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Remote Sensing
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Advancements in AI-Based Remote Sensing Object Detection
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Advancements in AI-Based Remote Sensing Object Detection

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

Deadline for manuscript submissions: 15 August 2025 | Viewed by 3862

Special Issue Editors

Dr. Yu Oishi

E-Mail Website
Guest Editor
Core Technology Research Headquaters, National Agriculture and Food Research Organization, Tsukuba 305-0856, Japan
Interests: hyperspectral RS; plant disease diagnosis; animal remote sensing; cloud mask
Special Issues, Collections and Topics in MDPI journals
Dr. Daniele Pannone

E-Mail Website
Guest Editor
Department of Computer Science, Sapienza Università di Roma, Rome, Italy
Interests: machine and deep learning; event recognition; object detection; person re-identification; signal analysis and processing

Special Issue Information

Dear Colleagues,

In recent years, it has become possible to perform not only high-frequency observations using geostationary satellites but also remote sensing using small satellite constellations and drones. To perform monitoring using these, it is necessary to extract the changes and desired information from a large amount of data. Additionally, when photographing from above, the view is from above rather than from the side as we normally would, making visual inspections difficult. Remote sensing object detection is important for these reasons.

Meanwhile, applications of deep learning are popular in generic image recognition, although it is necessary to determine the features and their thresholds used for image recognition; for example, supervised deep learning models learn and determine them from training images. In the results, it is inferred that the features that have been difficult to formulate are also being used.

Although the aim of this Special Issue is AI-based remote sensing object detection, detection using classification models and segmentation models, change detection, and tracking are also acceptable. Moreover, accompanying technologies and applications are similarly welcome. This Special Issue welcomes techniques and experimental research articles on the following topics, although progress reports on relevant research issues are also acceptable.

Dr. Yu Oishi
Dr. Daniele Pannone
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

  • deep learning
  • image recognition
  • change detection
  • object detection
  • tracking
  • monitoring
  • new sensors
  • algorithms
  • applications

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Published Papers (3 papers)

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22 pages, 5264 KiB  
Article
Lightweight Neural Network for Centroid Detection of Weak, Small Infrared Targets via Background Matching in Complex Scenes
by Xiangdong Xu, Jiarong Wang, Zhichao Sha, Haitao Nie, Ming Zhu and Yu Nie
Remote Sens. 2024, 16(22), 4301; https://doi.org/10.3390/rs16224301 - 18 Nov 2024
Cited by 1 | Viewed by 1156
Abstract
In applications such as aerial object interception and ballistic estimation, it is crucial to precisely detect the centroid position of the target rather than to merely identify the position of the target bounding box or segment all pixels belonging to the target. Due [...] Read more.
In applications such as aerial object interception and ballistic estimation, it is crucial to precisely detect the centroid position of the target rather than to merely identify the position of the target bounding box or segment all pixels belonging to the target. Due to the typically long distances between targets and imaging devices in such scenarios, targets often exhibit a low contrast and appear as dim, obscure shapes in infrared images, which represents a challenge for human observation. To rapidly and accurately detect small targets, this paper proposes a lightweight, end-to-end detection network for small infrared targets. Unlike existing methods, the input of this network is five consecutive images after background matching. This design significantly improves the network’s ability to extract target motion features and effectively reduces the interference of static backgrounds. The network mainly consists of a local feature aggregation module (LFAM), which uses multiple-sized convolution kernels to capture multi-scale features in parallel and integrates multiple spatial attention mechanisms to achieve accurate feature fusion and effective background suppression, thereby enhancing the ability to detect small targets. To improve the accuracy of predicted target centroids, a centroid correction algorithm is designed. In summary, this paper presents a lightweight centroid detection network based on background matching for weak, small infrared targets. The experimental results show that, compared to directly inputting a sequence of images into the neural network, inputting a sequence of images processed by background matching can increase the detection rate by 9.88%. Using the centroid correction algorithm proposed in this paper can therefore improve the centroid localization accuracy by 0.0134. Full article
(This article belongs to the Special Issue Advancements in AI-Based Remote Sensing Object Detection)
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17 pages, 12643 KiB  
Article
Detecting Moving Wildlife Using the Time Difference between Two Thermal Airborne Images
by Yu Oishi, Natsuki Yoshida and Hiroyuki Oguma
Remote Sens. 2024, 16(8), 1439; https://doi.org/10.3390/rs16081439 - 18 Apr 2024
Cited by 2 | Viewed by 1568
Abstract
Wildlife damage to agriculture is serious in Japan; therefore, it is important to understand changes in wildlife population sizes. Although several studies have been conducted to detect wildlife from drone images, behavioral changes (such as wildlife escaping when a drone approaches) have been [...] Read more.
Wildlife damage to agriculture is serious in Japan; therefore, it is important to understand changes in wildlife population sizes. Although several studies have been conducted to detect wildlife from drone images, behavioral changes (such as wildlife escaping when a drone approaches) have been confirmed. To date, the use of visible and near-infrared images has been limited to the daytime because many large mammals, such as sika deer (Cervus nippon), are crepuscular. However, it is difficult to detect wildlife in the thermal images of urban areas that are not open and contain various heat spots. To address this issue, a method was developed in a previous study to detect moving wildlife using pairs of time-difference thermal images. However, the user’s accuracy was low. In the current study, two methods are proposed for extracting moving wildlife using pairs of airborne thermal images and deep learning models. The first method was to judge grid areas with wildlife using a deep learning classification model. The second method detected each wildlife species using a deep learning object detection model. The proposed methods were then applied to pairs of airborne thermal images. The classification test accuracies of “with deer” and “without deer” were >85% and >95%, respectively. The average precision of detection, precision, and recall were >85%. This indicates that the proposed methods are practically accurate for monitoring changes in wildlife populations and can reduce the person-hours required to monitor a large number of thermal remote-sensing images. Therefore, efforts should be made to put these materials to practical use. Full article
(This article belongs to the Special Issue Advancements in AI-Based Remote Sensing Object Detection)
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17 pages, 3957 KiB  
Technical Note
A VCA Combined MHSA-CNN for Aero-Engine Hot Jet Remote Sensing Mixed Spectral Feature Extraction
by Zhenping Kang, Yurong Liao, Xinyan Yang and Zhaoming Li
Remote Sens. 2025, 17(7), 1147; https://doi.org/10.3390/rs17071147 - 24 Mar 2025
Viewed by 190
Abstract
To address the challenges of spectral unmixing and feature extraction in the hot jet mixtures of two types of aero-engine hot jets, Fourier transform infrared spectrometry was employed to precisely measure these mixtures. The collected data encompassed the spectra of hot jets generated [...] Read more.
To address the challenges of spectral unmixing and feature extraction in the hot jet mixtures of two types of aero-engine hot jets, Fourier transform infrared spectrometry was employed to precisely measure these mixtures. The collected data encompassed the spectra of hot jets generated individually by the two distinct engine models, as well as those of the mutually mixed hot jets. In this paper, a mixed spectral unmixing algorithm based on VCA was put forward. Initially, the vertex component analysis (VCA) algorithm was utilized to decompose the mixed spectra. By comparing with the separately measured actual pure spectra, it was found that the mean RMSE of the hot jet pure spectra extracted by VCA for the two engines was 0.34846, and the mean SAM reached 0.00096, thus validating the effectiveness of the algorithm. Subsequently, the least squares (LS) algorithm was applied to ascertain the abundance values of the mixed spectra. Among the mixed samples, the average abundance values of the two pure spectra were 0.78 and 0.22, respectively. To further extract the spectral features after unmixing, an innovative one-dimensional convolutional multi-head self-attention mechanism neural network (MHSA-CNN) algorithm was devised in this study. This algorithm can accurately pinpoint the key wave crests of the features at 2282–2283 cm−1 and 2388–2389 cm−1. The research findings offer crucial technical backing for the intelligent fault diagnosis of aero-engines and contribute to enhancing the accuracy and reliability of engine operating condition monitoring. Full article
(This article belongs to the Special Issue Advancements in AI-Based Remote Sensing Object Detection)
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