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Special Issue "Deep Learning Based Target Detection and Recognition in Remote Sensing Images"

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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: 30 April 2023 | Viewed by 4593

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Special Issue Editors

Dr. Zongxu Pan

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: target detection; target recognition; target tracking; deep learning; remote sensing

Prof. Dr. Fan Zhang

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Guest Editor

1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
2. Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing 100029, China
Interests: image processing; artificial intelligence; remote sensing; high performance computing
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Dr. Xinghua Li

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Guest Editor

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: multi-temporal remote sensing; deep learning; change detection; sparse representation; image restoration
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Dr. Bo Tang

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Guest Editor

Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Interests: machine learning; remote sensing
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Dr. Wei Yao

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Guest Editor

German Aerospace Center, 82234 Wessling, Germany
Interests: data mining; image classification; image segmentation; data visualization

Dr. Zhongling Huang

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Guest Editor

School of Automation, Northwestern Polytechnical University, Xi'an 710021, China
Interests: SAR target recognition; transfer learning; unsupervised learning
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Special Issue Information

Dear Colleagues,

Target detection and recognition is a fundamental task in remote sensing, and it plays a significant role in various applications. Tradition algorithms use manually designed features whose representation capability is limited. With the further development of deep learning (DL) techniques, DL-based target detection and recognition approaches have become increasingly popular. Despite substantial progress in the field of DL-based detectors and classifiers with automatically learned features, there are several remaining issues: 1) the performance of tiny targets or target detection in low-resolution images is not satisfactory due to limited information; 2) target detection and recognition with few training samples is still a challenge; techniques such as transfer learning, weakly supervised learning, self-supervised learning and meta learning are possible solutions requiring investigation; 3) current target detection and recognition models are more like black boxes; their interpretability needs to be further studied in order to advance their development in remote sensing images.

This Special Issue aims to provide a platform for researchers to discuss and provide solutions for the above-mentioned issues, contributing to the development of target detection and recognition in remote sensing images.

Topics of interest include, but are not limited to:

Deep-learning-based target detection, tracking and recognition in visible remote sensing images, infrared remote sensing images or synthetic aperture radar images.
Advanced remote sensing target detection and recognition techniques for addressing issues including few-shot learning, tiny target detection, fine-grained target recognition, etc.
Land cover and land use classification, change detection of remote sensing images with one sensor or multiple sensors.
Investigations on the physical interpretability of target detection and recognition models in remote sensing images.

Dr. Zongxu Pan
Prof. Dr. Fan Zhang
Dr. Xinghua Li
Dr. Bo Tang
Dr. Wei Yao
Dr. Zhongling Huang
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 2500 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

target detection
target recognition
change detection
land and use and land cover classification
deep learning
remote sensing images

Published Papers (5 papers)

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Research

Open AccessArticle

An Anchor-Free Detection Algorithm for SAR Ship Targets with Deep Saliency Representation

and

Remote Sens. 2023, 15(1), 103; https://doi.org/10.3390/rs15010103 - 24 Dec 2022

Viewed by 704

Abstract

Target detection in synthetic aperture radar (SAR) images has a wide range of applications in military and civilian fields. However, for engineering applications involving edge deployment, it is difficult to find a suitable balance of accuracy and speed for anchor-based SAR image target detection algorithms. Thus, an anchor-free detection algorithm for SAR ship targets with deep saliency representation, called SRDet, is proposed in this paper to improve SAR ship detection performance against complex backgrounds. First, we design a data enhancement method considering semantic relationships. Second, the state-of-the-art anchor-free target detection framework CenterNet2 is used as a benchmark, and a new feature-enhancing lightweight backbone, called LWBackbone, is designed to reduce the number of model parameters while effectively extracting the salient features of SAR targets. Additionally, a new mixed-domain attention mechanism, called CNAM, is proposed to effectively suppress interference from complex land backgrounds and highlight the target area. Finally, we construct a receptive-field-enhanced detection head module, called RFEHead, to improve the multiscale perception performance of the detection head. Experimental results based on three large-scale SAR target detection datasets, SSDD, HRSID and SAR-ship-dataset, show that our algorithm achieves a better balance between ship target detection accuracy and speed and exhibits excellent generalization performance. Full article

(This article belongs to the Special Issue Deep Learning Based Target Detection and Recognition in Remote Sensing Images)

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Open AccessArticle

Novel Asymmetric Pyramid Aggregation Network for Infrared Dim and Small Target Detection

and

Remote Sens. 2022, 14(22), 5643; https://doi.org/10.3390/rs14225643 - 08 Nov 2022

Cited by 1 | Viewed by 639

Abstract

Robust and efficient detection of small infrared target is a critical and challenging task in infrared search and tracking applications. The size of the small infrared targets is relatively tiny compared to the ordinary targets, and the sizes and appearances of the these targets in different scenarios are quite different. Besides, these targets are easily submerged in various background noise. To tackle the aforementioned challenges, a novel asymmetric pyramid aggregation network (APANet) is proposed. Specifically, a pyramid structure integrating dual attention and dense connection is firstly constructed, which can not only generate attention-refined multi-scale features in different layers, but also preserve the primitive features of infrared small targets among multi-scale features. Then, the adjacent cross-scale features in these multi-scale information are sequentially modulated through pair-wise asymmetric combination. This mutual dynamic modulation can continuously exchange heterogeneous cross-scale information along the layer-wise aggregation path until an inverted pyramid is generated. In this way, the semantic features of lower-level network are enriched by incorporating local focus from higher-level network while the detail features of high-level network are refined by embedding point-wise focus from lower-level network, which can highlight small target features and suppress background interference. Subsequently, recursive asymmetric fusion is designed to further dynamically modulate and aggregate high resolution features of different layers in the inverted pyramid, which can also enhance the local high response of small target. Finally, a series of comparative experiments are conducted on two public datasets, and the experimental results show that the APANet can more accurately detect small targets compared to some state-of-the-art methods. Full article

(This article belongs to the Special Issue Deep Learning Based Target Detection and Recognition in Remote Sensing Images)

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Open AccessArticle

A New Ship Detection Algorithm in Optical Remote Sensing Images Based on Improved R³Det

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Remote Sens. 2022, 14(19), 5048; https://doi.org/10.3390/rs14195048 - 10 Oct 2022

Cited by 1 | Viewed by 857

Abstract

The task of ship target detection based on remote sensing images has attracted more and more attention because of its important value in civil and military fields. To solve the problem of low accuracy in ship target detection in optical remote sensing ship images due to complex scenes and large-target-scale differences, an improved R³Det algorithm is proposed in this paper. On the basis of R³Det, a feature pyramid network (FPN) structure is replaced by a search architecture-based feature pyramid network (NAS FPN) so that the network can adaptively learn and select the feature combination update and enrich the multiscale feature information. After the feature extraction network, a shallow feature is added to the context information enhancement (COT) module to supplement the small target semantic information. An efficient channel attention (ECA) module is added to make the network gather in the target area. The improved algorithm is applied to the ship data in the remote sensing image data set FAIR1M. The effectiveness of the improved model in a complex environment and for small target detection is verified through comparison experiments with R³Det and other models. Full article

(This article belongs to the Special Issue Deep Learning Based Target Detection and Recognition in Remote Sensing Images)

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Open AccessArticle

A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion

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Remote Sens. 2022, 14(18), 4583; https://doi.org/10.3390/rs14184583 - 14 Sep 2022

Cited by 2 | Viewed by 826

Abstract

Convolutional Neural Network (CNN) has been widely applied in the field of synthetic aperture radar (SAR) image recognition. Nevertheless, CNN-based recognition methods usually encounter the problem of poor feature representation ability due to insufficient labeled SAR images. In addition, the large inner-class variety and high cross-class similarity of SAR images pose a challenge for classification. To alleviate the problems mentioned above, we propose a novel few-shot learning (FSL) method for SAR image recognition, which is composed of the multi-feature fusion network (MFFN) and the weighted distance classifier (WDC). The MFFN is utilized to extract input images’ features, and the WDC outputs the classification results based on these features. The MFFN is constructed by adding a multi-scale feature fusion module (MsFFM) and a hand-crafted feature insertion module (HcFIM) to a standard CNN. The feature extraction and representation capability can be enhanced by inserting the traditional hand-crafted features as auxiliary features. With the aid of information from different scales of features, targets of the same class can be more easily aggregated. The weight generation module in WDC is designed to generate category-specific weights for query images. The WDC distributes these weights along the corresponding Euclidean distance to tackle the high cross-class similarity problem. In addition, weight generation loss is proposed to improve recognition performance by guiding the weight generation module. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and the Vehicle and Aircraft (VA) dataset demonstrate that our proposed method surpasses several typical FSL methods. Full article

(This article belongs to the Special Issue Deep Learning Based Target Detection and Recognition in Remote Sensing Images)

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Open AccessArticle

MEA-Net: A Lightweight SAR Ship Detection Model for Imbalanced Datasets

Yiyu Guo

and

Luoyu Zhou

Remote Sens. 2022, 14(18), 4438; https://doi.org/10.3390/rs14184438 - 06 Sep 2022

Cited by 1 | Viewed by 916

Abstract

The existing synthetic aperture radar (SAR) ship datasets have an imbalanced number of inshore and offshore ship targets, and the number of small, medium and large ship targets differs greatly. At the same time, the existing SAR ship detection models in the application have a huge structure and require high computing resources. To solve these problems, we propose a SAR ship detection model named mask efficient adaptive network (MEA-Net), which is lightweight and high-accuracy for imbalanced datasets. Specifically, we propose the following three innovative modules. Firstly, we propose a mask data balance augmentation (MDBA) method, which solves the imbalance of sample data between inshore and offshore ship targets by combining mathematical morphological processing and ship label data to greatly improve the ability of the model to detect inshore ship targets. Secondly, we propose an efficient attention mechanism (EAM), which effectively integrates channel features and spatial features through one-dimensional convolution and two-dimensional convolution, to improve the feature extraction ability of the model for SAR ship targets. Thirdly, we propose an adaptive receptive field block (ARFB), which can achieve more effective multi-scale detection by establishing the mapping relationship between the size of the convolution kernel and the channel of feature map, to improve the detection ability of the model for ship targets of different sizes. Finally, MEA-Net is deployed on the Jeston Nano edge computing device of the 2 GB version. We conducted experimental validation on the SSDD and HRSID datasets. Compared with the baseline, the AP of MEA-Net increased by 2.18% on the SSDD dataset and 3.64% on the HRSID dataset. The FLOPs and model parameters of MEA-Net were only 2.80 G and 0.96 M, respectively. In addition, the FPS reached 6.31 on the Jeston Nano, which has broad application prospects. Full article

(This article belongs to the Special Issue Deep Learning Based Target Detection and Recognition in Remote Sensing Images)

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