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	2.7	4.5	2011	16.9 Days	CHF 2400	Submit
Electronics electronics	2.9	4.7	2012	15.6 Days	CHF 2400	Submit
Journal of Imaging jimaging	3.2	4.4	2015	21.7 Days	CHF 1800	Submit
Machine Learning and Knowledge Extraction make	3.9	8.5	2019	19.9 Days	CHF 1800	Submit
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700	Submit

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Published Papers (2 papers)

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All Applied Sciences Electronics J. Imaging MAKE Remote Sensing

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12 pages, 2779 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 288

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 794

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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