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Remote Sensing
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Recent Advances in Remote Sensing Image Processing Technology
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Recent Advances in Remote Sensing Image Processing Technology

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

Deadline for manuscript submissions: 25 November 2024 | Viewed by 2885

Special Issue Editors

Dr. Puhong Duan

E-Mail Website
Guest Editor
College of Electrical and Engineering, Hunan University, Changsha 41000, China
Interests: image processing; machine learning; feature extraction; image segmentation; object recognition
Dr. Xueqian Wang

E-Mail Website
Guest Editor
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Interests: target detection; information fusion; radar imaging; compressed sensing; dsitributed signal processing
Prof. Dr. Fulin Luo

E-Mail Website
Guest Editor
College of Computer Science, Chongqing University, Chongqing 400044, China
Interests: machine\deep learning foundations for remote sensing; remote sensing image classification; object detection; semantic segmentation; change detection; anomaly detection
Special Issues, Collections and Topics in MDPI journals
Dr. Junshi Xia

E-Mail Website
Guest Editor
Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
Interests: high-performance geo-computation; big earth data; data science
Special Issues, Collections and Topics in MDPI journals
Dr. Richard Gloaguen

E-Mail Website
Guest Editor
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Straße 40, 09599 Freiberg, Germany
Interests: hyperspectral imaging; mineral exploration; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing is an advanced technology used to acquire information about an object, area, or phenomenon without making physical contact with it. It involves the utilization of various sensors and instruments to capture data from a distance, typically from platforms such as satellites, aircraft, drones, or ground-based devices. In recent years, with the advancement of deep learning and machine learning methods, remote sensing technology has achieved great success, which has been widely applied in many areas, such as environmental monitoring, precision agriculture, and military reconnaissance. However, there are still many challenges in the practical applications of remote sensing technologies, such as their high computational complexity, poor transferability, and low interpretability. These challenges not only limit the applications of remote sensing images but also demand more innovative models. The objective of this Special Issue is to cover various applications, advanced algorithms, and models in the remote sensing field. We welcome related methods and applications that include but are not limited to the following:

  • Remote sensing image processing technologies;
  • Multi-source image fusion methods;
  • Feature extraction methods;
  • Image classification methods;
  • Change detection methods;
  • Object detection methods;
  • Various applications.

Dr. Puhong Duan
Dr. Xueqian Wang
Prof. Dr. Fulin Luo
Dr. Junshi Xia
Dr. Richard Gloaguen
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
  • image fusion
  • feature extraction
  • image classification
  • change detection
  • object detection

Published Papers (3 papers)

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Research

20 pages, 5552 KiB  
Article
Identification of Rare Wildlife in the Field Environment Based on the Improved YOLOv5 Model
by Xiaohui Su, Jiawei Zhang, Zhibin Ma, Yanqi Dong, Jiali Zi, Nuo Xu, Haiyan Zhang, Fu Xu and Feixiang Chen
Remote Sens. 2024, 16(9), 1535; https://doi.org/10.3390/rs16091535 - 26 Apr 2024
Viewed by 219
Abstract
Research on wildlife monitoring methods is a crucial tool for the conservation of rare wildlife in China. However, the fact that rare wildlife monitoring images in field scenes are easily affected by complex scene information, poorly illuminated, obscured, and blurred limits their use. [...] Read more.
Research on wildlife monitoring methods is a crucial tool for the conservation of rare wildlife in China. However, the fact that rare wildlife monitoring images in field scenes are easily affected by complex scene information, poorly illuminated, obscured, and blurred limits their use. This often results in unstable recognition and low accuracy levels. To address this issue, this paper proposes a novel wildlife identification model for rare animals in Giant Panda National Park (GPNP). We redesigned the C3 module of YOLOv5 using NAMAttention and the MemoryEfficientMish activation function to decrease the weight of field scene features. Additionally, we integrated the WIoU boundary loss function to mitigate the influence of low-quality images during training, resulting in the development of the NMW-YOLOv5 model. Our model achieved 97.3% for mAP50 and 83.3% for mAP50:95 in the LoTE-Animal dataset. When comparing the model with some classical YOLO models for the purpose of conducting comparison experiments, it surpasses the current best-performing model by 1.6% for mAP50:95, showcasing a high level of recognition accuracy. In the generalization ability test, the model has a low error rate for most rare wildlife species and is generally able to identify wildlife in the wild environment of the GPNP with greater accuracy. It has been demonstrated that NMW-YOLOv5 significantly enhances wildlife recognition accuracy in field environments by eliminating irrelevant features and extracting deep, effective features. Furthermore, it exhibits strong detection and recognition capabilities for rare wildlife in GPNP field environments. This could offer a new and effective tool for rare wildlife monitoring in GPNP. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing Image Processing Technology)
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18 pages, 13422 KiB  
Article
Remote Sensing Micro-Object Detection under Global and Local Attention Mechanism
by Yuanyuan Li, Zhengguo Zhou, Guanqiu Qi, Gang Hu, Zhiqin Zhu and Xin Huang
Remote Sens. 2024, 16(4), 644; https://doi.org/10.3390/rs16040644 - 09 Feb 2024
Viewed by 918
Abstract
With the rapid advancement of technology, satellite and drone technologies have had significant impacts on various fields, creating both opportunities and challenges. In areas like the military, urban planning, and environmental monitoring, the application of remote sensing technology is paramount. However, due to [...] Read more.
With the rapid advancement of technology, satellite and drone technologies have had significant impacts on various fields, creating both opportunities and challenges. In areas like the military, urban planning, and environmental monitoring, the application of remote sensing technology is paramount. However, due to the unique characteristics of remote sensing images, such as high resolution, large-scale scenes, and small, densely packed targets, remote sensing object detection faces numerous technical challenges. Traditional detection methods are inadequate for effectively detecting small targets, rendering the accurate and efficient detection of objects in complex remote sensing images a pressing issue. Current detection techniques fall short in accurately detecting small targets compared to medium and large ones, primarily due to limited feature information, insufficient contextual data, and poor localization capabilities for small targets. In response, we propose an innovative detection method. Unlike previous approaches that often focused solely on either local or contextual information, we introduce a novel Global and Local Attention Mechanism (GAL), providing an in-depth modeling method for input images. Our method integrates fine-grained local feature analysis with global contextual information processing. The local attention concentrates on details and spatial relationships within local windows, enabling the model to recognize intricate details in complex images. Meanwhile, the global attention addresses the entire image’s global information, capturing overarching patterns and structures, thus enhancing the model’s high-level semantic understanding. Ultimately, a specific mechanism fuses local details with global context, allowing the model to consider both aspects for a more precise and comprehensive interpretation of images. Furthermore, we have developed a multi-head prediction module that leverages semantic information at various scales to capture the multi-scale characteristics of remote sensing targets. Adding decoupled prediction heads aims to improve the accuracy and robustness of target detection. Additionally, we have innovatively designed the Ziou loss function, an advanced loss calculation, to enhance the model’s precision in small target localization, thereby boosting its overall performance in small target detection. Experimental results on the Visdrone2019 and DOTA datasets demonstrate that our method significantly surpasses traditional methods in detecting small targets in remote sensing imagery. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing Image Processing Technology)
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26 pages, 2291 KiB  
Article
End-to-End Convolutional Network and Spectral-Spatial Transformer Architecture for Hyperspectral Image Classification
by Shiping Li, Lianhui Liang, Shaoquan Zhang, Ying Zhang, Antonio Plaza and Xuehua Wang
Remote Sens. 2024, 16(2), 325; https://doi.org/10.3390/rs16020325 - 12 Jan 2024
Cited by 1 | Viewed by 829
Abstract
Although convolutional neural networks (CNNs) have proven successful for hyperspectral image classification (HSIC), it is difficult to characterize the global dependencies between HSI pixels at long-distance ranges and spectral bands due to their limited receptive domain. The transformer can compensate well for this [...] Read more.
Although convolutional neural networks (CNNs) have proven successful for hyperspectral image classification (HSIC), it is difficult to characterize the global dependencies between HSI pixels at long-distance ranges and spectral bands due to their limited receptive domain. The transformer can compensate well for this shortcoming, but it suffers from a lack of image-specific inductive biases (i.e., localization and translation equivariance) and contextual position information compared with CNNs. To overcome the aforementioned challenges, we introduce a simply structured, end-to-end convolutional network and spectral–spatial transformer (CNSST) architecture for HSIC. Our CNSST architecture consists of two essential components: a simple 3D-CNN-based hierarchical feature fusion network and a spectral–spatial transformer that introduces inductive bias information. The former employs a 3D-CNN-based hierarchical feature fusion structure to establish the correlation between spectral and spatial (SAS) information while capturing richer inductive bias and more discriminative local spectral-spatial hierarchical feature information, while the latter aims to establish the global dependency among HSI pixels while enhancing the acquisition of local information by introducing inductive bias information. Specifically, the spectral and inductive bias information is incorporated into the transformer’s multi-head self-attention mechanism (MHSA), thus making the attention spectrally aware and location-aware. Furthermore, a Lion optimizer is exploited to boost the classification performance of our newly developed CNSST. Substantial experiments conducted on three publicly accessible hyperspectral datasets unequivocally showcase that our proposed CNSST outperforms other state-of-the-art approaches. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing Image Processing Technology)
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