernke
Remote Sens. 2024, 16(19), 3546; https://doi.org/10.3390/rs16193546 - 24 Sep 2024
Viewed by 1045
Abstract
The monumental scale agricultural infrastructure systems built by Andean peoples during pre-Hispanic times have enabled intensive agriculture in the high-relief, arid/semi-arid landscape of the Southern Peruvian Andes. Large tracts of these labor-intensive systems have been abandoned, however, owing in large measure to a [...] Read more.
The monumental scale agricultural infrastructure systems built by Andean peoples during pre-Hispanic times have enabled intensive agriculture in the high-relief, arid/semi-arid landscape of the Southern Peruvian Andes. Large tracts of these labor-intensive systems have been abandoned, however, owing in large measure to a range of demographic, economic, and political crises precipitated by the Spanish invasion of the 16th century CE. This research seeks to better understand the dynamics of agricultural intensification and deintensification in the Andes by inventorying through the semantic segmentation of active and abandoned agricultural fields in satellite imagery across approximately 77,000 km2 of the Southern Peruvian Highlands. While manual digitization of agricultural fields in satellite imagery is time-consuming and labor-intensive, deep learning-based semantic segmentation makes it possible to map and classify en masse Andean agricultural infrastructure. Using high resolution satellite imagery, training and validation data were manually produced in distributed sample areas and were used to transfer-train a convolutional neural network for semantic segmentation. The resulting dataset was compared to manual surveys of the region and results suggest that deep learning can generate larger and more accurate datasets than those generated by hand. Full article
(This article belongs to the Special Issue Advances in Methods and Techniques for Satellite Image Processing and Analysis)
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36 pages, 8921 KiB  
Article
Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
by Andrew Du, Anh-Dzung Doan, Yee Wei Law and Tat-Jun Chin
Remote Sens. 2024, 16(18), 3469; https://doi.org/10.3390/rs16183469 - 18 Sep 2024
Cited by 2 | Viewed by 1672
Abstract
The advent of satellite-borne machine learning hardware accelerators has enabled the onboard processing of payload data using machine learning techniques such as convolutional neural networks (CNNs). A notable example is using a CNN to detect the presence of clouds in the multispectral data [...] Read more.
The advent of satellite-borne machine learning hardware accelerators has enabled the onboard processing of payload data using machine learning techniques such as convolutional neural networks (CNNs). A notable example is using a CNN to detect the presence of clouds in the multispectral data captured on Earth observation (EO) missions, whereby only clear sky data are downlinked to conserve bandwidth. However, prior to deployment, new missions that employ new sensors will not have enough representative datasets to train a CNN model, while a model trained solely on data from previous missions will underperform when deployed to process the data on the new missions. This underperformance stems from the domain gap, i.e., differences in the underlying distributions of the data generated by the different sensors in previous and future missions. In this paper, we address the domain gap problem in the context of onboard multispectral cloud detection. Our main contributions lie in formulating new domain adaptation tasks that are motivated by a concrete EO mission, developing a novel algorithm for bandwidth-efficient supervised domain adaptation, and demonstrating test-time adaptation algorithms on space deployable neural network accelerators. Our contributions enable minimal data transmission to be invoked (e.g., only 1% of the weights in ResNet50) to achieve domain adaptation, thereby allowing more sophisticated CNN models to be deployed and updated on satellites without being hampered by domain gap and bandwidth limitations. Full article
(This article belongs to the Special Issue Advances in Methods and Techniques for Satellite Image Processing and Analysis)
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17 pages, 16804 KiB  
Article
Land Cover Mapping in a Mangrove Ecosystem Using Hybrid Selective Kernel-Based Convolutional Neural Networks and Multi-Temporal Sentinel-2 Imagery
by Seyd Teymoor Seydi, Seyed Ali Ahmadi, Arsalan Ghorbanian and Meisam Amani
Remote Sens. 2024, 16(15), 2849; https://doi.org/10.3390/rs16152849 - 3 Aug 2024
Cited by 4 | Viewed by 2795
Abstract
Mangrove ecosystems provide numerous ecological services and serve as vital habitats for a wide range of flora and fauna. Thus, accurate mapping and monitoring of relevant land covers in mangrove ecosystems are crucial for effective conservation and management efforts. In this study, we [...] Read more.
Mangrove ecosystems provide numerous ecological services and serve as vital habitats for a wide range of flora and fauna. Thus, accurate mapping and monitoring of relevant land covers in mangrove ecosystems are crucial for effective conservation and management efforts. In this study, we proposed a novel approach for mangrove ecosystem mapping using a Hybrid Selective Kernel-based Convolutional Neural Network (HSK-CNN) framework and multi-temporal Sentinel-2 imagery. A time series of the Normalized Difference Vegetation Index (NDVI) products derived from Sentinel-2 imagery was produced to capture the temporal behavior of land cover types in the dynamic ecosystem of the study area. The proposed algorithm integrated Selective Kernel-based feature extraction techniques to facilitate the effective learning and classification of multiple land cover types within the dynamic mangrove ecosystems. The model demonstrated a high Overall Accuracy (OA) of 94% in classifying eight land cover classes, including mangrove, tidal zone, water, mudflat, urban, and vegetation. The HSK-CNN demonstrated superior performance compared to other algorithms, including random forest (OA = 85%), XGBoost (OA = 87%), Three-Dimensional (3D)-DenseNet (OA = 90%), Two-Dimensional (2D)-CNN (OA = 91%), Multi-Layer Perceptron (MLP)-Mixer (OA = 92%), and Swin Transformer (OA = 93%). Additionally, it was observed that the structure of the network, such as the types of convolutional layers and patch sizes, affected the classification accuracy using the proposed model and, thus, the optimum scenarios and values of these parameters should be determined to obtain the highest possible classification accuracy. Overall, it was observed that the produced map could offer valuable insights into the distribution of different land cover types in the mangrove ecosystem, facilitating informed decision-making for conservation and sustainable management efforts. Full article
(This article belongs to the Special Issue Advances in Methods and Techniques for Satellite Image Processing and Analysis)
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