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Weakly Supervised Deep Learning in Exploiting Remote Sensing Big Data
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Weakly Supervised Deep Learning in Exploiting Remote Sensing Big Data

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

Deadline for manuscript submissions: closed (15 August 2024) | Viewed by 14844

Special Issue Editors

Prof. Dr. Yansheng Li

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: knowledge graph; deep learning; big data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Xu Tang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Xidian University, Xi’an 710071, China
Interests: remote sensing image processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Tian Tian

E-Mail Website
Guest Editor
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: remote sensing image classification; object detection and recognition; multispectral image processing
Dr. Zhihui Zhu

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Denver, Denver, CO 80208, USA
Interests: data science; machine learning; signal processing; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As well known, the current remote sensing data acquisition capability can fully meet the requirements of various applications, but the extraction of useful information from remote sensing big data still requires a large research effort. The emerging deep learning methodologies have been introduced in the remote sensing community to mine data, information and knowledge from remote sensing big data, and have achieved better performance than the traditional handcrafted feature-based methods and shallow neural networks. However, there is still a barrier that hinders the use of deep learning in remote sensing applications. One of the major challenges is related to the generation of high-quality labels for samples to be used for the training of deep learning algorithms. Weakly supervised deep learning (WSDL) is a promising solution to address this problem as WSDL can utilize greedily labeled datasets that are easy to collect but not ideal to complete deep network training. To systematically promote cost-effective information extraction from remote sensing big data, this Special Issue aims to collect the achievements around remote sensing big data mining based on WSDL.

This Special Issue aims to collect and discuss various applications of remote sensing big data with WSDL. The Special Issue may cover but is not limited to the following topics: WSDL theory; WSDL-driven remote sensing image retrieval, WSDL-driven remote sensing image object detection, WSDL-driven remote sensing image classification, WSDL-driven remote sensing change detection, and so forth.

Articles may address, but are not limited to, the following topics:

  • Deep learning under coarse labels
  • Deep learning under noisy labels
  • Knowledge graph-guided deep learning
  • WSDL-driven remote sensing image retrieval
  • WSDL-driven remote sensing image classification
  • WSDL-driven remote sensing image object detection
  • WSDL-driven remote sensing image change detection
  • WSDL-driven remote sensing image vectorization

Dr. Yansheng Li
Dr. Xu Tang
Dr. Tian Tian
Dr. Zhihui Zhu
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 under coarse labels
  • deep learning under noisy labels
  • knowledge graph-guided deep learning
  • remote sensing image retrieval
  • remote sensing image classification
  • remote sensing image segmentation
  • remote sensing image object detection
  • remote sensing image change detection
  • remote sensing image vectorization

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

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28 pages, 20281 KiB  
Article
Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite Imagery
by Seth Goodman, Ariel BenYishay and Daniel Runfola
Remote Sens. 2024, 16(18), 3411; https://doi.org/10.3390/rs16183411 - 13 Sep 2024
Viewed by 770
Abstract
As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development [...] Read more.
As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development indicators such as poverty, road quality, and conflict. This article builds on existing work leveraging satellite imagery and machine learning for estimation or prediction, to explore the potential to extend these methods temporally. Using Landsat 8 imagery and data from the Armed Conflict Location & Event Data Project (ACLED) we produce subnational predictions of the risk of conflict fatalities in Nigeria during 2015, 2017, and 2019 using distinct models trained on both yearly and six-month windows of data from the preceding year. We find that predictions at conflict sites leveraging imagery from the preceding year for training can predict conflict fatalities in the following year with an area under the receiver operating characteristic curve (AUC) of over 75% on average. While models consistently outperform a baseline comparison, and performance in individual periods can be strong (AUC > 80%), changes based on ground conditions such as the geographic scope of conflict can degrade performance in subsequent periods. In addition, we find that training models using an entire year of data slightly outperform models using only six months of data. Overall, the findings suggest CNN-based methods are moderately effective at detecting features in Landsat satellite imagery associated with the risk of fatalities from conflict events across time periods. Full article
(This article belongs to the Special Issue Weakly Supervised Deep Learning in Exploiting Remote Sensing Big Data)
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18 pages, 2642 KiB  
Article
An Unsupervised CNN-Based Pansharpening Framework with Spectral-Spatial Fidelity Balance
by Matteo Ciotola, Giuseppe Guarino and Giuseppe Scarpa
Remote Sens. 2024, 16(16), 3014; https://doi.org/10.3390/rs16163014 - 16 Aug 2024
Viewed by 764
Abstract
In recent years, deep learning techniques for pansharpening multiresolution images have gained increasing interest. Due to the lack of ground truth data, most deep learning solutions rely on synthetic reduced-resolution data for supervised training. This approach has limitations due to the statistical mismatch [...] Read more.
In recent years, deep learning techniques for pansharpening multiresolution images have gained increasing interest. Due to the lack of ground truth data, most deep learning solutions rely on synthetic reduced-resolution data for supervised training. This approach has limitations due to the statistical mismatch between real full-resolution and synthetic reduced-resolution data, which affects the models’ generalization capacity. Consequently, there has been a shift towards unsupervised learning frameworks for pansharpening deep learning-based techniques. Unsupervised schemes require defining sophisticated loss functions with at least two components: one for spectral quality, ensuring consistency between the pansharpened image and the input multispectral component, and another for spatial quality, ensuring consistency between the output and the panchromatic input. Despite promising results, there has been limited investigation into the interaction and balance of these loss terms to ensure stability and accuracy. This work explores how unsupervised spatial and spectral consistency losses can be reliably combined preserving the outocome quality. By examining these interactions, we propose a general rule for balancing the two loss components to enhance the stability and performance of unsupervised pansharpening models. Experiments on three state-of-the-art algorithms using WorldView-3 images demonstrate that methods trained with the proposed framework achieve good performance in terms of visual quality and numerical indexes. Full article
(This article belongs to the Special Issue Weakly Supervised Deep Learning in Exploiting Remote Sensing Big Data)
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19 pages, 7525 KiB  
Article
Remote Sensing and Deep Learning to Understand Noisy OpenStreetMap
by Munazza Usmani, Francesca Bovolo and Maurizio Napolitano
Remote Sens. 2023, 15(18), 4639; https://doi.org/10.3390/rs15184639 - 21 Sep 2023
Cited by 1 | Viewed by 1600
Abstract
The OpenStreetMap (OSM) project is an open-source, community-based, user-generated street map/data service. It is the most popular project within the state of the art for crowdsourcing. Although geometrical features and tags of annotations in OSM are usually precise (particularly in metropolitan areas), there [...] Read more.
The OpenStreetMap (OSM) project is an open-source, community-based, user-generated street map/data service. It is the most popular project within the state of the art for crowdsourcing. Although geometrical features and tags of annotations in OSM are usually precise (particularly in metropolitan areas), there are instances where volunteer mapping is inaccurate. Despite the appeal of using OSM semantic information with remote sensing images, to train deep learning models, the crowdsourced data quality is inconsistent. High-resolution remote sensing image segmentation is a mature application in many fields, such as urban planning, updated mapping, city sensing, and others. Typically, supervised methods trained with annotated data may learn to anticipate the object location, but misclassification may occur due to noise in training data. This article combines Very High Resolution (VHR) remote sensing data with computer vision methods to deal with noisy OSM. This work deals with OSM misalignment ambiguity (positional inaccuracy) concerning satellite imagery and uses a Convolutional Neural Network (CNN) approach to detect missing buildings in OSM. We propose a translating method to align the OSM vector data with the satellite data. This strategy increases the correlation between the imagery and the building vector data to reduce the noise in OSM data. A series of experiments demonstrate that our approach plays a significant role in (1) resolving the misalignment issue, (2) instance-semantic segmentation of buildings with missing building information in OSM (never labeled or constructed in between image acquisitions), and (3) change detection mapping. The good results of precision (0.96) and recall (0.96) demonstrate the viability of high-resolution satellite imagery and OSM for building detection/change detection using a deep learning approach. Full article
(This article belongs to the Special Issue Weakly Supervised Deep Learning in Exploiting Remote Sensing Big Data)
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29 pages, 5342 KiB  
Article
Meta-Knowledge Guided Weakly Supervised Instance Segmentation for Optical and SAR Image Interpretation
by Man Chen, Yao Zhang, Enping Chen, Yahao Hu, Yifei Xie and Zhisong Pan
Remote Sens. 2023, 15(9), 2357; https://doi.org/10.3390/rs15092357 - 29 Apr 2023
Cited by 3 | Viewed by 1739
Abstract
The interpretation of optical and synthetic aperture radar (SAR) images in remote sensing is general for many tasks, such as environmental monitoring, marine management, and resource planning. Instance segmentation of optical and SAR images, which can simultaneously provide instance-level localization and pixel-level classification [...] Read more.
The interpretation of optical and synthetic aperture radar (SAR) images in remote sensing is general for many tasks, such as environmental monitoring, marine management, and resource planning. Instance segmentation of optical and SAR images, which can simultaneously provide instance-level localization and pixel-level classification of objects of interest, is a crucial and challenging task in image interpretation. Considering that most current methods for instance segmentation of optical and SAR images rely on expensive pixel-level annotation, we develop a weakly supervised instance segmentation (WSIS) method to balance the visual processing requirements with the annotation cost. First, we decompose the prior knowledge of the mask-aware task in WSIS into three meta-knowledge components: fundamental knowledge, apparent knowledge, and detailed knowledge inspired by human visual perception habits of “whole to part” and “coarse to detailed.” Then, a meta-knowledge-guided weakly supervised instance segmentation network (MGWI-Net) is proposed. In this network, the weakly supervised mask (WSM) head can instantiate both fundamental knowledge and apparent knowledge to perform mask awareness without any annotations at the pixel level. The network also includes a mask information awareness assist (MIAA) head, which can implicitly guide the network to learn detailed information about edges through the boundary-sensitive feature of the fully connected conditional random field (CRF), facilitating the instantiation of detailed knowledge. The experimental results show that the MGWI-Net can efficiently generate instance masks for optical and SAR images and achieve the approximate instance segmentation results of the fully supervised method with about one-eighth of the annotation production time. The model parameters and processing speed of our network are also competitive. This study can provide inexpensive and convenient technical support for applying and promoting instance segmentation methods for optical and SAR images. Full article
(This article belongs to the Special Issue Weakly Supervised Deep Learning in Exploiting Remote Sensing Big Data)
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20 pages, 11947 KiB  
Article
Leveraging Self-Paced Semi-Supervised Learning with Prior Knowledge for 3D Object Detection on a LiDAR-Camera System
by Pei An, Junxiong Liang, Xing Hong, Siwen Quan, Tao Ma, Yanfei Chen, Liheng Wang and Jie Ma
Remote Sens. 2023, 15(3), 627; https://doi.org/10.3390/rs15030627 - 20 Jan 2023
Cited by 4 | Viewed by 2556
Abstract
Three dimensional (3D) object detection with an optical camera and light detection and ranging (LiDAR) is an essential task in the field of mobile robot and autonomous driving. The current 3D object detection method is based on deep learning and is data-hungry. Recently, [...] Read more.
Three dimensional (3D) object detection with an optical camera and light detection and ranging (LiDAR) is an essential task in the field of mobile robot and autonomous driving. The current 3D object detection method is based on deep learning and is data-hungry. Recently, semi-supervised 3D object detection (SSOD-3D) has emerged as a technique to alleviate the shortage of labeled samples. However, it is still a challenging problem for SSOD-3D to learn 3D object detection from noisy pseudo labels. In this paper, to dynamically filter the unreliable pseudo labels, we first introduce a self-paced SSOD-3D method SPSL-3D. It exploits self-paced learning to automatically adjust the reliability weight of the pseudo label based on its 3D object detection loss. To evaluate the reliability of the pseudo label in accuracy, we present prior knowledge based SPSL-3D (named as PSPSL-3D) to enhance the SPSL-3D with the semantic and structure information provided by a LiDAR-camera system. Extensive experimental results in the public KITTI dataset demonstrate the efficiency of the proposed SPSL-3D and PSPSL-3D. Full article
(This article belongs to the Special Issue Weakly Supervised Deep Learning in Exploiting Remote Sensing Big Data)
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Review

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34 pages, 3652 KiB  
Review
A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images
by Xuan Wang, Lijun Sun, Abdellah Chehri and Yongchao Song
Remote Sens. 2023, 15(20), 5062; https://doi.org/10.3390/rs15205062 - 21 Oct 2023
Cited by 18 | Viewed by 5734
Abstract
High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution image into a corresponding high-resolution image by a specific [...] Read more.
High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution image into a corresponding high-resolution image by a specific algorithm. With the emergence and swift advancement of generative adversarial networks (GANs), image super-resolution reconstruction is experiencing a new era of progress. Unfortunately, there has been a lack of comprehensive efforts to bring together the advancements made in the field of super-resolution reconstruction using generative adversarial networks. Hence, this paper presents a comprehensive overview of the super-resolution image reconstruction technique that utilizes generative adversarial networks. Initially, we examine the operational principles of generative adversarial networks, followed by an overview of the relevant research and background information on reconstructing remote sensing images through super-resolution techniques. Next, we discuss significant research on generative adversarial networks in high-resolution image reconstruction. We cover various aspects, such as datasets, evaluation criteria, and conventional models used for image reconstruction. Subsequently, the super-resolution reconstruction models based on generative adversarial networks are categorized based on whether the kernel blurring function is recognized and utilized during training. We provide a brief overview of the utilization of generative adversarial network models in analyzing remote sensing imagery. In conclusion, we present a prospective analysis of forthcoming research directions pertaining to super-resolution reconstruction methods that rely on generative adversarial networks. Full article
(This article belongs to the Special Issue Weakly Supervised Deep Learning in Exploiting Remote Sensing Big Data)
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