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Pattern Recognition and Image Processing for Remote Sensing (3rd Edition)

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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 (28 November 2024) | Viewed by 12264

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Special Issue Editors

Dr. Zhengxia Zou

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Guest Editor

School of Astronautics, Beihang University, Beijing 102206, China
Interests: computer vision and related applications in remote sensing; self-driving; video games
Special Issues, Collections and Topics in MDPI journals

Dr. Bin Pan

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Guest Editor

School of Statistics and Data Science, Nankai University, Tianjin 300071, China
Interests: hyperspectral image processing; remote sensing; machine learning
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Dr. Xia Xu

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Guest Editor

School of Computer Science, Nankai University, Tianjin 300350, China
Interests: hyperspectral unmixing; remote sensing image processing; multi-objective optimization
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Dr. Zhou Zhang

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Guest Editor

Department of Biological Systems Engineering, University of Wisconsin-Madison, 230 Agricultural Engineering Building, 460 Henry Mall, Madison, WI 53706, USA
Interests: hyperspectral remote sensing; machine learning; unmanned aerial vehicle (UAV)-based imaging platform developments; precision agriculture; high-throughput plant phenotyping
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Special Issue Information

Dear Colleagues,

This is the third volume of the Special Issue “Pattern Recognition and Image Processing for Remote Sensing”, which has been a great success.

Remote sensing provides a global perspective and a wealth of data about earth systems, allowing us to visualize and analyze objects and features on the earth’s surface. Today, pattern recognition and image processing technologies are revolutionizing earth observation and presenting unprecedented opportunities and challenges. Despite recent progress, there are still some open problems and challenges, such as deep learning with multi-modal and multi-resolution remote sensing images, light-weight processing for large-scale data, domain adaptation, and data fusion.

To answer these questions, this Special Issue focuses on presenting the latest advances in pattern recognition and image processing. We invite you to submit papers with methodological contributions and innovative applications. All types of image modalities are encouraged, such as multispectral imaging, hyperspectral imaging, synthetic aperture radar (SAR), multi-temporal imaging, LIDAR, etc. The platform is also unrestricted, and sensing can be carried using drones, aircraft, satellites, robots, etc. Any other applications related to remote sensing are welcome. The potential topics may include, but are not limited to, the following:

Pattern recognition and machine learning;
Deep learning;
Image classification, object detection, and image segmentation;
Change detection;
Image synthesis;
Multi-modal data fusion from different sensors;
Image quality improvement;
Real-time processing of remote sensing data;
Unsupervised learning and self-supervised learning;
Advanced deep learning techniques (e.g., generative adversarial networks, diffusion probabilistic models, and physics-informed neural networks);
Applications of remote sensing image in agriculture, marine, meteorology, and other fields.

Dr. Zhengxia Zou
Dr. Bin Pan
Dr. Xia Xu
Dr. Zhou Zhang
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 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

remote sensing
pattern recognition
image processing
machine learning
deep learning

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Related Special Issue

Pattern Recognition and Image Processing for Remote Sensing in Remote Sensing (7 articles)

Published Papers (9 papers)

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Research

19 pages, 8888 KiB

Open AccessArticle

LCMorph: Exploiting Frequency Cues and Morphological Perception for Low-Contrast Road Extraction in Remote Sensing Images

by Xin Li, Shumin Yang, Fan Meng, Wenlong Li, Zongchi Yang and Ruoyu Wei

Remote Sens. 2025, 17(2), 257; https://doi.org/10.3390/rs17020257 - 13 Jan 2025

Viewed by 413

Abstract

Road extraction in remote sensing images is crucial for urban planning, traffic navigation, and mapping. However, certain lighting conditions and compositional materials often cause roads to exhibit colors and textures similar to the background, leading to incomplete extraction. Additionally, the elongated and curved road morphology conflicts with the rectangular receptive field of traditional convolution. These challenges significantly affect the accuracy of road extraction in remote sensing images. To address these issues, we propose an end-to-end low-contrast road extraction network called LCMorph, which leverages both frequency cues and morphological perception. First, Frequency-Aware Modules (FAMs) are introduced in the encoder to extract frequency cues, effectively distinguishing low-contrast roads from the background. Subsequently, Morphological Perception Blocks (MPBlocks) are employed in the decoder to adaptively adjust the receptive field to the elongated and curved nature of roads. MPBlock relies on snake convolution, which mimics snakes’ twisting behavior for accurate road extraction. Experiments demonstrate that our method achieves state-of-the-art performance in terms of F1 score and IoU on the self-constructed low-contrast road dataset (LC-Roads), as well as the public DeepGlobe and Massachusetts Roads datasets. Full article

(This article belongs to the Special Issue Pattern Recognition and Image Processing for Remote Sensing (3rd Edition))

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19 pages, 19883 KiB

Open AccessArticle

SSOD-QCTR: Semi-Supervised Query Consistent Transformer for Optical Remote Sensing Image Object Detection

by Xinyu Ma, Pengyuan Lv and Xunqiang Gong

Remote Sens. 2024, 16(23), 4556; https://doi.org/10.3390/rs16234556 - 5 Dec 2024

Viewed by 656

Abstract

This paper proposes a semi-supervised query consistent transformer for optical remote sensing image object detection (SSOD-QCTR). A detection transformer (DETR)-like model is adopted as the basic network, and it follows the teacher–student training scheme. The proposed method makes three major contributions. Firstly, to consider the problem of inaccurate pseudo-labels generated in the initial training epochs, a dynamic geometry-aware-based intersection over union (DGAIoU) loss function is proposed to dynamically update the weight coefficients according to the quality of the pseudo-labels in the current epoch. Secondly, we propose an improved focal (IF) loss function, which deals with the category imbalance problem by decreasing the category probability coefficients of the major categories. Thirdly, to solve the problem of uncertain correspondence between the output of the teacher and student models caused by the random initialization of the object queries, a query consistency (QC)-based loss function is proposed to introduce a consistency constraint of the outputs of the two models by taking the same regions of interest extracted from the pseudo-labels as the input object query. Extensive exploratory experiments on two publicly available datasets, DIOR and HRRSD, demonstrated that SSOD-QCTR outperforms the related methods, achieving a mAP of 65.28% and 81.73% for the DIOR and HRRSD datasets, respectively. Full article

(This article belongs to the Special Issue Pattern Recognition and Image Processing for Remote Sensing (3rd Edition))

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22 pages, 15409 KiB

Open AccessArticle

A Deformable Split Fusion Method for Object Detection in High-Resolution Optical Remote Sensing Image

by Qinghe Guan, Ying Liu, Lei Chen, Guandian Li and Yang Li

Remote Sens. 2024, 16(23), 4487; https://doi.org/10.3390/rs16234487 - 29 Nov 2024

Viewed by 502

Abstract

To better address the challenges of complex backgrounds, varying object sizes, and arbitrary orientations in remote sensing object detection tasks, this paper proposes a deformable split fusion method based on an improved RoI Transformer called RoI Transformer-DSF. Specifically, the deformable split fusion method contains a deformable split module (DSM) and a space fusion module (SFM). Firstly, the DSM aims to assign different receptive fields according to the size of the remote sensing object and focus the feature attention on the remote sensing object to capture richer semantic and contextual information. Secondly, the SFM can highlight the spatial location of the remote sensing object and fuse spatial information of different scales to improve the detection ability of the algorithm for objects of different sizes. In addition, this paper presents the ResNext_Feature Calculation_block (ResNext_FC_block) to build the backbone of the algorithm and modifies the original regression loss to the KFIoU to improve the feature extraction capability and regression accuracy of the algorithm. Experiments show that the mAP_0.5 of this method on DOTAv1.0 and FAIR1M (plane) datasets is 83.53% and 44.14%, respectively, which is 3% and 1.87% higher than that of the RoI Transformer, and it can be applied to the field of remote sensing object detection. Full article

(This article belongs to the Special Issue Pattern Recognition and Image Processing for Remote Sensing (3rd Edition))

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19 pages, 3558 KiB

Open AccessArticle

SCCMDet: Adaptive Sparse Convolutional Networks Based on Class Maps for Real-Time Onboard Detection in Unmanned Aerial Vehicle Remote Sensing Images

by Qifan Tan, Xuqi Yang, Cheng Qiu, Yanhuan Jiang, Jinze He, Jingshuo Liu and Yahui Wu

Remote Sens. 2024, 16(6), 1031; https://doi.org/10.3390/rs16061031 - 14 Mar 2024

Viewed by 1422

Abstract

Onboard, real-time object detection in unmaned aerial vehicle remote sensing (UAV-RS) has always been a prominent challenge due to the higher image resolution required and the limited computing resources available. Due to the trade-off between accuracy and efficiency, the advantages of UAV-RS are difficult to fully exploit. Current sparse-convolution-based detectors only convolve some of the meaningful features in order to accelerate the inference speed. However, the best approach to the selection of meaningful features, which ultimately determines the performance, is an open question. This study proposes the use of adaptive sparse convolutional networks based on class maps for real-time onboard detection in UAV-RS images (SCCMDet) to solve this problem. For data pre-processing, SCCMDet obtains the real class maps as labels from the ground truth to supervise the feature selection process. In addition, a generate class map network (GCMN), equipped with a newly designed loss function, identifies the importance of features to generate a binary class map which filters the image for its more meaningful sparse features. Comparative experiments were conducted on the VisDrone dataset, and the experimental results show that our method accelerates YOLOv8 by 41.94% at most and increases the performance by 2.52%. Moreover, ablation experiments demonstrate the effectiveness of the proposed model. Full article

(This article belongs to the Special Issue Pattern Recognition and Image Processing for Remote Sensing (3rd Edition))

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29 pages, 11740 KiB

Open AccessArticle

Learning Point Processes and Convolutional Neural Networks for Object Detection in Satellite Images

by Jules Mabon, Mathias Ortner and Josiane Zerubia

Remote Sens. 2024, 16(6), 1019; https://doi.org/10.3390/rs16061019 - 13 Mar 2024

Viewed by 1392

Abstract

Convolutional neural networks (CNN) have shown great results for object-detection tasks by learning texture and pattern-extraction filters. However, object-level interactions are harder to grasp without increasing the complexity of the architectures. On the other hand, Point Process models propose to solve the detection of the configuration of objects as a whole, allowing the factoring in of the image data and the objects’ prior interactions. In this paper, we propose combining the information extracted by a CNN with priors on objects within a Markov Marked Point Process framework. We also propose a method to learn the parameters of this Energy-Based Model. We apply this model to the detection of small vehicles in optical satellite imagery, where the image information needs to be complemented with object interaction priors because of noise and small object sizes. Full article

(This article belongs to the Special Issue Pattern Recognition and Image Processing for Remote Sensing (3rd Edition))

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25 pages, 63267 KiB

Open AccessArticle

WBIM-GAN: A Generative Adversarial Network Based Wideband Interference Mitigation Model for Synthetic Aperture Radar

by Xiaoyu Xu, Weiwei Fan, Siyao Wang and Feng Zhou

Remote Sens. 2024, 16(5), 910; https://doi.org/10.3390/rs16050910 - 4 Mar 2024

Cited by 2 | Viewed by 1284

Abstract

Wideband interference (WBI) can significantly reduce the image quality and interpretation accuracy of synthetic aperture radar (SAR). To eliminate the negative effects of WBI on SAR, we propose a novel end-to-end data-driven approach to mitigate WBI. Specifically, the WBI is mitigated by an explicit function called WBI mitigation–generative adversarial network (WBIM-GAN), mapping from an input WBI-corrupted echo to its properly WBI-free echo. WBIM-GAN comprises a WBI mitigation network and a target echo discriminative network. The WBI mitigation network incorporates a deep residual network to enhance the performance of WBI mitigation while addressing the issue of gradient saturation in the deeper layers. Simultaneously, the class activation mapping technique fully demonstrates that the WBI mitigation network can localize the WBI region rather than the target echo. By utilizing the PatchGAN architecture, the target echo discriminative network can capture the local texture and statistical features of target echoes, thus improving the effectiveness of WBI mitigation. Before applying the WBIM-GAN, the short-time Fourier transform (STFT) converts SAR echoes into a time–frequency domain (TFD) to better characterize WBI features. Finally, by comparing different WBI mitigation methods applied to several real measured SAR data collected by the Sentinel-1 system, the efficiency and superiority of WBIM-GAN are proved sufficiently. Full article

(This article belongs to the Special Issue Pattern Recognition and Image Processing for Remote Sensing (3rd Edition))

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20 pages, 70021 KiB

Open AccessArticle

SSGAM-Net: A Hybrid Semi-Supervised and Supervised Network for Robust Semantic Segmentation Based on Drone LiDAR Data

by Hua Wu, Zhe Huang, Wanhao Zheng, Xiaojing Bai, Li Sun and Mengyang Pu

Remote Sens. 2024, 16(1), 92; https://doi.org/10.3390/rs16010092 - 25 Dec 2023

Cited by 1 | Viewed by 1997

Abstract

The semantic segmentation of drone LiDAR data is important in intelligent industrial operation and maintenance. However, current methods are not effective in directly processing airborne true-color point clouds that contain geometric and color noise. To overcome this challenge, we propose a novel hybrid learning framework, named SSGAM-Net, which combines supervised and semi-supervised modules for segmenting objects from airborne noisy point clouds. To the best of our knowledge, we are the first to build a true-color industrial point cloud dataset, which is obtained by drones and covers 90,000 m². Secondly, we propose a plug-and-play module, named the Global Adjacency Matrix (GAM), which utilizes only few labeled data to generate the pseudo-labels and guide the network to learn spatial relationships between objects in semi-supervised settings. Finally, we build our point cloud semantic segmentation network, SSGAM-Net, which combines a semi-supervised GAM module and a supervised Encoder–Decoder module. To evaluate the performance of our proposed method, we conduct experiments to compare our SSGAM-Net with existing advanced methods on our expert-labeled dataset. The experimental results show that our SSGAM-Net outperforms the current advanced methods, reaching

85.3 %

in mIoU, which ranges from

4.2

58.0 %

higher than other methods, achieving a competitive level. Full article

(This article belongs to the Special Issue Pattern Recognition and Image Processing for Remote Sensing (3rd Edition))

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17 pages, 4124 KiB

Open AccessArticle

A Generative Adversarial Network with Spatial Attention Mechanism for Building Structure Inference Based on Unmanned Aerial Vehicle Remote Sensing Images

by Hao Chen, Zhixiang Guo, Xing Meng and Fachuan He

Remote Sens. 2023, 15(18), 4390; https://doi.org/10.3390/rs15184390 - 6 Sep 2023

Viewed by 1407

Abstract

The acquisition of building structures has broad applications across various fields. However, existing methods for inferring building structures predominantly depend on manual expertise, lacking sufficient automation. To tackle this challenge, we propose a building structure inference network that utilizes UAV remote sensing images, with the PIX2PIX network serving as the foundational framework. We enhance the generator by incorporating an additive attention module that performs multi-scale feature fusion, enabling the combination of features from diverse spatial resolutions of the feature map. This modification enhances the model’s capability to emphasize global relationships during the mapping process. To ensure the completeness of line elements in the generator’s output, we design a novel loss function based on the Hough transform. A line penalty term is introduced that transforms the output of the generator and ground truth to the Hough domain due to the original loss function’s inability to effectively constrain the completeness of straight-line elements in the generated results in the spatial domain. A dataset of the appearance features obtained from UAV remote sensing images and the internal floor plan structure is made. Using UAV remote sensing images of multi-story residential buildings, high-rise residential buildings, and office buildings as test collections, the experimental results show that our method has better performance in inferring a room’s layout and the locations of load-bearing columns, achieving an average improvement of 11.2% and 21.1% over PIX2PIX in terms of the IoU and RMSE, respectively. Full article

(This article belongs to the Special Issue Pattern Recognition and Image Processing for Remote Sensing (3rd Edition))

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16 pages, 46124 KiB

Open AccessArticle

Lie Group Equivariant Convolutional Neural Network Based on Laplace Distribution

by Dengfeng Liao and Guangzhong Liu

Remote Sens. 2023, 15(15), 3758; https://doi.org/10.3390/rs15153758 - 28 Jul 2023

Cited by 1 | Viewed by 1862

Abstract

Traditional convolutional neural networks (CNNs) lack equivariance for transformations such as rotation and scaling. Consequently, they typically exhibit weak robustness when an input image undergoes generic transformations. Moreover, the complex model structure complicates the interpretation of learned low- and mid-level features. To address these issues, we introduce a Lie group equivariant convolutional neural network predicated on the Laplace distribution. This model’s Lie group characteristics blend multiple mid- and low-level features in image representation, unveiling the Lie group geometry and spatial structure of the Laplace distribution function space. It efficiently computes and resists noise while capturing pertinent information between image regions and features. Additionally, it refines and formulates an equivariant convolutional network appropriate for the Lie group feature map, maximizing the utilization of the equivariant feature at each level and boosting data efficiency. Experimental validation of our methodology using three remote sensing datasets confirms its feasibility and superiority. By ensuring a high accuracy rate, it enhances data utility and interpretability, proving to be an innovative and effective approach. Full article

(This article belongs to the Special Issue Pattern Recognition and Image Processing for Remote Sensing (3rd Edition))

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