Emerging Research in Target Detection and Recognition in Remote Sensing Images

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 2095

Special Issue Editors

Dr. Tao Tang
E-Mail Website1 Website2
Guest Editor
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410000, China
Interests: intelligent interpretation of remote sensing images; remote sensing image object detection; remote sensing image target recognition
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100000, China
Interests: synthetic aperture radar (SAR); polarimetric SAR image processing; remote sensing
College of Electronic science and Technology, National University of Defense Technology, Changsha 410000, China
Interests: remote sensing; pattern recognition; image processing; target detection

Special Issue Information

Dear Colleagues,

In the field of Earth observation, the massive remote sensing data obtained by a large number of in orbit satellites or aircrafts brings more observational information and processing challenges for remote sensing image interpretation. The detection, recognition, and tracking of various types of high-value artificial targets on the ground and sea has become one of the hotspots in the processing and application of Earth observation information. In recent years, a large amount of rapid target detection, recognition, and tracking methods based on artificial intelligence technology have brought many beneficial solutions to the processing of remote sensing Earth observation information, and have had a significant impact in the field of remote sensing. They have provided promising tools for solving many challenging issues in accuracy and reliability of target detection and recognition in remote sensing images.

In this special issue, we plan to compile a series of papers to report on new methods and technologies for object detection and recognition in remote sensing images that have emerged in recent years. We anticipate that new research will leverage new methods and technologies such as artificial intelligence to solve more practical problems in remote sensing image applications.

The article may cover, but is not limited to, the following topics:

  • Advanced artificial intelligence based object detection/recognition/tracking;
  • Remote sensing image change detection/semantic segmentation;
  • Remote sensing multi-sensor data fusion/multimodal data analysis;
  • Remote sensing image super-resolution/restoration;
  • Unsupervised/weak supervised learning for remote sensing image target detection or recognition;
  • Advanced artificial intelligence technology for remote sensing applications;
  • Intelligent detection and recognition of composite targets based on knowledge graphs or knowledge reasoning.

Dr. Tao Tang
Dr. Canbin Hu
Dr. Yuli Sun
Guest Editors

Manuscript Submission Information

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Keywords

  • remote sensing
  • image processing
  • image interpretation
  • target detection
  • classification/recognition
  • deep networks
  • knowledge graphs

Published Papers (2 papers)

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Research

21 pages, 4426 KiB  
Article
Improved Detection Method for Micro-Targets in Remote Sensing Images
Information 2024, 15(2), 108; https://doi.org/10.3390/info15020108 - 12 Feb 2024
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Abstract
With the exponential growth of remote sensing images in recent years, there has been a significant increase in demand for micro-target detection. Recently, effective detection methods for small targets have emerged; however, for micro-targets (even fewer pixels than small targets), most existing methods [...] Read more.
With the exponential growth of remote sensing images in recent years, there has been a significant increase in demand for micro-target detection. Recently, effective detection methods for small targets have emerged; however, for micro-targets (even fewer pixels than small targets), most existing methods are not fully competent in feature extraction, target positioning, and rapid classification. This study proposes an enhanced detection method, especially for micro-targets, in which a combined loss function (consisting of NWD and CIOU) is used instead of a singular CIOU loss function. In addition, the lightweight Content-Aware Reassembly of Features (CARAFE) replaces the original bilinear interpolation upsampling algorithm, and a spatial pyramid structure is added into the network model’s small target layer. The proposed algorithm undergoes training and validation utilizing the benchmark dataset known as AI-TOD. Compared to speed-oriented YOLOv7-tiny, the mAP0.5 and mAP0.5:0.95 of our improved algorithm increased from 42.0% and 16.8% to 48.7% and 18.9%, representing improvements of 6.7% and 2.1%, respectively, while the detection speed was almost equal to that of YOLOv7-tiny. Furthermore, our method was also tested on a dataset of multi-scale targets, which contains small targets, medium targets, and large targets. The results demonstrated that mAP0.5:0.95 increased from “9.8%, 54.8%, and 68.2%” to “12.6%, 55.6%, and 70.1%” for detection across different scales, indicating improvements of 2.8%, 0.8%, and 1.9%, respectively. In summary, the presented method improves detection metrics for micro-targets in various scenarios while satisfying the requirements of detection speed in a real-time system. Full article
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13 pages, 5546 KiB  
Article
Three-Stage MPViT-DeepLab Transfer Learning for Community-Scale Green Infrastructure Extraction
Information 2024, 15(1), 15; https://doi.org/10.3390/info15010015 - 26 Dec 2023
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Abstract
The extraction of community-scale green infrastructure (CSGI) poses challenges due to limited training data and the diverse scales of the targets. In this paper, we reannotate a training dataset of CSGI and propose a three-stage transfer learning method employing a novel hybrid architecture, [...] Read more.
The extraction of community-scale green infrastructure (CSGI) poses challenges due to limited training data and the diverse scales of the targets. In this paper, we reannotate a training dataset of CSGI and propose a three-stage transfer learning method employing a novel hybrid architecture, MPViT-DeepLab, to help us focus on CSGI extraction and improve its accuracy. In MPViT-DeepLab, a Multi-path Vision Transformer (MPViT) serves as the feature extractor, feeding both coarse and fine features into the decoder and encoder of DeepLabv3+, respectively, which enables pixel-level segmentation of CSGI in remote sensing images. Our method achieves state-of-the-art results on the reannotated dataset. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Remote Object Tracking Based on Optical Flow Reconstruction of Motion-Group Parameters
Authors: Simeon Karpuzov; George Petkov; Sylvia Ilieva; Alexander Petkov; Stiliyan Kalitzin
Affiliation: Image Sciences Institute, University Medical Center Utrecht
Abstract: Object tracking has significance in many applications ranging from control of unmanned vehicles to autonomous monitoring of specific situations and events, especially when providing safety for patients with specific adverse conditions such as epileptic seizures. Conventional tracking methods face many challenges, such as the need for dedicated attached devices or tags, high image noise, complex object movements and intensive computational requirements. Method. We propose a novel optical flow-based method for object tracking. It utilizes real-time image sequences from the camera and reconstructs global motion-group parameters of the content. A rectangular region of interest surrounding the moving object can be steered by these parameters to follow the target. The method successfully applies to multi-spectral data, further improving its effectiveness. Results. Experimental results on simulated tests and complex real-world data demonstrate the method's capabilities. The proposed optical flow reconstruction provides accurate, robust and faster results as compared to both pixel-level algorithms and segmentation-based approaches.

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