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

Deadline for manuscript submissions: closed (15 December 2025) | Viewed by 26352

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Dr. Ali Jamali

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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:

Shallow and deep learning remote sensing image interpretation and analysis (image classification, pan-sharpening, image enhancement, object detection, semantic segmentation, and change detection).
Graphic, adversarial, unsupervised, semi-supervised, self-supervised, active, and transfer learning for dealing with limited and/or low-quality data.
Knowledge acquisition of deep learning models for remote sensing imagery.
Novel benchmark datasets for remote sensing image analysis.
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 250 words) can be sent to the Editorial Office for assessment.

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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Published Papers (11 papers)

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Research

25 pages, 65227 KB

Open AccessArticle

SAANet: Detecting Dense and Crossed Stripe-like Space Objects Under Complex Stray Light Interference

by Yuyuan Liu, Hongfeng Long, Xinghui Sun, Yihui Zhao, Zhuo Chen, Yuebo Ma and Rujin Zhao

Remote Sens. 2026, 18(2), 299; https://doi.org/10.3390/rs18020299 - 16 Jan 2026

Viewed by 329

Abstract

With the deployment of mega-constellations, the proliferation of on-orbit Resident Space Objects (RSOs) poses a severe challenge to Space Situational Awareness (SSA). RSOs produce elongated and stripe-like signatures in long-exposure imagery as a result of their relative orbital motion. The accurate detection of these signatures is essential for critical applications like satellite navigation and space debris monitoring. However, on-orbit detection faces two challenges: the obscuration of dim RSOs by complex stray light interference, and their dense overlapping trajectories. To address these challenges, we propose the Shape-Aware Attention Network (SAANet), establishing a unified Shape-Aware Paradigm. The network features a streamlined Shape-Aware Feature Pyramid Network (SA-FPN) with structurally integrated Two-way Orthogonal Attention (TTOA) to explicitly model linear topologies, preserving dim signals under intense stray light conditions. Concurrently, we propose an Adaptive Linear Oriented Bounding Box (AL-OBB) detection head that leverages a Joint Geometric Constraint Mechanism to resolve the ambiguity of regressing targets amid dense, overlapping trajectories. Experiments on the AstroStripeSet and StarTrails datasets demonstrate that SAANet achieves state-of-the-art (SOTA) performance, achieving Recalls of 0.930 and 0.850, and Average Precisions (APs) of 0.864 and 0.815, respectively. Full article

(This article belongs to the Special Issue Advances in Methods and Techniques for Satellite Image Processing and Analysis)

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31 pages, 33847 KB

Open AccessArticle

Incremental Data Cube Architecture for Sentinel-2 Time Series: Multi-Cube Approaches to Dynamic Baseline Construction

by Roxana Trujillo and Mauricio Solar

Remote Sens. 2026, 18(2), 260; https://doi.org/10.3390/rs18020260 - 14 Jan 2026

Viewed by 694

Abstract

Incremental computing is becoming increasingly important for processing large-scale datasets. In satellite imagery, spatial resolution, temporal depth, and large files pose significant computational challenges, requiring efficient architectures to manage processing time and resource usage. Accordingly, in this study, we propose a dynamic architecture, termed Multi-Cube, for optical satellite time series. The framework introduces a modular and baseline-aware approach that enables scalable subdivision, incremental growth, and consistent management of spatiotemporal data. Built on NetCDF, xarray, and Zarr, Multi-Cube automatically constructs stable multidimensional data cubes while minimizing redundant reprocessing, formalizing automated internal decisions governing cube subdivision, baseline reuse, and incremental updates to support recurrent monitoring workflows. Its performance was evaluated using more than 83,000 Sentinel-2 images (covering 2016–2024) across multiple areas of interest. The proposed approach achieved a 5.4× reduction in end-to-end runtime, decreasing execution time from 53 h to 9 h, while disk I/O requirements were reduced by more than two orders of magnitude compared with a traditional sequential reprocessing pipeline. The framework supports parallel execution and on-demand sub-cube extraction for responsive large-area monitoring while internally handling incremental updates and adaptive cube management without requiring manual intervention. The results demonstrate that the Multi-Cube architecture provides a decision-driven foundation for integrating dynamic Earth observation workflows with analytical modules. Full article

(This article belongs to the Special Issue Advances in Methods and Techniques for Satellite Image Processing and Analysis)

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25 pages, 1903 KB

Open AccessArticle

Frequency-Aware Enhancement Network for Satellite Video Super-Resolution

by Xiujuan Lang, Jin Zhang, Tao Lu, Yuan Yao, Yu Wang and Liwei Wang

Remote Sens. 2025, 17(24), 3994; https://doi.org/10.3390/rs17243994 - 11 Dec 2025

Viewed by 716

Abstract

The lower quality of frames in satellite videos compared to natural videos poses significant challenges in capturing detailed information for alignment and fusion in the image space. In this paper, we introduce a novel frequency-aware enhancement network (FAENet) for satellite video super-resolution (SVSR), which tackles these challenges from a frequency-domain perspective. By leveraging frequency components, FAENet amplifies the distinctions between frames and between objects, thereby improving alignment and reconstruction accuracy. Firstly, the proposed Frequency Alignment Compensation Mechanism (FACM) incorporates a frequency-domain distribution alignment function to enable effective alignment compensation. This mechanism can be seamlessly integrated into existing alignment methods designed for natural video, thereby enhancing their applicability to SVSR tasks. Secondly, we introduce the Frequency Prompt Enhancement Block (FPEB), which facilitates edge reconstruction by leveraging frequency-domain prompts to distinguish objects from artifacts, thereby improving the clarity and accuracy of reconstructed edges. The proposed FAENet achieves 35.33 dB PSNR on the Jilin-189 dataset and 40.57 dB on the SAT-MTB-VSR dataset, outperforming other state-of-the-art compared methods and demonstrating its effectiveness and robustness in addressing the unique challenges of SVSR. Full article

(This article belongs to the Special Issue Advances in Methods and Techniques for Satellite Image Processing and Analysis)

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20 pages, 9178 KB

Open AccessArticle

Graph-Based Relaxation for Over-Normalization Avoidance in Reflectance Normalization of Multi-Temporal Satellite Imagery

by Gabriel Yedaya Immanuel Ryadi, Chao-Hung Lin and Bo-Yi Lin

Remote Sens. 2025, 17(23), 3877; https://doi.org/10.3390/rs17233877 - 29 Nov 2025

Viewed by 464

Abstract

Reflectance normalization is critical for minimizing temporal discrepancies and facilitating reliable multi-temporal satellite analysis. However, this process is challenged by the risks of under-normalization and over-normalization, which stem from the inherent complexities of varying atmospheric conditions, data acquisition, and environmental dynamics. Under-normalization occurs when multi-temporal variations are insufficiently corrected, resulting in temporal reflectance inconsistencies. Over-normalization arises when overly aggressive adjustments suppress meaningful variability, such as seasonal and phenological patterns, thereby compromising data integrity. Effectively addressing these challenges is essential for preserving the spatial and temporal fidelity of satellite imagery, which is crucial for applications such as environmental monitoring and long-term change analysis. This study introduces a novel graph-based relaxation for reflectance normalization aimed at addressing issues of under- and over-normalization through a two-stage structural normalization strategy: intra-normalization and inter-normalization. A graph structure represents adjacency and similarity among image instances, enabling an iterative relaxation process to adjust reflectance values. In the proposed framework, the intra-normalization stage aligns images within the same reflectance group to preserve temporally local reflectance patterns, while the inter-normalization stage harmonizes reflectance across different groups, ensuring smooth temporal transitions and maintaining essential temporal variability. Experimental results with the metrics root mean squared error (RMSE) and Structural Similarity Index Measure (SSIM) demonstrate the effectiveness of the proposed method. Specifically, the proposed method achieves around 37% improvement measured by RMSE in the transition of two adjacent image groups compared with related normalization methods. Graph-based relaxation preserves seasonal dynamics, ensures smooth transitions, and improves vegetation indices, making it suitable for both short-term and long-term environmental change analysis. Full article

(This article belongs to the Special Issue Advances in Methods and Techniques for Satellite Image Processing and Analysis)

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24 pages, 4973 KB

Open AccessArticle

An Enhanced Method for Optical Imaging Computation of Space Objects Integrating an Improved Phong Model and Higher-Order Spherical Harmonics

by Qinyu Zhu, Can Xu, Yasheng Zhang, Yao Lu, Xia Wang and Peng Li

Remote Sens. 2025, 17(21), 3543; https://doi.org/10.3390/rs17213543 - 26 Oct 2025

Viewed by 827

Abstract

Space-based optical imaging detection serves as a crucial means for acquiring characteristic information of space objects, with the quality and resolution of images directly influencing the accuracy of subsequent missions. Addressing the scarcity of datasets in space-based optical imaging, this study introduces a method that combines an improved Phong model and higher-order spherical harmonics (HOSH) for the optical imaging computation of space objects. Utilizing HOSH to fit the light field distribution, this approach comprehensively considers direct sunlight, earthshine, reflected light from other extremely distant celestial bodies, and multiple scattering from object surfaces. Through spectral reflectance experiments, an improved Phong model is developed to calculate the optical scattering characteristics of space objects and to retrieve common material properties such as metallicity, roughness, index of refraction (IOR), and Alpha for four types of satellite surfaces. Additionally, this study designs two sampling methods: a random sampling based on the spherical Fibonacci function (RSSF) and a sequential frame sampling based on predefined trajectories (SSPT). Through numerical analysis of the geometric and radiative rendering pipeline, this method simulates multiple scenarios under both high-resolution and wide-field-of-view operational modes across a range of relative distances. Simulation results validate the effectiveness of the proposed approach, with average rendering speeds of 2.86 s per frame and 1.67 s per frame for the two methods, respectively, demonstrating the capability for real-time rapid imaging while maintaining low computational resource consumption. The data simulation process spans six distinct relative distance intervals, ensuring that multi-scale images retain substantial textural features and are accompanied by attitude labels, thereby providing robust support for algorithms aimed at space object attitude estimation, and 3D reconstruction. 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, 74973 KB

Open AccessArticle

TVI-MFAN: A Text–Visual Interaction Multilevel Feature Alignment Network for Visual Grounding in Remote Sensing

by Hao Chi, Weiwei Qin, Xingyu Chen, Wenxin Guo and Baiwei An

Remote Sens. 2025, 17(17), 2993; https://doi.org/10.3390/rs17172993 - 28 Aug 2025

Viewed by 1135

Abstract

Visual grounding for remote sensing (RSVG) focuses on localizing specific objects in remote sensing (RS) imagery based on linguistic expressions. Existing methods typically employ pre-trained models to locate the referenced objects. However, due to the insufficient capability of cross-modal interaction and alignment, the extracted visual features may suffer from semantic drift, limiting the performance of RSVG. To address this, the article introduces a novel RSVG framework named the text–visual interaction multilevel feature alignment network (TVI-MFAN), which leverages a text–visual interaction attention (TVIA) module to dynamically generate adaptive weights and biases at both spatial and channel dimensions, enabling the visual feature to focus on relevant linguistic expressions. Additionally, a multilevel feature alignment network (MFAN) aggregates contextual information by using cross-modal alignment to enhance features and suppress irrelevant regions. Experiments demonstrate that the proposed method achieves 75.65% and 80.24% (2.42% and 3.1% absolute improvement) accuracy on the OPT-RSVG and DIOR-RSVG dataset, validating its effectiveness. Full article

(This article belongs to the Special Issue Advances in Methods and Techniques for Satellite Image Processing and Analysis)

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22 pages, 4465 KB

Open AccessEditor’s ChoiceArticle

Urban Expansion Scenario Prediction Model: Combining Multi-Source Big Data, a Graph Attention Network, a Vector Cellular Automata, and an Agent-Based Model

by Yunqi Gao, Dongya Liu, Xinqi Zheng, Xiaoli Wang and Gang Ai

Remote Sens. 2025, 17(13), 2272; https://doi.org/10.3390/rs17132272 - 2 Jul 2025

Cited by 3 | Viewed by 2215

Abstract

The construction of transition rules is the core and difficulty faced by the cellular automata (CA) model. Dynamic mining of transition rules can more accurately simulate urban land use change. By introducing a graph attention network (GAT) to mine CA model transition rules, the temporal and spatial dynamics of the model are increased based on the construction of a real-time dynamic graph structure. At the same time, by adding an agent-based model (ABM) to the CA model, the simulation evolution of different human decision-making behaviors can be achieved. Based on this, an urban expansion scenario prediction (UESP) model has been proposed: (1) the UESP model employs a multi-head attention mechanism to dynamically capture high-order spatial dependencies, supporting the efficient processing of large-scale datasets with over 50,000 points of interest (POIs); (2) it incorporates the behaviors of agents such as residents, governments, and transportation systems to more realistically reflect human micro-level decision-making; and (3) by integrating macro-structural learning with micro-behavioral modeling, it effectively addresses the existing limitations in representing high-order spatial relationships and human decision-making processes in urban expansion simulations. Based on the policy context of the Outline of the Beijing–Tianjin–Hebei (BTH) Coordinated Development Plan, four development scenarios were designed to simulate construction land change by 2030. The results show that (1) the UESP model achieved an overall accuracy of 0.925, a Kappa coefficient of 0.878, and a FoM index of 0.048, outperforming traditional models, with the FoM being 3.5% higher; (2) through multi-scenario simulation prediction, it is found that under the scenario of ecological conservation and farmland protection, forest and grassland increase by 3142 km², and cultivated land increases by 896 km², with construction land showing a concentrated growth trend; and (3) the expansion of construction land will mainly occur at the expense of farmland, concentrated around Beijing, Tianjin, Tangshan, Shijiazhuang, and southern core cities in Hebei, forming a “core-driven, axis-extended, and cluster-expanded” spatial pattern. 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, 45843 KB

Open AccessArticle

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 51 | Viewed by 5949

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 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 KB

Open AccessArticle

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

Cited by 4 | Viewed by 2688

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 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 KB

Open AccessArticle

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 4 | Viewed by 3534

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 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 KB

Open AccessArticle

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 10 | Viewed by 5435

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 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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