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Remote Sensing
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Machine Learning and Image Processing for Object Detection
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Machine Learning and Image Processing for Object Detection

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

Deadline for manuscript submissions: 15 December 2024 | Viewed by 8441

Special Issue Editors

Prof. Dr. Weifeng Liu

E-Mail Website
Guest Editor
College of Information and Control Engineering, China University of Petroleum (East China), #66 Changjiang West Road, Huangdao District, Qingdao 266580, China
Interests: machine learning; manifold learning; representation learning; multiview learning; image classification; remote sensing image analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Igor García Olaizola

E-Mail Website
Guest Editor
VICOMTech, Donostia-San, Sebastian, Spain
Interests: communications and signal processing; image sensors; radiation thermometry; machine/deep learning and artificial intelligence in sensing and imaging; sensor data in industry
Dr. Bingfeng Zhang

E-Mail Website
Guest Editor
Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK
Interests: computer vision; pattern recognition; object detection

Special Issue Information

Dear Colleagues,

Object detection is one of the most challenging and fundamental topics in computer vision. Aiming to locate instances of objects in images, object detection has witnessed increasing attention in this decade. Recently, with the rapid development of machine learning and image processing technologies, object detection has achieved remarkable performance, leading to a wide range of applications, such as 3D object detection in autonomous driving and small object detection in remoting sense images. Many advanced machine learning algorithms (e.g., deep learning) and image processing technologies have been proposed to handle different cases of object detection, but there are still several main intractable problems when applying object detection in the real world, especially for processing remoting sensing images—for example, how to detect unknown/untrained classes in remote sensing images, how to train using limited annotations or unbalanced data distribution, how to process remote sensing images related to small objects, how to achieve better-quality measurements for remote sensing images, how to achieve effective detection for 3D objects in remote sensing images, etc.

This Special Issue seeks original contributions of pioneer researchers addressing the abovementioned key problems and issues. We will only accept submissions related to machine learning and image processing for object detection related to remote sensing images. The topics of interest include (but are not limited to):

  • New machine learning algorithms for remote sensing image object detection;
  • Quality measurement for object detection in remote sensing images;
  • New deep neural networks for remote sensing image object detection;
  • Open-world object detection in remote sensing images;
  • Small object detection related to remote sensing images;
  • 3D object detection in remote sensing images;
  • Long-tailed object detection in remote sensing images;
  • Object detection on drone imagery;
  • Image augmentation or pre-processing for remote sensing image object detection;
  • Post-processing approaches for object detection in remote sensing images.

Prof. Dr. Weifeng Liu
Dr. Igor García Olaizola
Dr. Bingfeng 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

  • machine learning algorithm
  • image processing
  • deep learning
  • object detection
  • quality measurement
  • 3D objects
  • pre/post-processing
  • drone imagery

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Published Papers (4 papers)

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Research

29 pages, 13207 KiB  
Article
Dual-Structure Elements Morphological Filtering and Local Z-Score Normalization for Infrared Small Target Detection against Heavy Clouds
by Lingbing Peng, Zhi Lu, Tao Lei and Ping Jiang
Remote Sens. 2024, 16(13), 2343; https://doi.org/10.3390/rs16132343 - 27 Jun 2024
Cited by 1 | Viewed by 596
Abstract
Infrared (IR) small target detection in sky scenes is crucial for aerospace, border security, and atmospheric monitoring. Most current works are typically designed for generalized IR scenes, which may not be optimal for the specific scenario of sky backgrounds, particularly for detecting small [...] Read more.
Infrared (IR) small target detection in sky scenes is crucial for aerospace, border security, and atmospheric monitoring. Most current works are typically designed for generalized IR scenes, which may not be optimal for the specific scenario of sky backgrounds, particularly for detecting small and dim targets at long ranges. In these scenarios, the presence of heavy clouds usually causes significant false alarms due to factors such as strong edges, streaks, large undulations, and isolated floating clouds. To address these challenges, we propose an infrared dim and small target detection algorithm based on morphological filtering with dual-structure elements. First, we design directional dual-structure element morphological filters, which enhance the grayscale difference between the target and the background in various directions, thus highlighting the region of interest. The grayscale difference is then normalized in each direction to mitigate the interference of false alarms in complex cloud backgrounds. Second, we employ a dynamic scale awareness strategy, effectively preventing the loss of small targets near cloud edges. We enhance the target features by multiplying and fusing the local response values in all directions, which is followed by threshold segmentation to achieve target detection results. Experimental results demonstrate that our method achieves strong detection performance across various complex cloud backgrounds. Notably, it outperforms other state-of-the-art methods in detecting targets with a low signal-to-clutter ratio (MSCR ≤ 2). Furthermore, the algorithm does not rely on specific parameter settings and is suitable for parallel processing in real-time systems. Full article
(This article belongs to the Special Issue Machine Learning and Image Processing for Object Detection)
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19 pages, 115797 KiB  
Article
Design of a Multimodal Detection System Tested on Tea Impurity Detection
by Zhankun Kuang, Xiangyang Yu, Yuchen Guo, Yefan Cai and Weibin Hong
Remote Sens. 2024, 16(9), 1590; https://doi.org/10.3390/rs16091590 - 29 Apr 2024
Viewed by 677
Abstract
A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time [...] Read more.
A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu’er tea were obtained by this system and used for pixel classification. The experimental results showed that the accuracy of a support vector machine (SVM) model based on combined features was 93%, which was 7% higher than that based on spectral features only. By applying a median filtering algorithm and a contour detection algorithm to the label matrix extracted from pixel-classified images, except hair, eight impurities were detected successfully. Moreover, taking advantage of the high resolution of a visible light camera, small impurities could be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it was proved that the system could obtain multiple images to allow a more detailed and comprehensive understanding of the detected items and had an excellent ability to detect small impurities. Full article
(This article belongs to the Special Issue Machine Learning and Image Processing for Object Detection)
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22 pages, 21804 KiB  
Article
Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall
by Mitra Nasimi and Richard L. Wood
Remote Sens. 2024, 16(7), 1130; https://doi.org/10.3390/rs16071130 - 23 Mar 2024
Viewed by 1191
Abstract
Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is [...] Read more.
Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deep learning-based automated detection and advanced image processing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method’s effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation. Full article
(This article belongs to the Special Issue Machine Learning and Image Processing for Object Detection)
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29 pages, 3920 KiB  
Article
A Lightweight Object Detection Algorithm for Remote Sensing Images Based on Attention Mechanism and YOLOv5s
by Pengfei Liu, Qing Wang, Huan Zhang, Jing Mi and Youchen Liu
Remote Sens. 2023, 15(9), 2429; https://doi.org/10.3390/rs15092429 - 5 May 2023
Cited by 17 | Viewed by 4429
Abstract
The specific characteristics of remote sensing images, such as large directional variations, large target sizes, and dense target distributions, make target detection a challenging task. To improve the detection performance of models while ensuring real-time detection, this paper proposes a lightweight object detection [...] Read more.
The specific characteristics of remote sensing images, such as large directional variations, large target sizes, and dense target distributions, make target detection a challenging task. To improve the detection performance of models while ensuring real-time detection, this paper proposes a lightweight object detection algorithm based on an attention mechanism and YOLOv5s. Firstly, a depthwise-decoupled head (DD-head) module and spatial pyramid pooling cross-stage partial GSConv (SPPCSPG) module were constructed to replace the coupled head and the spatial pyramid pooling-fast (SPPF) module of YOLOv5s. A shuffle attention (SA) mechanism was introduced in the head structure to enhance spatial attention and reconstruct channel attention. A content-aware reassembly of features (CARAFE) module was introduced in the up-sampling operation to reassemble feature points with similar semantic information. In the neck structure, a GSConv module was introduced to maintain detection accuracy while reducing the number of parameters. Experimental results on remote sensing datasets, RSOD and DIOR, showed an improvement of 1.4% and 1.2% in mean average precision accuracy compared with the original YOLOv5s algorithm. Moreover, the algorithm was also tested on conventional object detection datasets, PASCAL VOC and MS COCO, which showed an improvement of 1.4% and 3.1% in mean average precision accuracy. Therefore, the experiments showed that the constructed algorithm not only outperformed the original network on remote sensing images but also performed better than the original network on conventional object detection images. Full article
(This article belongs to the Special Issue Machine Learning and Image Processing for Object Detection)
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