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Remote Sensing
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Recent Advances in Neural Network for Remote Sensing
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Recent Advances in Neural Network for Remote Sensing

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 18501

Special Issue Editors

Dr. Xiaoli Li

E-Mail Website
Guest Editor
Machine Intellection Department, Institute for Infocomm Research, Singapore 138632, Singapore
Interests: artificial intelligence; machine learning; medical sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Zhenghua Chen

E-Mail Website
Guest Editor
Institute for Infocomm Research (I2R), A*STAR, 1 Fusionopolis Way, Singapore 138632, Singapore
Interests: data analytics; deep learning; domain adaptation; self-supervised learning and related applications
Special Issues, Collections and Topics in MDPI journals
Dr. Min Wu

E-Mail Website
Guest Editor
Institute for Infocomm Research (I2R), A*STAR, 1 Fusionopolis Way, Singapore 138632, Singapore
Interests: data mining; machine learning; transfer learning; bioinformatics and health informatics; IoT data analytics; machine fault diagnosis and prognosis
Dr. Jianfei Yang

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore
Interests: artificial intelligence of things; transfer learning; deep learning; unsupervised learning and related applications

Special Issue Information

Dear Colleagues,

Remote sensing can benefit various applications, such as urban planning, geospatial object detection, military monitoring, vegetation mapping and precision agriculture. With the development of remoting sensing devices, such as unmanned aerial vehicle, more and more imaging data are available for analysis. Neural networks which plays a key role in imaging processing has achieved great success for remoting sensing with large amount of data. However, there are still many challenges that neural network-based remote sensing faces, such as data annotation, environmental noise (e.g., cloud noise and weather changes), heterogeneity (e.g., different imaging devices), etc. Recently, many advanced neural network techniques, such as convolutional neural network, self-supervised learning, domain adaptation, active learning, knowledge graph, etc., have been developed to solve various practical issues in image processing. However, few works have explored these recent advances in neural networks on solving challenging issues in remote sensing. The aim of the present Special Issue is to cover the relevant topics, trends, and best practices on recent advances in neural network for remote sensing, and also to introduce new practices in the field.

We would like to invite you to contribute by submitting articles on your recent research, experimental work, reviews, and/or case studies related to the field of recent advances in neural network for remote sensing. Contributions may be from, but not limited to, the following topics:

  • Convolutional neural network for remote sensing
  • Self-supervised learning
  • Active learning
  • Doman adaptation/generalization
  • Adversarial learning
  • Metric learning
  • Knowledge graphs
  • Multimodal learning
  • Explainable neural networks
  • Reinforcement learning
  • Representation learning

Dr. Xiaoli Li
Dr. Zhenghua Chen
Dr. Min Wu
Dr. Jianfei Yang
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

  • Convolutional neural network for remote sensing
  • Self-supervised learning
  • Active learning
  • Doman adaptation/generalization
  • Adversarial learning
  • Metric learning
  • Knowledge graphs
  • Multimodal learning
  • Explainable neural networks
  • Reinforcement learning
  • Representation learning

Published Papers (6 papers)

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Research

21 pages, 3616 KiB  
Article
A Swin Transformer-Based Encoding Booster Integrated in U-Shaped Network for Building Extraction
by Xiao Xiao, Wenliang Guo, Rui Chen, Yilong Hui, Jianing Wang and Hongyu Zhao
Remote Sens. 2022, 14(11), 2611; https://doi.org/10.3390/rs14112611 - 29 May 2022
Cited by 19 | Viewed by 5294
Abstract
Building extraction is a popular topic in remote sensing image processing. Efficient building extraction algorithms can identify and segment building areas to provide informative data for downstream tasks. Currently, building extraction is mainly achieved by deep convolutional neural networks (CNNs) based on the [...] Read more.
Building extraction is a popular topic in remote sensing image processing. Efficient building extraction algorithms can identify and segment building areas to provide informative data for downstream tasks. Currently, building extraction is mainly achieved by deep convolutional neural networks (CNNs) based on the U-shaped encoder–decoder architecture. However, the local perceptive field of the convolutional operation poses a challenge for CNNs to fully capture the semantic information of large buildings, especially in high-resolution remote sensing images. Considering the recent success of the Transformer in computer vision tasks, in this paper, first we propose a shifted-window (swin) Transformer-based encoding booster. The proposed encoding booster includes a swin Transformer pyramid containing patch merging layers for down-sampling, which enables our encoding booster to extract semantics from multi-level features at different scales. Most importantly, the receptive field is significantly expanded by the global self-attention mechanism of the swin Transformer, allowing the encoding booster to capture the large-scale semantic information effectively and transcend the limitations of CNNs. Furthermore, we integrate the encoding booster in a specially designed U-shaped network through a novel manner, named the Swin Transformer-based Encoding Booster- U-shaped Network (STEB-UNet), to achieve the feature-level fusion of local and large-scale semantics. Remarkably, compared with other Transformer-included networks, the computational complexity and memory requirement of the STEB-UNet are significantly reduced due to the swin design, making the network training much easier. Experimental results show that the STEB-UNet can effectively discriminate and extract buildings of different scales and demonstrate higher accuracy than the state-of-the-art networks on public datasets. Full article
(This article belongs to the Special Issue Recent Advances in Neural Network for Remote Sensing)
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19 pages, 2894 KiB  
Article
A Dilated Segmentation Network with the Morphological Correction Method in Farming Area Image Series
by Xiuchun Lin, Shiyun Wa, Yan Zhang and Qin Ma
Remote Sens. 2022, 14(8), 1771; https://doi.org/10.3390/rs14081771 - 7 Apr 2022
Cited by 9 | Viewed by 1814
Abstract
Farming areas are made up of diverse land use types, such as arable lands, grasslands, woodlands, water bodies, and other surrounding agricultural architectures. They possess imperative economic value, and are considerably valued in terms of farmers’ livelihoods and society’s flourishment. Meanwhile, detecting crops [...] Read more.
Farming areas are made up of diverse land use types, such as arable lands, grasslands, woodlands, water bodies, and other surrounding agricultural architectures. They possess imperative economic value, and are considerably valued in terms of farmers’ livelihoods and society’s flourishment. Meanwhile, detecting crops in farming areas, such as wheat and corn, allows for more direct monitoring of farming area production and is significant for practical production and management. However, existing image segmentation methods are relatively homogeneous, with insufficient ability to segment multiple objects around the agricultural environment and small-scale objects such as corn and wheat. Motivated by these issues, this paper proposed a global-transformer segmentation network based on the morphological correction method. In addition, we applied the dilated convolution technique to the backbone of the model and the transformer technique to the branches. This innovation of integrating the above-mentioned techniques has an active impact on the segmentation of small-scale objects. Subsequently, the backbone improved by this method was applied to an object detection network based on a corn and wheat ears dataset. Experimental results reveal that our model can effectively detect wheat ears in a complicated environment. For two particular segmentation objects in farming areas, namely water bodies and roads, we notably proposed a morphological correction method, which effectively reduces the number of connected domains in the segmentation results with different parameters of dilation and erosion operations. The segmentation results of water bodies and roads were thereby improved. The proposed method achieved 0.903 and 13 for mIoU and continuity. This result reveals a remarkable improvement compared with the comparison model, and the continuity has risen by 408%. These comparative results demonstrate that the proposed method is eminent and robust enough to provide preliminary preparations and viable strategies for managing farming area resources and detecting crops. Full article
(This article belongs to the Special Issue Recent Advances in Neural Network for Remote Sensing)
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Graphical abstract

29 pages, 4452 KiB  
Article
GANsformer: A Detection Network for Aerial Images with High Performance Combining Convolutional Network and Transformer
by Yan Zhang, Xi Liu, Shiyun Wa, Shuyu Chen and Qin Ma
Remote Sens. 2022, 14(4), 923; https://doi.org/10.3390/rs14040923 - 14 Feb 2022
Cited by 24 | Viewed by 3052
Abstract
There has been substantial progress in small object detection in aerial images in recent years, due to the extensive applications and improved performances of convolutional neural networks (CNNs). Typically, traditional machine learning algorithms tend to prioritize inference speed over accuracy. Insufficient samples can [...] Read more.
There has been substantial progress in small object detection in aerial images in recent years, due to the extensive applications and improved performances of convolutional neural networks (CNNs). Typically, traditional machine learning algorithms tend to prioritize inference speed over accuracy. Insufficient samples can cause problems for convolutional neural networks, such as instability, non-convergence, and overfitting. Additionally, detecting aerial images has inherent challenges, such as varying altitudes and illuminance situations, and blurred and dense objects, resulting in low detection accuracy. As a result, this paper adds a transformer backbone attention mechanism as a branch network, using the region-wide feature information. This paper also employs a generative model to expand the input aerial images ahead of the backbone. The respective advantages of the generative model and transformer network are incorporated. On the dataset presented in this study, the model achieves 96.77% precision, 98.83% recall, and 97.91% mAP by adding the Multi-GANs module to the one-stage detection network. These three indices are enhanced by 13.9%, 20.54%, and 10.27%, respectively, when compared to the other detection networks. Furthermore, this study provides an auto-pruning technique that may achieve 32.2 FPS inference speed with a minor performance loss while responding to the real-time detection task’s usage environment. This research also develops a macOS application for the proposed algorithm using Swift development technology. Full article
(This article belongs to the Special Issue Recent Advances in Neural Network for Remote Sensing)
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22 pages, 3150 KiB  
Article
DA-IMRN: Dual-Attention-Guided Interactive Multi-Scale Residual Network for Hyperspectral Image Classification
by Liang Zou, Zhifan Zhang, Haijia Du, Meng Lei, Yong Xue and Z. Jane Wang
Remote Sens. 2022, 14(3), 530; https://doi.org/10.3390/rs14030530 - 23 Jan 2022
Cited by 6 | Viewed by 2746
Abstract
Deep learning-based fusion of spectral-spatial information is increasingly dominant for hyperspectral image (HSI) classification. However, due to insufficient samples, current feature fusion methods often neglect joint interactions. In this paper, to further improve the classification accuracy, we propose a dual-attention-guided interactive multi-scale residual [...] Read more.
Deep learning-based fusion of spectral-spatial information is increasingly dominant for hyperspectral image (HSI) classification. However, due to insufficient samples, current feature fusion methods often neglect joint interactions. In this paper, to further improve the classification accuracy, we propose a dual-attention-guided interactive multi-scale residual network (DA-IMRN) to explore the joint spectral-spatial information and assign pixel-wise labels for HSIs without information leakage. In DA-IMRN, two branches focusing on spatial and spectral information separately are employed for feature extraction. A bidirectional-attention mechanism is employed to guide the interactive feature learning between two branches and promote refined feature maps. In addition, we extract deep multi-scale features corresponding to multiple receptive fields from limited samples via a multi-scale spectral/spatial residual block, to improve classification performance. Experimental results on three benchmark datasets (i.e., Salinas Valley, Pavia University, and Indian Pines) support that attention-guided multi-scale feature learning can effectively explore the joint spectral-spatial information. The proposed method outperforms state-of-the-art methods with the overall accuracy of 91.26%, 93.33%, and 82.38%, and the average accuracy of 94.22%, 89.61%, and 80.35%, respectively. Full article
(This article belongs to the Special Issue Recent Advances in Neural Network for Remote Sensing)
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30 pages, 4909 KiB  
Article
Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method
by Chengbiao Fu, Anhong Tian, Daming Zhu, Junsan Zhao and Heigang Xiong
Remote Sens. 2021, 13(24), 5140; https://doi.org/10.3390/rs13245140 - 17 Dec 2021
Cited by 7 | Viewed by 2100
Abstract
Soil salinization is a global ecological and environmental problem in arid and semi-arid areas that can be ameliorated via soil management, visible-near infrared-shortwave infrared (VNIR-SWIR) spectroscopy can be adapted to rapidly monitor soil salinity content. This study explored the potential of Grünwald–Letnikov fractional-order [...] Read more.
Soil salinization is a global ecological and environmental problem in arid and semi-arid areas that can be ameliorated via soil management, visible-near infrared-shortwave infrared (VNIR-SWIR) spectroscopy can be adapted to rapidly monitor soil salinity content. This study explored the potential of Grünwald–Letnikov fractional-order derivative (FOD), feature band selection methods, nonlinear partial least squares regression (PLSR), and four machine learning models to estimate the soil salinity content using VNIR-SWIR spectra. Ninety sample points were field scanned with VNIR-SWR and soil samples (0–20 cm) were obtained at the time of scanning. The samples points come from three zones representing different intensities of human interference (I, II, and III Zones) in Fukang, Xinjiang, China. Each zone contained thirty sample points. For modeling, we firstly adopted FOD (with intervals of 0.1 and range of 0–2) as a preprocessing method to analyze soil hyperspectral data. Then, four sets of spectral bands (R-FOD-FULL indicates full band range, R-FOD-CC5 bands that met a 0.05 significance test, R-FOD-CC1 bands that met a 0.01 significance test, and R-FOD-CC1-CARS represents CC1 combined with competitive adaptive reweighted sampling) were selected as spectral input variables to develop the estimation model. Finally, four machine learning models, namely, generalized regression neural network (GRNN), extreme learning machine (ELM), random forest (RF), and PLSR, to estimate soil salinity. Study results showed that (1) the heat map of correlation coefficient matrix between hyperspectral data and salinity indicated that FOD significantly improved the correlation. (2) The characteristic band variables extracted and used by R-FOD-CC1 were fewer in number, and redundancy between bands smaller than R-FOD-FULL and R-FOD-CC5, thus estimation accuracy of R-FOD-CC1 was higher than R-FOD-CC5 or R-FOD-FULL. A high prediction accuracy was achieved with a less complex calculation. (3) The GRNN model yielded the best salinity estimation in all three zones compared to ELM, BPNN, RF, and PLSR on the whole, whereas, the RF model had the worst estimation effect. The R-FOD-CC1-CARS-GRNN model yielded the best salinity estimation in I Zone with R2, RMSE and RPD of 0.7784, 1.8762, and 2.0568, respectively. The fractional order was 1.5 and estimation performance was great. The optimal model for predicting soil salinity in II and III Zone was, also, R-FOD-CC1-CARS-GRNN (R2 = 0.7912, RMSE = 3.4001, and RPD = 1.8985 in II Zone; R2 = 0.8192, RMSE = 6.6260, and RPD = 1.8190 in III Zone), with the fractional order of 1.7- and 1.6-, respectively, and the estimation performance were all fine. (4) The characteristic bands selected by the best model in I, II, and III Zones were 8, 9, and 11, respectively, which account for 0.45%, 0.51%, and 0.63%% of the full bands. This approach reduces the number of modeled band variables and simplifies the model structure. Full article
(This article belongs to the Special Issue Recent Advances in Neural Network for Remote Sensing)
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34 pages, 690 KiB  
Article
An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing
by Adrian Doicu, Alexandru Doicu, Dmitry S. Efremenko, Diego Loyola and Thomas Trautmann
Remote Sens. 2021, 13(24), 5061; https://doi.org/10.3390/rs13245061 - 13 Dec 2021
Cited by 2 | Viewed by 2079
Abstract
In this paper, we present neural network methods for predicting uncertainty in atmospheric remote sensing. These include methods for solving the direct and the inverse problem in a Bayesian framework. In the first case, a method based on a neural network for simulating [...] Read more.
In this paper, we present neural network methods for predicting uncertainty in atmospheric remote sensing. These include methods for solving the direct and the inverse problem in a Bayesian framework. In the first case, a method based on a neural network for simulating the radiative transfer model and a Bayesian approach for solving the inverse problem is proposed. In the second case, (i) a neural network, in which the output is the convolution of the output for a noise-free input with the input noise distribution; and (ii) a Bayesian deep learning framework that predicts input aleatoric and model uncertainties, are designed. In addition, a neural network that uses assumed density filtering and interval arithmetic to compute uncertainty is employed for testing purposes. The accuracy and the precision of the methods are analyzed by considering the retrieval of cloud parameters from radiances measured by the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR). Full article
(This article belongs to the Special Issue Recent Advances in Neural Network for Remote Sensing)
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