Computational Intelligence in Remote Sensing: 2nd Edition

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400
Journal of Imaging jimaging	2.7	5.9	2015	18.3 Days	CHF 1800
Machine Learning and Knowledge Extraction make	4.0	6.3	2019	20.8 Days	CHF 1800
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700

21 pages, 4465 KiB

Open AccessArticle

Smart Distillation for Land Use Monitoring: A Lightweight Remote Sensing Segmentation Strategy

by Yaowei Wu, Jiao Liu, Yuangong Chen and Jianzhou Gong

Electronics 2025, 14(2), 349; https://doi.org/10.3390/electronics14020349 - 17 Jan 2025

Viewed by 663

As the demand for land use monitoring continues to grow, high-precision remote sensing products have become increasingly important. Compared to traditional methods, deep learning networks demonstrate significant advantages in automatic feature extraction, handling complex scenes, and improving classification accuracy. However, as the complexity [...] Read more.

As the demand for land use monitoring continues to grow, high-precision remote sensing products have become increasingly important. Compared to traditional methods, deep learning networks demonstrate significant advantages in automatic feature extraction, handling complex scenes, and improving classification accuracy. However, as the complexity of these networks increases, so does the computational cost. To address this challenge, we propose an innovative knowledge distillation model, integrating two key modules—spatial-global attention feature distillation (SGAFD) and channel attention-based relational distillation (CARD). This model enables a lightweight “student” network to be guided by a large “teacher” network, enhancing classification performance while maintaining a compact model size. We validated our approach on the large-scale public remote sensing datasets GID15 and LoveDA, and the results show that these modules effectively improve classification performance, overcoming the limitations of lightweight models and advancing the practical applications of land use monitoring. Full article

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

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22 pages, 4118 KiB

Open AccessArticle

Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images

by Valdivino Alexandre de Santiago Júnior

Appl. Sci. 2024, 14(23), 10776; https://doi.org/10.3390/app142310776 - 21 Nov 2024

Viewed by 927

Abstract

Few-shot learning (FSL) is a learning paradigm which aims to address the issue of machine/deep learning techniques which traditionally need huge amounts of labelled data to work out. The remote sensing (RS) community has explored this paradigm with numerous published studies to date. [...] Read more.

Few-shot learning (FSL) is a learning paradigm which aims to address the issue of machine/deep learning techniques which traditionally need huge amounts of labelled data to work out. The remote sensing (RS) community has explored this paradigm with numerous published studies to date. Nevertheless, there is still a need for clear pieces of evidence on FSL-related issues in the RS context, such as which of the inference approaches is more suitable: inductive or transductive? Moreover, how does the number of epochs used during training, based on the meta-training (base) dataset, relate to the number of unseen classes during inference? This study aims to address these and other relevant questions in the context of FSL for scene classification in RS images. A comprehensive evaluation was conducted considering eight FSL approaches (three inductive and five transductive) and six scene classification databases. Some conclusions of this research are as follows: (1) transductive approaches are better than inductive ones. In particular, the transductive technique Transductive Information Maximisation (TIM) presented the best overall performance, where in 20 cases it got the first place; (2) a larger number of training epochs is more beneficial when there are more unseen classes during the inference phase. The most impressive gains occurred particularly considering the AID (6-way) and RESISC-45 (9-way) datasets. Notably, in the AID dataset, a remarkable 58.412% improvement was achieved in 1-shot tasks going from 10 to 200 epochs; (3) using five samples in the support set is statistically significantly better than using only one; and (4) a higher similarity between unseen classes (during inference) and some of the training classes does not lead to an improved performance. These findings can guide RS researchers and practitioners in selecting optimal solutions/strategies for developing their applications demanding few labelled samples. Full article

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

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17 pages, 654 KiB

Open AccessArticle

Enhanced Transformer for Remote-Sensing Image Captioning with Positional-Channel Semantic Fusion

by An Zhao, Wenzhong Yang, Danny Chen and Fuyuan Wei

Electronics 2024, 13(18), 3605; https://doi.org/10.3390/electronics13183605 - 11 Sep 2024

Viewed by 1297

Abstract

Remote-sensing image captioning (RSIC) aims to generate descriptive sentences for ages by capturing both local and global semantic information. This task is challenging due to the diverse object types and varying scenes in ages. To address these challenges, we propose a positional-channel semantic [...] Read more.

Remote-sensing image captioning (RSIC) aims to generate descriptive sentences for ages by capturing both local and global semantic information. This task is challenging due to the diverse object types and varying scenes in ages. To address these challenges, we propose a positional-channel semantic fusion transformer (PCSFTr). The PCSFTr model employs scene classification to initially extract visual features and learn semantic information. A novel positional-channel multi-headed self-attention (PCMSA) block captures spatial and channel dependencies simultaneously, enriching the semantic information. The feature fusion (FF) module further enhances the understanding of semantic relationships. Experimental results show that PCSFTr significantly outperforms existing methods. Specifically, the BLEU-4 index reached 78.42% in UCM-caption, 54.42% in RSICD, and 69.01% in NWPU-captions. This research provides new insights into RSIC by offering a more comprehensive understanding of semantic information and relationships within images and improving the performance of image captioning models. Full article

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

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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

Cited by 3 | Viewed by 999

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 916

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

Cited by 3 | Viewed by 5234

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

Cited by 1 | Viewed by 969

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 1269

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 2005

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

R M S E

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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