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Deep Learning-Based Analysis of High-Resolution Remote Sensing Images: Registration, Fusion, and Change Detection

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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: 31 August 2026 | Viewed by 2663

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Special Issue Editor

Dr. Shikai Jiang

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Guest Editor

Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: optical engineering; remote sensing; image processing

Special Issue Information

Dear Colleagues,

Multi-temporal optical remote sensing change detection analyzes images from different time periods, overcoming radiometric and geometric differences to extract effective surface change information. Its scientific significance and practical value make it irreplaceable for disaster assessment and the dynamic monitoring of urban planning and environmental evolution. The core challenge lies in achieving precise registration, efficient fusion, and distinguishing real changes from false alarms in order to extract useful surface change information.

This Special Issue aims to collect the latest breakthroughs in research on deep learning for the analysis of high-resolution remote sensing images, focusing on the three core tasks: registration, fusion, and change detection. It will promote the development of a new generation of intelligent processing algorithms that can address the challenges posed by massive, multi-source, and heterogeneous remote sensing data. The goal is to enhance the automation and intelligent interpretation capabilities of Earth observation systems and provide precise decision-making support for resource monitoring, urban planning, and sustainable development.

This Special Issue welcomes original research and reviews on topics including, but not limited to, the following:

Image Registration: Deep learning-based feature extraction and matching, non-rigid registration, cross-modal/cross-temporal image registration, unsupervised and weakly supervised registration.
Image Fusion: Pansharpening, multi-modal fusion (e.g., optical-SAR), spatio-temporal fusion, fusion techniques based on generative models.
Change Detection: Deep networks based on difference feature mining, direct comparison, or decision-level fusion; generalizable models addressing seasonal and illumination variations; few-shot and weakly supervised change detection; 3D change analysis.

Dr. Shikai Jiang
Guest Editor

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Keywords

deep learning
high-resolution remote sensing images
image registration
image fusion
change detection
multi-modal data
computer vision for remote sensing

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Published Papers (5 papers)

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Research

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31 pages, 7021 KB

Open AccessArticle

TMAFNet: A Transformer-Based Multi-Level Adaptive Fusion Network for Remote Sensing Change Detection

by Yushuai Yuan, Zhiyong Fan, Shuai Zhang, Min Xia and Yalu Huang

Remote Sens. 2026, 18(8), 1143; https://doi.org/10.3390/rs18081143 - 12 Apr 2026

Viewed by 181

Abstract

High-resolution remote sensing imagery encompasses complex land cover types and rich textural details, whilst temporal variations often manifest as subtle feature differences and unstable structural patterns. This renders traditional change detection methods ineffective at accurately characterizing genuine alterations, frequently leading to underdetection, false positives, and ambiguous boundaries. To address these challenges, this paper proposes a Transformer-Based Multi-level Adaptive Fusion Network. It is built upon the DeepLabV3+ encoder–decoder framework, in which a shared-weight ResNet-101 is adopted as the backbone for dual-temporal feature extraction, with the final residual block of layer 4 cropped to extract deeper semantic features at a higher spatial resolution. The Adaptive Window–Attention Feature Fusion Module (AWAFM) adaptively models local and global differences across temporal phases, enhancing sensitivity to genuine changes. The Dual Strip Pool Fusion Module (DSPFM) enhances sensitivity to directional structural variations through horizontal and vertical strip pooling. The Progressive Multi-Scale Feature Fusion Module (PMFFM) progressively aggregates deep and shallow features via semantic residual transmission. To further suppress misleading suppression caused by complex textures, the Transformer-Enhanced Reverse Attention Fusion Module (TRAFM) explicitly models long-range dependencies, effectively mitigating false change responses. On the LEVIR-CD dataset, it achieves state-of-the-art performance, with a PA and an IoU of 92.36% and 90.13%, respectively. On the SYSU-CD dataset, PA and IoU reach 88.96% and 86.15%, demonstrating TMAFNet’s stability and superiority in scenarios involving complex ground surface disturbances, weak textural variations, and large-scale structural changes. Full article

(This article belongs to the Special Issue Deep Learning-Based Analysis of High-Resolution Remote Sensing Images: Registration, Fusion, and Change Detection)

32 pages, 21931 KB

Open AccessArticle

Harmonic Phenology Mapping: From Vegetation Indices to Field Delineation

by Filip Papić, Mario Miler, Damir Medak and Luka Rumora

Remote Sens. 2026, 18(7), 1011; https://doi.org/10.3390/rs18071011 - 27 Mar 2026

Viewed by 453

Abstract

Operational agricultural monitoring in the Central European lowlands requires timely parcel boundaries; however, unmarked field edges produce minimal spectral contrast in single-date imagery. Previous works demonstrated that harmonic NDVI encoding enables zero-shot field delineation using foundational models, but the influence of the spectral index choice on temporal boundaries remained unquantified. This study systematically evaluates eleven vegetation indices—NDVI, GNDVI, NDRE, EVI, EVI2, SAVI, MSAVI, NDWI, CIg, CIre, and NDYVI—within a fixed harmonic phenology encoding pipeline. A one-year PlanetScope time series (15 × 15 km, Slavonija, Croatia) was decomposed via annual sinusoidal regression to extract per-pixel phase, amplitude, and mean parameters. These harmonic descriptors were mapped to HSV colour channels and segmented using the Segment Anything Model without fine-tuning. Official agricultural parcels (PAAFRD, 2025) provided ground truth for pixel-wise, object-wise, and size-stratified evaluation. Performance stratified into three tiers based on object-wise metrics. Soil-adjusted and enhanced-greenness indices (MSAVI, EVI, EVI2, and SAVI) achieved F1 = 0.51–0.52, and mIoU = 0.70–0.71, statistically outperforming standard ratio formulations (NDVI: F1 = 0.49) and chlorophyll indices (CIg, CIre: F1 = 0.45–0.47). Pixel-wise scores remained compressed (F1 > 0.88 across all indices), indicating consistent interior coverage but index-dependent boundary precision. Error analysis revealed scale-dependent patterns: merging dominated small parcels (<10,000 m²), while fragmentation increased with parcel size. Results demonstrate that spectral formulation is a systematic design factor in phenology-based delineation, with soil background correction and dynamic range compression improving seasonal trajectory separability. The harmonic parameters generated by this framework provide feature-ready input for crop classification, suggesting that integrated boundary extraction and crop mapping workflows merit further investigation. Full article

(This article belongs to the Special Issue Deep Learning-Based Analysis of High-Resolution Remote Sensing Images: Registration, Fusion, and Change Detection)

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28 pages, 11762 KB

Open AccessArticle

A Coarse-to-Fine Optical-SAR Image Registration Algorithm for UAV-Based Multi-Sensor Systems Using Geographic Information Constraints and Cross-Modal Feature Consistency Mapping

by Xiaoyong Sun, Zhen Zuo, Xiaojun Guo, Xuan Li, Peida Zhou, Runze Guo and Shaojing Su

Remote Sens. 2026, 18(5), 683; https://doi.org/10.3390/rs18050683 - 25 Feb 2026

Viewed by 410

Abstract

Optical and synthetic aperture radar (SAR) image registration faces challenges from nonlinear radiometric distortions and geometric deformations caused by different imaging mechanisms. This paper proposes a coarse-to-fine registration algorithm integrating geographic information constraints with cross-modal feature consistency mapping. The coarse stage employs imaging geometry-based coordinate transformation with airborne navigation data to eliminate scale and rotation differences. The fine stage constructs a multi-scale phase congruency-based feature response aggregation model combined with rotation-invariant descriptors and global-to-local search for sub-pixel alignment. Experiments on integrated airborne optical/SAR datasets demonstrate superior performance with an average RMSE of 2.00 pixels, outperforming both traditional handcrafted methods (3MRS, OS-SIFT, POS-GIFT, GLS-MIFT) and state-of-the-art deep learning approaches (SuperGlue, LoFTR, ReDFeat, SAROptNet) while reducing execution time by 37.0% compared with the best-performing baseline. The proposed coarse registration also serves as an effective preprocessing module that improves SuperGlue’s matching rate by 167% and LoFTR’s by 109%, with a hybrid refinement strategy achieving 1.95 pixels RMSE. The method demonstrates robust performance under challenging conditions, enabling real-time UAV-based multi-sensor fusion applications. Full article

(This article belongs to the Special Issue Deep Learning-Based Analysis of High-Resolution Remote Sensing Images: Registration, Fusion, and Change Detection)

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29 pages, 2310 KB

Open AccessArticle

Lightweight Unsupervised Homography Estimation for Infrared and Visible Images Based on UAV Perspective Enabling Real-Time Processing in Space–Air–Ground Integrated Network

by Yanhao Liao, Yinhui Luo, Jide Qian, Yuezhou Wu, Chengqi Li and Hongming Chen

Remote Sens. 2025, 17(23), 3884; https://doi.org/10.3390/rs17233884 - 29 Nov 2025

Viewed by 892

Abstract

Homography estimation of infrared and visible light images is a key visual technique that enables drones to perceive their environment and perform autonomous localization in low-altitude environments. Its potential lies in integration with edge computing and 5G technologies, enabling real-time control of drones within air–ground integrated networks. However, research on homography estimation techniques for low-altitude dynamic viewpoints remains scarce. Additionally, images in low-altitude scenarios suffer from issues such as blurring and jitter, presenting new challenges for homography estimation tasks. To address these issues, this paper proposes a light-weight homography estimation method, LFHomo, comprising two components: two anti-blurring feature extractors with non-shared parameters and a lightweight homography estimator, LFHomoE. The anti-blurring feature extractors introduce in-verse residual layers and feature displacement modules to capture sufficient contextual information in blurred regions and to enable lossless and rapid propagation of feature information. In addition, a spatial-reduction-based channel shuffle and spatial joint attention module is designed to suppress redundant features introduced by lossless transmission, allowing efficient extraction and refinement of informative features at low computational cost. The homography estimator LFHomoE adopts a CNN–GNN hybrid architecture to efficiently model geometric relationships between cross-modal features and to achieve fast prediction of homography matrices. Meanwhile, we construct and annotate an unregistered infrared and visible image dataset from drone perspectives for model training and evaluation. Experimental results show that LFHomo maintains great registration accuracy while significantly reducing model size and inference time. Full article

(This article belongs to the Special Issue Deep Learning-Based Analysis of High-Resolution Remote Sensing Images: Registration, Fusion, and Change Detection)

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Review

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40 pages, 3667 KB

Open AccessReview

Deep Learning Methods for SAR and Optical Image Fusion: A Review

by Chengyan Guo, Zhiyuan Zhang, Kexin Huang, Lan Luo, Ziqing Yang, Shuyun Shi and Junpeng Shi

Remote Sens. 2026, 18(8), 1196; https://doi.org/10.3390/rs18081196 - 16 Apr 2026

Viewed by 262

Abstract

Synthetic Aperture Radar (SAR) and optical image fusion technology plays a crucial role in remote sensing applications. It effectively combines the high spatial resolution and rich spectral information of optical images with the all-weather and penetrating observation advantages of SAR images, thereby significantly enhancing image interpretation accuracy and task execution capabilities. This paper systematically reviews deep learning-based fusion methods for SAR and optical images, with a particular focus on recent advances in deep learning models. Furthermore, it summarizes commonly used evaluation metrics for assessing fusion image quality, providing a basis for comparing and analyzing the performance of different methods. In addition, commonly used SAR-optical fusion datasets are briefly reviewed to highlight their roles in algorithm development and performance evaluation. Unlike conventional review articles, this paper further analyzes the guidance and supporting role of fusion algorithms from the perspective of typical and specific applications. Finally, it identifies key challenges and issues faced by current fusion methods, including data registration, model lightweight design, and multimodal feature alignment, and offers perspectives on future research directions. This review aims to provide routes and references for the development of SAR and optical image fusion technology. Full article

(This article belongs to the Special Issue Deep Learning-Based Analysis of High-Resolution Remote Sensing Images: Registration, Fusion, and Change Detection)

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