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Advances in Deep Learning and Machine Learning for Remote Sensing Image Analysis

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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: 30 June 2026 | Viewed by 4435

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Special Issue Editors

Dr. Keshav D. Singh

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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; artificial intelligence; big-data analytics
Special Issues, Collections and Topics in MDPI journals

Dr. Abdul Bais

E-Mail Website
Guest Editor

Electronic Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
Interests: image analysis; multimodal image fusion; computer vision; deep learning; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Saeid Homayouni

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Guest Editor

Centre for Water, Earth and Environment, at the National Scientific Research Institute (INRS), Quebec City, QC, Canada
Interests: geomatics; remote sensing; earth observation; optical and synthetic aperture radar; artificial intelligence; machine learning; agro-environmental applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of remote sensing has witnessed a remarkable surge in both the quality and quantity of the data generated, with significant advancements in its spatiotemporal resolution. Concurrently, machine learning and image processing methodologies have experienced substantial progress, particularly in big data analytics. These two advancements have broadened the scope of remote sensing applications across a range of fields such as environmental sciences, agriculture, geosciences, and civil engineering. Machine learning, deep learning, and generative AI techniques have created powerful tools such as non-linear relationship mapping, vision language models, object recognition, image segmentation, and sophisticated detection algorithms, which hold immense potential for enhancing remote sensing applications. When integrated with traditional remote sensing methods, these advanced machine learning approaches could pave the way for innovative solutions in multi-source data fusion, computer vision, and predictive analytics. This integration is crucial for advancing the analysis of remote sensing images, making it an exciting and rapidly evolving area of research.

The scale and complexity of machine learning approaches and the availability of multi-source remote sensing data are significant challenges in handling big data and developing high-performance computational strategies for remote sensing applications. Addressing these challenges requires advancements in machine learning, deep learning techniques capable of managing large datasets, and methods for multi-source data fusion to enhance object detection, image segmentation, classification, and other remote sensing tasks. We invite submissions on themes including imagery data analysis, remote sensing, machine learning, deep learning, computer vision, big data, high-performance computing (HPC), predictive analytics, multi-source/sensor data fusion, object detection and recognition, and image segmentation. This Special Issue highlights cutting-edge research and innovative solutions in these areas, contributing to the advancement of remote sensing image analysis, which is in alignment with the scope of Remote Sensing.

We encourage submissions of both regular research papers and reviews on topics, including, but not limited to, the following:

Machine and deep learning models in remote sensing;
Image processing and computer vision;
RGB, multispectral, and hyperspectral imaging;
Thermal and LiDAR imagery data;
Advanced remote sensing applications;
Large language models for remote sensing;
The application of generative AI in remote sensing imagery;
Big data and predictive analytics.

Dr. Keshav D. Singh
Dr. Abdul Bais
Dr. Saeid Homayouni
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 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

imagery data analysis
remote sensing
machine learning
deep learning
computer vision
exploiting big data
HPC and predictive analytics
multi-source/sensor data fusion
object detection and recognition
image segmentation
large language models
generative AI

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Published Papers (4 papers)

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Research

24 pages, 5693 KB

Open AccessArticle

From Geometric Alignment to Scale Balance: Directional Strip Convolution and Efficient Scale Fusion for Remote Sensing Ship Detection

by Jing Sun, Guoyou Shi, Yaxin Yang and Xiaolian Cheng

Remote Sens. 2026, 18(6), 873; https://doi.org/10.3390/rs18060873 - 12 Mar 2026

Viewed by 234

Abstract

Optical remote sensing ship detection faces significant challenges in realistic maritime scenes due to strong background clutter (e.g., docks, shorelines, wake streaks), extreme scale variation, and the elongated geometry of ships with diverse orientations. These factors frequently lead to geometric misalignment, unstable localization, and false alarms, particularly in congested ports and complex sea states. To enhance robustness under clutter while retaining the set prediction efficiency of DETR, we propose the Directional Efficient Network (DENet), a structure-aware enhancement built upon RT-DETR. DENet introduces two complementary components. First, Directional Strip Convolution (DSConv) replaces the standard

3 \times 3

convolution for spatial mixing. By predicting offsets conditioned on input features, DSConv performs strip aggregation that aligns with slender hull structures, thereby suppressing interference from line-shaped background patterns. Second, Efficient Scale Fusion (ESF) augments the Hybrid Encoder as an additive residual correction. It combines multiple receptive field branches with lightweight differential compensation to balance low-frequency context and high-frequency structural transitions, ensuring stable multi-scale fusion in cluttered scenes. Extensive experiments demonstrate the effectiveness of DENet. On ShipRSImageNet,

{AP}^{val}

improves from 58.8% to 63.2% and

{AP}_{50}^{val}

increases from 68.5% to 73.6%. Consistent gains are also observed on NWPU VHR-10, where

{AP}^{val}

reaches 63.0% and

{AP}_{50}^{val}

reaches 94.6%, alongside improvements on the Infrared Ship Database and VisDrone2019-DET, validating the method’s generalization capabilities. Full article

(This article belongs to the Special Issue Advances in Deep Learning and Machine Learning for Remote Sensing Image Analysis)

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26 pages, 29834 KB

Open AccessArticle

Self-Training Based Image–Text Multimodal Unsupervised Domain Adaptation Segmentation Model for Remote Sensing Images

by Qianqian Liu and Xili Wang

Remote Sens. 2026, 18(4), 651; https://doi.org/10.3390/rs18040651 - 20 Feb 2026

Viewed by 388

Abstract

Deep self-training-based unsupervised domain adaptation (UDA) semantic segmentation methods learn from labeled source domain images and unlabeled target domain images, performing more stably than those based on adversarial training. We propose a self-training-based image–text multimodal unsupervised domain adaptation semantic segmentation model (SIT-UDA) for remote sensing images. Unlike UDA methods, which rely solely on images, SIT-UDA enhances generalization performance by integrating category hint information from textual descriptions with image data to segment images. SIT-UDA employs a teacher–student self-training framework consisting of two components: the teacher multimodal segmentation model, which predicts pseudo-labels for target domain images, and the student multimodal segmentation model, which is trained to learn feature representations from both the source and target domains with guidance from the teacher model. To enhance the adaptability of image–text pretrained models in remote sensing domains, SIT-UDA introduces text prompt tuning to optimize the text features in the student model, and two learning strategies are proposed to optimize the model’s training objectives: One is the entropy-guided pixel-level weighting (EGPW) strategy, which adaptively weights the loss obtained by self-training on target domain images, leveraging the pseudo-labels rationally according to the entropy value at the pixel level. The other is the contrastive text constraint (CTC) strategy, which maximizes the similarity of text features for the same category between teacher and student models while minimizing the similarity of text features across different categories, improving text feature discriminability to promote cross-domain image–text alignment. Experiments in various domain adaptation scenarios among three remote sensing datasets (Potsdam, Vaihingen and LoveDA) demonstrate that the SIT-UDA is superior to the comparative domain adaptation semantic segmentation methods in terms of qualitative and quantitative segmentation results. Full article

(This article belongs to the Special Issue Advances in Deep Learning and Machine Learning for Remote Sensing Image Analysis)

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29 pages, 6009 KB

Open AccessArticle

Mamba-Based Infrared and Visible Images Fusion Method

by Jinsong He, Jianghua Cheng, Tong Liu, Bang Cheng, Xiaoyi Pan and Yahui Cai

Remote Sens. 2026, 18(4), 636; https://doi.org/10.3390/rs18040636 - 18 Feb 2026

Viewed by 418

Abstract

Visible-infrared image fusion is crucial for applications like autonomous driving and nighttime surveillance, yet it remains challenging due to the inherent limitations of existing deep learning models. Convolutional Neural Networks (CNNs) are constrained by their local receptive fields, while Transformers suffer from quadratic computational complexity. To address these issues, this paper investigates the application of the Mamba model—a novel State Space Model (SSM) with linear-complexity global modeling and selective scanning capabilities—to the task of visible-infrared image fusion. Building upon Mamba, we propose a novel fusion framework featuring two key designs: (1) A Multi-Path Mamba (MPMamba) module that orchestrates parallel Mamba blocks with convolutional streams to extract multi-scale, modality-specific features; and (2) a Dual-path Mamba Attention Fusion (DMAF) module that explicitly decouples and processes shared and complementary features via dual Mamba paths, followed by dynamic calibration with a Convolutional Block Attention Module (CBAM). Extensive experiments on the MSRS benchmark demonstrate that our framework achieves state-of-the-art performance, outperforming strong baselines such as U2Fusion and SwinFusion across key metrics including Information Entropy (EN), Spatial Frequency (SF), Mutual Information (MI), and edge-based fusion quality (Qabf). Visual results confirm its ability to produce fused images that saliently preserve thermal targets while retaining rich texture details. Full article

(This article belongs to the Special Issue Advances in Deep Learning and Machine Learning for Remote Sensing Image Analysis)

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22 pages, 5361 KB

Open AccessArticle

LMVMamba: A Hybrid U-Shape Mamba for Remote Sensing Segmentation with Adaptation Fine-Tuning

by Fan Li, Xiao Wang, Haochen Wang, Hamed Karimian, Juan Shi and Guozhen Zha

Remote Sens. 2025, 17(19), 3367; https://doi.org/10.3390/rs17193367 - 5 Oct 2025

Cited by 1 | Viewed by 2140

Abstract

High-precision semantic segmentation of remote sensing imagery is crucial in geospatial analysis. It plays an immeasurable role in fields such as urban governance, environmental monitoring, and natural resource management. However, when confronted with complex objects (such as winding roads and dispersed buildings), existing semantic segmentation methods still suffer from inadequate target recognition capabilities and multi-scale representation issues. This paper proposes a neural network model, LMVMamba (LoRA Multi-scale Vision Mamba), for semantic segmentation of remote sensing images. This model integrates the advantages of convolutional neural networks (CNNs), Transformers, and state-space models (Mamba) with a multi-scale feature fusion strategy. It simultaneously captures global contextual information and fine-grained local features. Specifically, in the encoder stage, the ResT Transformer serves as the backbone network, employing a LoRA fine-tuning strategy to effectively enhance model accuracy by training only the introduced low-rank matrix pairs. The extracted features are then passed to the decoder, where a U-shaped Mamba decoder is designed. In this stage, a Multi-Scale Post-processing Block (MPB) is introduced, consisting of depthwise separable convolutions and residual concatenation. This block effectively extracts multi-scale features and enhances local detail extraction after the VSS block. Additionally, a Local Enhancement and Fusion Attention Module (LAS) is added at the end of each decoder block. LAS integrates the SimAM attention mechanism, further enhancing the model’s multi-scale feature fusion capability and local detail segmentation capability. Through extensive comparative experiments, it was found that LMVMamba achieves superior performance on the OpenEarthMap dataset (mIoU 52.3%, OA 69.8%, mF1: 68.0%) and LoveDA (mIoU 67.9%, OA 80.3%, mF1: 80.5%) datasets. Ablation experiments validated the effectiveness of each module. The final results indicate that this model is highly suitable for high-precision land-cover classification tasks in remote sensing imagery. LMVMamba provides an effective solution for precise semantic segmentation of high-resolution remote sensing imagery. Full article

(This article belongs to the Special Issue Advances in Deep Learning and Machine Learning for Remote Sensing Image Analysis)

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