Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Advanced Deep Learning Techniques for Information Extraction and Analysis of Remote Sensing Imagery

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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 July 2026 | Viewed by 6073

Share This Special Issue

Special Issue Editors

Dr. Hao Zhu

E-Mail Website
Guest Editor

School of Artificial Intelligence, Xidian University, Xi’an 710126, China
Interests: remote sensing image processing; deep learning; machine learning

Prof. Dr. Biao Hou

E-Mail Website
Guest Editor

School of Artificial Intelligence, Xidian University, Xi’an 710126, China
Interests: remote sensing image interpretation and object recognition; unmanned system collaborative perception; artificial intelligence chips and systems

Special Issue Information

Dear Colleagues,

Remote sensing images are captured through sensors mounted on aircraft and satellites, detecting electromagnetic radiation at different wavelengths, such as visible and LiDAR, reflected or emitted from the Earth's surface or atmosphere. This technology provides critical information about the physical properties and characteristics of observed areas. As a result, remote sensing images have found widespread applications in agriculture, environmental monitoring, disaster response, urban planning, and geological exploration. The rapid advancements in remote sensing technology for Earth observation have led to rapid growth in the scale, dimensions, resolution, and complexity of remote sensing data, demanding the more effective utilization of advanced deep learning techniques to extract and analyze meaningful information.

However, despite the significant breakthroughs achieved by using deep learning for the extraction and analysis of remote sensing data, thoroughly analyzing the semantic content of each remote sensing image is still confounded by significant challenges, e.g., the generalization capacity is limited in few-shot or zero-shot settings, the matching and fusion of multimodal images present significant difficulties, high computing and manual labeling costs, etc. To address these challenges, the community must continue to develop advanced technologies such as self-supervised learning, reinforcement learning, and large-scale foundational modeling to alleviate existing technical bottlenecks and further promote the broad application of remote sensing images.

This Special Issue encourages researchers to submit papers focused on leveraging advanced deep learning techniques to enhance the extraction and analysis of remote sensing information. We particularly welcome research on the latest advancements in theoretical methods and applied technologies, offering cutting-edge insights across various domains of remote sensing, including but not limited to the following topics:

Unsupervised, semi-supervised, weakly supervised, and self-supervised remote sensing image information extraction and analysis;
Few-shot or zero-shot remote sensing image representation;
Remote sensing image feature interpretation;
Remote sensing target detection;
Remote sensing image matching;
Multimodal remote sensing image fusion;
Remote sensing image segmentation;
Acceleration or compression of remote sensing image analysis models;
Other topics related to remote sensing image analysis applications.

This Special Issue will promote the use of advanced deep learning techniques to enhance the extraction and analysis of remote sensing information. This topic is included in the scope of Remote Sensing and is a popular research direction in the journal.

This Special Issue welcomes contributions related to the following topics:

Unsupervised, semi-supervised, weakly supervised, and self-supervised remote sensing image information extraction and analysis;
Few-shot or zero-shot remote sensing image representation;
Remote sensing image feature interpretation;
Remote sensing target detection;
Remote sensing image matching;
Multimodal remote sensing image fusion;
Remote sensing image segmentation;
Acceleration or compression of remote sensing image analysis models;
Other topics related to remote sensing image analysis applications.

Dr. Hao Zhu
Prof. Dr. Biao Hou
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

image fusion
image segmentation
object detection
model acceleration or compression
image matching

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

34 pages, 246563 KB

Open AccessArticle

Topology-Aware Field Parcel Delineation: Bridging Deep Semantic Features and Geometric Constraints

by Yaming Duan, Runze Zhao, Xiangde Xu and Jinshui Zhang

Remote Sens. 2026, 18(11), 1783; https://doi.org/10.3390/rs18111783 - 1 Jun 2026

Viewed by 161

Abstract

The accurate and automated delineation of Field Parcels (FPs) serves as the foundation for modern precision agriculture. While deep learning-based extraction from high-resolution remote sensing imagery has improved pixel-level accuracy, current methods often neglect the intrinsic topological relationships between parcels, leading to geometric inconsistencies such as broken boundaries and structural ambiguities. To address these limitations, this paper proposes a topology-aware, end-to-end framework for polygonal FP extraction. We employed Convolutional Neural Networks (CNNs) with a coupled boundary-region representation to extract deep features that implicitly encode boundary width. Crucially, we introduce a Topological Relationship Construction (TRC) mechanism that transforms raster features into a node-edge topological network, enabling the direct generation of vector entities with guaranteed spatial adjacency. Based on this topology, we further developed Double-Line Detection (DLD) and Dangling Line Extension (DLE) algorithms to resolve the topological absence of single/double-line boundaries and fixed fracture errors in complex scenarios. Experimental results demonstrate that the proposed method achieved an F1 score of 0.910 and an IoU of 0.835, effectively ensuring stable and geometrically reasonable outputs even when CNN predictions are fragmented. This approach provides a solution for end-to-end vector mapping in agriculture. Full article

(This article belongs to the Special Issue Advanced Deep Learning Techniques for Information Extraction and Analysis of Remote Sensing Imagery)

► Show Figures

Figure 1

29 pages, 2016 KB

Open AccessArticle

Comparison of Lightweight Deep Neural Networks for Landsat Time-Series Land Use and Land Cover Classification over the Conterminous United States

by Zhixin Wang, Giorgos Mountrakis and Ahmadreza Safaeinia

Remote Sens. 2026, 18(11), 1757; https://doi.org/10.3390/rs18111757 - 1 Jun 2026

Viewed by 190

Abstract

Accurate and timely land cover and land use (LCLU) classification from medium-spatial-resolution optical time-series data is essential for large-scale environmental monitoring. lightweight deep neural networks (DNNs) offer reduced computational and memory requirements, enabling efficient deployment in resource-constrained scenarios. While popular in computer vision tasks, their ability to simultaneously model spatial, spectral, and temporal information for medium-resolution optical time series is understudied. This study addresses this gap by evaluating seven existing lightweight models spanning four architectural families: convolutional and recurrent hybrids, convolutional and transformer hybrids, 3D convolutional models, and video transformers against a traditional hybrid convolutional transformer (CNNTransformer) benchmark across the Conterminous United States (CONUS). Models are trained on 500,000 Landsat time-series samples with 25 repetitions and evaluated across five model sizes (3k, 5k, 10k, 25k, and 50k parameters) to assess both accuracy and stability. Results show that Simple Recurrent Unit (SRU)-based lightweight hybrids provide the best performance. Specifically, MobileNetSRU consistently outperformed the benchmark at small-to-moderate model sizes (3k–15k), achieving peak relative improvement gains of ~2.5–7.5% at 7.5k parameters. MobileNetSRU also demonstrated superior robustness in limited-data scenarios (50k training samples), particularly for spectrally stable classes like water and bare land. This study reveals that the inherent inductive bias of recurrent-based lightweight models aligns more effectively with the sequential phenology of satellite data than more flexible, data-hungry attention mechanisms at small parameter scales. These findings suggest that strategically matching architectural priorities to temporal data structures can significantly reduce the trade-off between model efficiency and classification accuracy in scalable Earth-observation workflows. Full article

(This article belongs to the Special Issue Advanced Deep Learning Techniques for Information Extraction and Analysis of Remote Sensing Imagery)

► Show Figures

Figure 1

27 pages, 15933 KB

Open AccessArticle

DSFNet: A Directional Statistical Fusion Network for Cloud and Cloud Shadow Segmentation

by Yuqi Fang, Zhiyong Fan, Min Xia, Ni Li and Xiaolin Yang

Remote Sens. 2026, 18(9), 1432; https://doi.org/10.3390/rs18091432 - 4 May 2026

Viewed by 372

Abstract

Accurate cloud and cloud shadow segmentation is a critical prerequisite for remote sensing image preprocessing. However, this task remains challenging due to the directional continuity of projected cloud shadows, the radiometric ambiguity between low-reflectance shadows and other dark surfaces, and the difficulty of preserving semantic consistency and fine boundaries in complex scenes. To address these issues, this paper proposes a Directional Statistical Fusion Network (DSFNet) based on an enhanced DeepLabV3+ architecture. Specifically, a Directional Scale Refinement Module (DSRM) is introduced in parallel with Atrous Spatial Pyramid Pooling to strengthen the representation of direction-sensitive cloud-shadow structures and multi-scale cloud regions. An Adaptive Statistical Context Attention (ASCA) module is further designed to perform robust feature modulation by jointly exploiting global statistics, edge-aware statistics, and median-based normalization, thereby suppressing anomalous responses under heterogeneous backgrounds. In the decoder, an Adaptive Grouped Multi-scale Fusion (AGMF) module is employed to adaptively fuse shallow detail features and high-level semantic features through discrepancy-guided grouped gating, improving structural consistency and boundary recovery. In addition, a hybrid loss is adopted to further optimize segmentation. Experiments on the GF1_WHU dataset show that DSFNet achieves 76.97% mIoU, demonstrating strong effectiveness and robustness in complex remote sensing scenes. Full article

(This article belongs to the Special Issue Advanced Deep Learning Techniques for Information Extraction and Analysis of Remote Sensing Imagery)

► Show Figures

Figure 1

41 pages, 7837 KB

Open AccessArticle

Deep Learning Style Transfer for Enhanced Smoke Plume Visibility: A Standardized False Color Composite (SFCC) in GEMS Satellite Imagery

by Yemin Jeong, Seung Hee Kim, Menas Kafatos, Jeong-Ah Yu, Kyoung-Hee Sung, Sang-Min Kim, Seung-Yeon Kim, Goo Kim, Jae-Jin Kim and Yangwon Lee

Remote Sens. 2026, 18(3), 483; https://doi.org/10.3390/rs18030483 - 2 Feb 2026

Viewed by 663

Abstract

Wildfire smoke visualization using geostationary satellite imagery is essential for real-time monitoring and atmospheric analysis; however, inconsistencies in color tone across Geostationary Environment Monitoring Spectrometer (GEMS) images hinder reliable interpretation and model training. This study proposes a Standardized False Color Composite (SFCC) framework based on deep learning style transfer to enhance the visual consistency and interpretability of wildfire smoke scenes. Four tone-standardization methods were compared: the statistical Empirical Cumulative Distribution Function (ECDF) correction and three neural approaches—ReHistoGAN, StyTr², and Style Injection Diffusion Model (SI-DM). Each model was evaluated visually and quantitatively using six metrics (SSIM, LPIPS, FID, histogram similarity, ArtFID, and LSCI) and validated on three major wildfire events in Korea (2022–2025). Among the tested models, SI-DM achieved the most balanced performance, preserving structural features while ensuring consistent color-tone alignment (ArtFID = 1.620; LSCI mean = 0.894). Qualitative assessments further confirmed that SI-DM effectively delineated smoke boundaries and maintained natural background tones under complex atmospheric conditions. Additional analysis using GEMS UVAI, VISAI, and CHOCHO demonstrated that the styled composites partially reflect the optical and chemical characteristics distinguishing wildfire smoke from dust aerosols. The proposed SFCC framework establishes a foundation for visually standardized satellite smoke imagery and provides potential for future aerosol-type classification and automated detection applications. Full article

(This article belongs to the Special Issue Advanced Deep Learning Techniques for Information Extraction and Analysis of Remote Sensing Imagery)

► Show Figures

Figure 1

44 pages, 12626 KB

Open AccessArticle

Hyperspectral Image Segmentation for Optimal Satellite Operations: In-Orbit Deployment of 1D-CNN

by Jon Alvarez Justo, Dennis D. Langer, Simen Berg, Jens Nieke, Radu Tudor Ionescu, Per Gunnar Kjeldsberg and Tor Arne Johansen

Remote Sens. 2025, 17(4), 642; https://doi.org/10.3390/rs17040642 - 13 Feb 2025

Cited by 8 | Viewed by 3777

Abstract

AI on spaceborne platforms optimizes operations and increases automation, crucial for satellites with limited downlink capacity. It can ensure that only valuable information is transmitted, minimizing resources spent on unnecessary data, which is especially important in hyperspectral Earth Observation missions, producing large data volumes. Our previous work showed that the 1D-CNN, 1D-Justo-LiuNet, outperformed 2D-CNNs and Vision Transformers for hyperspectral segmentation with an accuracy of 0.93 and 4563 parameters, making our model the best choice for in-orbit deployment. While the state of the art has deployed 1D-CNNs on low-power platforms, such as Unmanned Aerial Vehicles, they have still not been deployed in space before. In this work, we mark the first deployment and testing of a 1D-CNN in a satellite. We implement a C version of the 1D-Justo-LiuNet and, after ground validation, we deploy it on board the HYPSO-1 satellite. We demonstrate in-flight segmentation of hyperspectral images via the 1D-CNN to classify pixels into sea, land, and cloud categories. We show how in-orbit segmentation improves satellite operations, increases automation, and optimizes downlink. We give examples of how in-orbit segmentation addresses mission challenges in HYPSO-1, such as incomplete data reception, incorrect satellite pointing, and cloud cover, helping to decide whether to transmit or discard data on board. An additional CNN autonomously interprets the segmented images, enabling on-board decisions on data downlink. Full article

(This article belongs to the Special Issue Advanced Deep Learning Techniques for Information Extraction and Analysis of Remote Sensing Imagery)

► Show Figures

Journal Menu

Journal Browser

Advanced Deep Learning Techniques for Information Extraction and Analysis of Remote Sensing Imagery

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI