Computational Intelligence in Remote Sensing: 2nd Edition

Topic Information

Dear Colleagues,

With the development of earth-observation techniques, huge amounts of remote sensing data with a high spectral–spatial–temporal resolution are captured constantly, and remote sensing data processing and analysis have been successfully used in numerous fields, including geography, environmental monitoring, land surveys, disaster management, mineral exploration, and so forth. They also have military, intelligence, commercial, economic, planning, and humanitarian applications, among others. For the processing, analysis, and application of remote sensing data, there are many challenges, such as the huge amount of data, complex data structures, small, labeled samples, and non-convex optimization. Computational intelligence techniques, which are inspired by biological intelligent systems, can provide possible solutions to the above-mentioned problems.

Computational intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Traditionally, the three main pillars of CI have been neural networks, fuzzy systems, and evolutionary computation. However, over time, many nature-inspired computing paradigms have evolved. Thus, CI is an evolving field, and at present, in addition to the three main constituents, it encompasses computing paradigms such as ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a major role in developing successful intelligent systems, including games and cognitive developmental systems. Over the last few years, there has been an explosion of research on deep learning, specifically deep convolutional neural networks, and deep learning has become the core method for artificial intelligence. In fact, some of the most successful AI systems today are based on CI. In the future, CI will produce effective solutions to the challenges in remote sensing.

This Topic aims to provide a forum for disseminating the achievements related to the research and applications of computational intelligence techniques for remote sensing (e.g., multi-/hyper-spectral, SAR, and LIDAR) analysis and applications, with topics including but not limited to:

• Neural networks in remote sensing;
• Evolutionary computation in remote sensing;
• Fuzzy logic and systems in remote sensing;
• Artificial intelligence in remote sensing;
• Machine learning in remote sensing;
• Deep learning in remote sensing;
• Earth observation big data intelligence;
• Remote sensing image analysis;
• Remote sensing imagery.

Dr. Yue Wu
Prof. Dr. Kai Qin
Prof. Dr. Maoguo Gong
Prof. Dr. Qiguang Miao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400	Submit
Journal of Imaging jimaging	2.7	5.9	2015	20.9 Days	CHF 1800	Submit
Machine Learning and Knowledge Extraction make	4.0	6.3	2019	27.1 Days	CHF 1800	Submit
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700	Submit

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Published Papers (6 papers)

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All Applied Sciences Electronics J. Imaging MAKE Remote Sensing

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16 pages, 4099 KiB  

Open AccessArticle

Multi-Frequency Spectral–Spatial Interactive Enhancement Fusion Network for Pan-Sharpening

by Yunxuan Tang, Huaguang Li, Guangxu Xie, Peng Liu and Tong Li

Electronics 2024, 13(14), 2802; https://doi.org/10.3390/electronics13142802 - 16 Jul 2024

Viewed by 283

Abstract

The objective of pan-sharpening is to effectively fuse high-resolution panchromatic (PAN) images with limited spectral information and low-resolution multispectral (LR-MS) images, thereby generating a fused image with a high spatial resolution and rich spectral information. However, current fusion techniques face significant challenges, including [...] Read more.

The objective of pan-sharpening is to effectively fuse high-resolution panchromatic (PAN) images with limited spectral information and low-resolution multispectral (LR-MS) images, thereby generating a fused image with a high spatial resolution and rich spectral information. However, current fusion techniques face significant challenges, including insufficient edge detail, spectral distortion, increased noise, and limited robustness. To address these challenges, we propose a multi-frequency spectral–spatial interaction enhancement network (MFSINet) that comprises the spectral–spatial interactive fusion (SSIF) and multi-frequency feature enhancement (MFFE) subnetworks. The SSIF enhances both spatial and spectral fusion features by optimizing the characteristics of each spectral band through band-aware processing. The MFFE employs a variant of wavelet transform to perform multiresolution analyses on remote sensing scenes, enhancing the spatial resolution, spectral fidelity, and the texture and structural features of the fused images by optimizing directional and spatial properties. Moreover, qualitative analysis and quantitative comparative experiments using the IKONOS and WorldView-2 datasets indicate that this method significantly improves the fidelity and accuracy of the fused images. Full article

(This article belongs to the Topic Computational Intelligence in Remote Sensing: 2nd Edition)

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23 pages, 17222 KiB  

Open AccessArticle

Random Stepped Frequency ISAR 2D Joint Imaging and Autofocusing by Using 2D-AFCIFSBL

by Yiding Wang, Yuanhao Li, Jiongda Song and Guanghui Zhao

Remote Sens. 2024, 16(14), 2521; https://doi.org/10.3390/rs16142521 - 9 Jul 2024

Viewed by 296

Abstract

With the increasingly complex electromagnetic environment faced by radar, random stepped frequency (RSF) has garnered widespread attention owing to its remarkable Electronic Counter-Countermeasure (ECCM) characteristic, and it has been universally applied in inverse synthetic aperture radar (ISAR) in recent years. However, if the [...] Read more.

With the increasingly complex electromagnetic environment faced by radar, random stepped frequency (RSF) has garnered widespread attention owing to its remarkable Electronic Counter-Countermeasure (ECCM) characteristic, and it has been universally applied in inverse synthetic aperture radar (ISAR) in recent years. However, if the phase error induced by the translational motion of the target in RSF ISAR is not precisely compensated, the imaging result will be defocused. To address this challenge, a novel 2D method based on sparse Bayesian learning, denoted as 2D-autofocusing complex-value inverse-free SBL (2D-AFCIFSBL), is proposed to accomplish joint ISAR imaging and autofocusing for RSF ISAR. First of all, to integrate autofocusing into the ISAR imaging process, phase error estimation is incorporated into the imaging model. Then, we increase the speed of Bayesian inference by relaxing the evidence lower bound (ELBO) to avoid matrix inversion, and we further convert the iterative process into a matrix form to improve the computational efficiency. Finally, the 2D phase error is estimated through maximum likelihood estimation (MLE) in the image reconstruction iteration. Experimental results on both simulated and measured datasets have substantiated the effectiveness and computational efficiency of the proposed 2D joint imaging and autofocusing method. Full article

(This article belongs to the Topic Computational Intelligence in Remote Sensing: 2nd Edition)

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37 pages, 31622 KiB  

Open AccessReview

A Review of Deep Learning-Based Methods for Road Extraction from High-Resolution Remote Sensing Images

by Ruyi Liu, Junhong Wu, Wenyi Lu, Qiguang Miao, Huan Zhang, Xiangzeng Liu, Zixiang Lu and Long Li

Remote Sens. 2024, 16(12), 2056; https://doi.org/10.3390/rs16122056 - 7 Jun 2024

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Abstract

Road extraction from high-resolution remote sensing images has long been a focal and challenging research topic in the field of computer vision. Accurate extraction of road networks holds extensive practical value in various fields, such as urban planning, traffic monitoring, disaster response and [...] Read more.

Road extraction from high-resolution remote sensing images has long been a focal and challenging research topic in the field of computer vision. Accurate extraction of road networks holds extensive practical value in various fields, such as urban planning, traffic monitoring, disaster response and environmental monitoring. With rapid development in the field of computational intelligence, particularly breakthroughs in deep learning technology, road extraction technology has made significant progress and innovation. This paper provides a systematic review of deep learning-based methods for road extraction from remote sensing images, focusing on analyzing the application of computational intelligence technologies in improving the precision and efficiency of road extraction. According to the type of annotated data, deep learning-based methods are categorized into fully supervised learning, semi-supervised learning, and unsupervised learning approaches, each further divided into more specific subcategories. They are comparatively analyzed based on their principles, advantages, and limitations. Additionally, this review summarizes the metrics used to evaluate the performance of road extraction models and the high-resolution remote sensing image datasets applied for road extraction. Finally, we discuss the main challenges and prospects for leveraging computational intelligence techniques to enhance the precision, automation, and intelligence of road network extraction. Full article

(This article belongs to the Topic Computational Intelligence in Remote Sensing: 2nd Edition)

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23 pages, 1927 KiB  

Open AccessArticle

D²Former: Dual-Domain Transformer for Change Detection in VHR Remote Sensing Images

by Huanhuan Zheng, Hui Liu, Lei Lu, Shiyin Li and Jiyan Lin

Electronics 2024, 13(11), 2204; https://doi.org/10.3390/electronics13112204 - 5 Jun 2024

Viewed by 390

Abstract

Computational intelligence technologies have been extensively applied for the interpretation of remote sensing imagery. Recently, the computational-intelligence-based Transformer change detection (CD) approach has attracted increasing attention. However, the current Transformer-based CD method can better capture global features, but there is no good solution [...] Read more.

Computational intelligence technologies have been extensively applied for the interpretation of remote sensing imagery. Recently, the computational-intelligence-based Transformer change detection (CD) approach has attracted increasing attention. However, the current Transformer-based CD method can better capture global features, but there is no good solution for the loss of local detail information. For this reason, introducing semantic and frequency information from the perspective of a dual-domain can be beneficial for improving the representation of detailed features to improve CD performance. To overcome this limitation, a dual-domain Transformer (D²Former) is proposed for CD. Firstly, we adopt a semantic tokenizer to capture the semantic information, which promotes the enrichment and refinement of semantic change information in the Transformer. Secondly, a frequency tokenizer is introduced to acquire the frequency information of the features, which offers the proposed D²Former another aspect and dimension to enhance the ability to detect change information. Therefore, the proposed D²Former employs dual-domain tokenizers to acquire and fuse the feature representation with rich semantic and frequency information, which can refine the features to acquire more fine-grained CD ability. Extensive experiments on three CD benchmark datasets demonstrate that the proposed D²Former obviously outperforms some other existing approaches. The results present the competitive performance of our method on the WHU-CD, LEVIR-CD, and GZ-CD datasets, for which it achieved F1-Score metrics of 92.85%, 90.60%, and 87.02%, respectively. Full article

(This article belongs to the Topic Computational Intelligence in Remote Sensing: 2nd Edition)

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12 pages, 2811 KiB  

Open AccessArticle

Characterizing Lossy Dielectric Materials in Shock Physics by Millimeter-Wave Interferometry Using One-Dimensional Convolutional Neural Networks and Nonlinear Optimization

by Ngoc Tuan Pham, Alexandre Lefrançois and Hervé Aubert

Electronics 2024, 13(9), 1664; https://doi.org/10.3390/electronics13091664 - 25 Apr 2024

Viewed by 603

Abstract

When a dielectric material undergoes mechanical impact, it generates a shock wave, causing changes in its refractive index. Recent demonstrations have proven that the modified refractive index can be determined remotely using a millimeter-wave interferometer. However, these demonstrations are based on the resolution [...] Read more.

When a dielectric material undergoes mechanical impact, it generates a shock wave, causing changes in its refractive index. Recent demonstrations have proven that the modified refractive index can be determined remotely using a millimeter-wave interferometer. However, these demonstrations are based on the resolution of an inverse electromagnetic problem, which assumes that the losses in the material are negligible. This restrictive assumption is overcome in this article, in which a new approach is proposed to solve the inverse electromagnetic problem in lossy and shocked dielectric materials. Our methodology combines a one-dimensional convolutional neural network architecture, namely Inverse problem of Lossless or Lossy Shocked Wavefront Network (ILSW-Net), with a nonlinear optimization technique based on the Nelder–Mead algorithm to estimate losses within dielectric materials under a mechanical impact. Experimental results for both simulated and real signals show that our method can successfully predict the velocities and the refractive index while accurately estimating the shock wavefront. Full article

(This article belongs to the Topic Computational Intelligence in Remote Sensing: 2nd Edition)

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26 pages, 9713 KiB  

Open AccessArticle

Beyond Pixel-Wise Unmixing: Spatial–Spectral Attention Fully Convolutional Networks for Abundance Estimation

by Jiaxiang Huang and Puzhao Zhang

Remote Sens. 2023, 15(24), 5694; https://doi.org/10.3390/rs15245694 - 12 Dec 2023

Viewed by 1053

Abstract

Spectral unmixing poses a significant challenge within hyperspectral image processing, traditionally addressed by supervised convolutional neural network (CNN)-based approaches employing patch-to-pixel (pixel-wise) methods. However, such pixel-wise methodologies often necessitate image splitting into overlapping patches, resulting in redundant computations and potential information leakage between [...] Read more.

Spectral unmixing poses a significant challenge within hyperspectral image processing, traditionally addressed by supervised convolutional neural network (CNN)-based approaches employing patch-to-pixel (pixel-wise) methods. However, such pixel-wise methodologies often necessitate image splitting into overlapping patches, resulting in redundant computations and potential information leakage between training and test samples, consequently yielding overoptimistic outcomes. To overcome these challenges, this paper introduces a novel patch-to-patch (patch-wise) framework with nonoverlapping splitting, mitigating the need for repetitive calculations and preventing information leakage. The proposed framework incorporates a novel neural network structure inspired by the fully convolutional network (FCN), tailored for patch-wise unmixing. A highly efficient band reduction layer is incorporated to reduce the spectral dimension, and a specialized abundance constraint module is crafted to enforce both the Abundance Nonnegativity Constraint and the Abundance Sum-to-One Constraint for unmixing tasks. Furthermore, to enhance the performance of abundance estimation, a spatial–spectral attention module is introduced to activate the most informative spatial areas and feature maps. Extensive quantitative experiments and visual assessments conducted on two synthetic datasets and three real datasets substantiate the superior performance of the proposed algorithm. Significantly, the method achieves an impressive $RMSE$ loss of 0.007, which is at least 4.5 times lower than that of other baselines on Urban hyperspectral images. This outcome demonstrates the effectiveness of our approach in addressing the challenges of spectral unmixing. Full article

(This article belongs to the Topic Computational Intelligence in Remote Sensing: 2nd Edition)

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