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Exploring Applications of Convolutional Neural Networks in Analyzing Multispectral Satellite imagery: A Systematic Review

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BIG DATA MINING AND ANALYTICS 2025年第2期8卷 407-429页

作者： Ivanda, Antonia Seric, Ljiljana Braovic, Maja Univ Split Fac Elect Engn Mech Engn & Naval Architecture Dept Elect & Comp Sci Split 21000 Croatia

remote sensing is of great importance for analyzing and studying various phenomena occurrence and development on Earth. Today is possible to extract features specific to various fields of application with the application of modern machine learning techniques, such as Convolutional Neural Networks (CNN) on MultiSpectral images (MSI). This systematic review examines the application of 1D-, 2D-, 3D-, and 4D-CNNs to MSI, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review addresses three Research Questions (RQ): RQ1: "In which application domains different CNN models have been successfully applied for processing MSI data?", RQ2: "What are the commonly utilized MSI datasets for training CNN models in the context of processing multispectral satellite imagery?", and RQ3: "How does the degree of CNN complexity impact the performance of classification, regression or segmentation tasks for multispectral satellite imagery?". Publications are selected from three databases, Web of Science, IEEE Xplore, and Scopus. Based on the obtained results, the main conclusions are: (1) The majority of studies are applied in the field of agriculture and are using Sentinel-2 satellite data;(2) Publications implementing 1D-, 2D-, and 3D-CNNs mostly utilize classification. For 4D-CNN, there are limited number of studies, and all of them use segmentation;(3) This study shows that 2D-CNNs prevail in all application domains, but 3D-CNNs prove to be better for spatio-temporal pattern recognition, more specifically in agricultural and environmental monitoring applications. 1D-CNNs are less common compared to 2D-CNNs and 3D-CNNs, but they show good performance in spectral analysis tasks. 4D-CNNs are more complex and still underutilized, but they have potential for complex data analysis. More details about metrics according to each CNN are provided in the text and supplementary files, offering a comprehensive overview of the evaluation metrics

关键词： remote sensing MultiSpectral images (MSI) MultiSpectral images (MSI) satellite images satellite images Convolutional Neural Networks (CNN) Convolutional Neural Networks (CNN) machine learning machine learning classification classification regression regression segmentation segmentation segmentation

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Toward robust pattern similarity metric for distributed model evaluation

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2024年第10期38卷 4007-4025页

作者： Yorulmaz, Eymen Berkay Kartal, Elif Demirel, Mehmet Cuneyd Istanbul Tech Univ Dept Civil Engn TR-34467 Istanbul Turkiye

SPAtial EFficiency (SPAEF) metric is one of the most thoroughly used metrics in hydrologic community. In this study, our aim is to improve SPAEF by replacing the histogram match component with other statistical indices, i.e. kurtosis and earth mover's distance, or by adding a fourth or fifth component such as kurtosis and skewness. The existing spatial metrics i.e. SPAtial efficiency (SPAEF), structural similarity (SSIM) and spatial pattern efficiency metric (SPEM) were compared with newly proposed metrics to assess their converging performance. The mesoscale hydrologic model (mHM) of the Moselle River is used to simulate streamflow (Q) and actual evapotranspiration (AET). The two-source energy balance AET during the growing season is used as monthly reference maps to calculate the spatial performance of the model. The moderate resolution imaging spectroradiometer based leaf area index is utilized by the mHM via pedo-transfer functions and multi-scale parameter regionalization approach to scale the potential ET. In addition to the real monthly AET maps, we also tested these metrics using a synthetic true AET map simulated with a known parameter set for a randomly selected day. The results demonstrate that the newly developed four-component metric i.e. SPAtial Hybrid 4 (SPAH4) slightly outperforms conventional three-component metric i.e. SPAEF (3% better). However, SPAH4 significantly outperforms the other existing metrics i.e. 40% better than SSIM and 50% better than SPEM. We believe that other fields such as remote sensing, change detection, function space optimization and image processing can also benefit from SPAH4.

关键词： mHM Model calibration Spatial pattern SPAEF MODIS TSEB

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RSNet: The Search for remote sensing Deep Neural Networks in recognition Tasks

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2021年第3期59卷 2520-2534页

作者： Wang, Junjue Zhong, Yanfei Zheng, Zhuo Ma, Ailong Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Wuhan Univ Hubei Prov Engn Res Ctr Nat Resources Remote Sens Wuhan 430079 Peoples R China

Deep learning algorithms, especially convolutional neural networks (CNNs), have recently emerged as a dominant paradigm for high spatial resolution remote sensing (HRS) image recognition. A large amount of CNNs have already been successfully applied to various HRS recognition tasks, such as land-cover classification and scene classification. However, they are often modifications of the existing CNNs derived from natural image processing, in which the network architecture is inherited without consideration of the complexity and specificity of HRS images. In this article, the remote sensing deep neural network (RSNet) framework is proposed using an automatically search strategy to find the appropriate network architecture for HRS image recognition tasks. In RSNet, the hierarchical search space is first designed to include module- and transition-level spaces. The module-level space defines the basic structure block, where a series of lightweight operations as candidates, including depthwise separable convolutions, is proposed to ensure the efficiency. The transition-level space controls the spatial resolution transformations of the features. In the hierarchical search space, a gradient-based search strategy is used to find the appropriate architecture. In RSNet, the task-driven architecture training process can acquire the optimal model parameters of the switchable recognition module for HRS image recognition tasks. The experimental results obtained using four benchmark data sets for land-cover classification and scene classification tasks demonstrate that the searched RSNet can achieve a satisfactory accuracy with a high computational efficiency and, hence, provides an effective option for the processing of HRS imagery.

关键词： High-resolution remote sensing image remote sensing recognition search for convolutional neural networks (CNNs)

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EDO-SANet: Shape-Aware Network with Edge Detection Operator for Polyp Segmentation 24

EDO-SANet: Shape-Aware Network with Edge Detection Operator ...

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2nd Asia Conference on Computer Vision, image processing and pattern recognition (CVIPPR)

作者： Zhao, Yifei Wang, Xiaoying Li, Yang Yin, Junping North China Elect Power Univ Beijing Peoples R China Northeast Normal Univ Changchun Peoples R China Inst Appl Phys & Computat Math Beijing Peoples R China

ISBN: (纸本)9798400716607

Automatic and accurate segmentation of colonic polyps can effectively help physicians quickly identify the size and location of polyps during endoscopy. However, due to the variable intestinal environment, polyps are highly similar to the surrounding tissue leading to over-segmentation or under-segmentation. To tackle these concerns, we propose a novel network with shape-awareness. Specifically, the network employs a dual-stream encoder, including the mainstream Transoformer-based encoder and an auxiliary encoder to extract global and local representations. The auxiliary encoder is built on deformable convolutional v3 for sensing the shape of polyps. In order to better identify the edges of polyps, the Edge Detection Guided Module (EDGM) is proposed to utilize edge detection operators to highlight the importance of edge information in low-level features. Furthermore, we introduce a Semantic Interaction Module (SIM) to better localize and calibrate targets by integrating global and local semantics in high-level features. Compared to other state-of-the-art methods, our model achieves better performance both in learning and generalization ability on five polyp segmentation datasets. The potential applicability of our model to other biomedical fields is demonstrated through its outstanding performance. The code is available at https://***/xff12138/EDOSANet

关键词： Polyp segmentation Medical image processing Deep learning Deformable convolution Edge detection operator

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Optimal trained ensemble of classification model for satellite image classification

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Multimedia Tools and Applications 2025年第7期84卷 3745-3766页

作者： Singh, Sarabjeet Kundra, Harish Kundra, Sheetal Pratima, P. Venkata Devi, M. V. Anjana Kumar, Sushil Hassan, Mariya Department of Computer Science and Engineering Chandigarh College of Engineering and Technology Chandigarh160019 India Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Artificial Intelligence and Data Science Guru Nanak Institute of Technology Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Computer Science and Engineering Central University of Haryana Jant-Pali Mahendergarh123031 India

Satellite image classification is the most significant remote sensing method for computerized analysis and pattern detection of satellite data. This method relies on the image's diversity structures and necessitates a thorough validation of the training data using the chosen classification algorithm. The classifying of remotely sensed images refers to the process of classifying pixels into a fixed set of distinct classes based on their data values. The pixel is categorized into that class if it complies with a specific set of requirements. A new SIC scheme is suggested in this work, where, Contrast limited adaptive histogram equalization (CLAHE) is deployed for pre-processing the image. Further, statistical features, Vegetation Indices (VI) and Improved Hybrid Vegetation Index (IHVI) are extracted from the preprocessed image. Then, we deploy 2 phase classification process for classifying the image. In the first phase, Bi-Directional Gated Recurrent Unit (BI-GRU), Convolutional Neural Network (CNN) and Deep Max Out (DMO) are used. In 2nd phase, optimized RNN is used for determining the final classification result. Also, the training of the model is done by the new Chaotic Opposition Behavior Improvised Coot Algorithm (COBI-CA) optimization via tuning the optimal weights. The COOT method has been enhanced and is now the suggested algorithm. Lastly, we conduct an analysis to compare the suggested Satellite image Classification (SIC) model's performance to the traditional approaches in terms of statistical analysis, precision, accuracy, and error measure. In comparison to other conventional methods, the accuracy of the suggested model is 97%. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2024.

关键词： Pixels

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Urban Green Space Planning Based on remote sensing image Enhancement and Wireless sensing Technology

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MOBILE NETWORKS & APPLICATIONS 2025年第SUPPL1期30卷 30-30页

作者： Xu, Tao Guo, Bo Ruan, Chengzhi Gao, Qiang Yan, Biao Wuyi Univ Coll Mech & Elect Engn Wuyishan 354300 Fujian Peoples R China

Traditional urban green space planning methods often rely on site survey and manual mapping, which is inefficient and costly. In order to solve these problems, a new urban green space planning method is proposed based on remote sensing image enhancement technology and wireless sensing technology. In this paper, remote sensing image technology is used to obtain high-resolution urban green space images, and image enhancement algorithm is used to improve the clarity and recognition of the images. Then through the wireless sensor network in the green space, real-time environmental data is collected. Finally, combined with remote sensing data and sensor data, a comprehensive analysis is carried out to develop a reasonable green space planning scheme. The results show that the urban green space planning method based on remote sensing image enhancement and wireless sensing technology not only improves the accuracy and efficiency of green space monitoring, but also significantly reduces the cost. The real-time environmental data obtained by wireless sensors can more accurately reflect the ecological status of green space, and contribute to scientific planning and management of urban green space. Therefore, the combination of remote sensing image enhancement and wireless sensing technology provides an efficient and low-cost new way for urban green space planning.

关键词： remote sensing image image enhancement Wireless sensing technology Urban green space planning

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An extraction method of environmental behaviour characteristics in landscape design

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL TECHNOLOGY AND MANAGEMENT 2023年第1-2期26卷 66-80页

作者： Wang, Shujing Tang, Haibin Jilin Univ Architecture & Technol Sch Art & Design Changchun 130114 Peoples R China Nissho Gakuen Chang Chun High Sch Changchun 130031 Peoples R China

Aiming at the problems of low extraction accuracy and long extraction time in the traditional landscape environmental behaviour feature extraction method, this paper puts forward the environmental behaviour feature extraction method in landscape design. Firstly, the environmental behaviour factors are analysed according to the sense of comfort, security, communication, recognition and achievement, and the environmental behaviour data are obtained. Then, radiometric calibration, atmospheric correction, geometric correction and greyscale processing are carried out for the obtained behaviour data. Then, the scale difference is used to calculate the grey features and detail features of the preprocessed environmental behaviour image, and the grey features and detail features are weighted and fused to extract the environmental behaviour features of landscape architecture. Finally, the simulation results show that the accuracy of the proposed method for landscape environmental behaviour feature extraction is up to 100%, with high extraction accuracy and short extraction time.

关键词： landscape architecture landscape design environmental behaviour feature extraction remote sensing image technology greyscale processing

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High Spatial Resolution remote sensing imagery Classification Based on Markov Random Field Model Integrating Granularity and Semantic Features 5th

High Spatial Resolution Remote Sensing Imagery Classificatio...

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5th Chinese Conference on pattern recognition and Computer Vision (PRCV)

作者： Wang, Jun Dai, Qinling Wang, Leiguang Zhao, Yili Fu, Haoyu Zhang, Yue Southwest Forestry Univ Fac Forestry Kunming Yunnan Peoples R China Southwest Forestry Univ Fac Art & Design Kunming Yunnan Peoples R China Southwest Forestry Univ Fac Big Data & Artificial Intelligence Kunming Yunnan Peoples R China

ISBN: (纸本)9783031189128;9783031189135

In remote sensing image classification, it is difficult to distinguish the homogeneity of same land class and the heterogeneity between different land classes. Moreover, high spatial resolution remote sensing images often show the phenomenon of ground object classes fragmentation and salt-and-pepper noise after classification. To improve the above phenomenon, Markov random field (MRF) is a widely used method for remote sensing image classification due to its effective spatial context description. Some MRF-based methods capture more image information by building interaction between pixel granularity and object granularity. Some other MRF-based methods construct representations at different semantic layers on the image to extract the spatial relationship of objects. This paper proposes a new MRF-based method that combines multi-granularity and different semantic layers of information to improve remote sensing image classification. A hierarchical interaction algorithm is proposed that iteratively updates information between different granularity and semantic layers to generate results. The experimental results demonstrate that: on the Gaofen-2 imagery, the proposed model shows a better classification performance than other methods.

关键词： Markov random field (MRF) remote sensing image classification Multiclass-layer

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Optimizing synthetic aperture radar image classification and change detection: a proportional factor firefly algorithm and multilayer perceptron approach

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SIGNAL image AND VIDEO processing 2024年第SUPPL 1期18卷 771-787页

作者： Pandeeswari, B. Alice, K. Sutha, J. Govt Polytech Coll Women Dept Comp Engn Madurai Tamil Nadu India SRM Inst Sci & Technol Dept Comp Technol Chengalpattu Tamil Nadu India SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

Change detection in remote sensing images is essential for monitoring alterations in the Earth's surface over time. Despite numerous methodologies suggested for synthetic aperture radar (SAR) image classification and change detection, achieving both high accuracy and computational efficiency remains a challenge. To address this gap, present the proportional factor-based firefly optimization and multilayer perceptron deep neural network (PFFO-MPDNN) framework tailored for SAR image classification and change detection (SAR-IC-CD). This approach begins by harnessing data from diverse geographical locations, including Bern, Ottawa, Mexico, and the Yellow River datasets. employing bilateral and spatial filtering (BSF) for noise reduction and modified contrast-limited adaptive histogram equalization (MCLAHE) for contrast enhancement, prepare the data for subsequent analysis. Leveraging the modified gauss log ratio (MGLR) operator, derive dissimilar image representations, enriching the dataset's discriminative power. Next, extract features using ternary pattern and discrete wavelet transform (TP-DWT), including SPCH, gray-level co-occurrence matrix, run length, Shannon entropy, and Pearson correlation. Through proportional factor-based firefly optimization (PFFO), select optimal features to enhance classification accuracy. The selected features are then fed into a multilayer perceptron and deep neural network (MPDNN) for image classification, effectively distinguishing between changed and unchanged image parts. This experiment is conducted in MATLAB, and it demonstrates the superior performance of the proposed PFFO-MPDNN-SAR-IC-CD method across various performance metrics, including accuracy, sensitivity, specificity, rand index, global consistency error, variation of information, false-negative rate (FNR), and false-positive rate (FPR). Comparisons with existing methods, including RBF-DCNN-SAR-IC, DBN-SAR-IC, CNN-SAR-IC, USF-SAR-IC, DDN-SAR-IC, L-CapsNet-SAR-IC, SVM-HRLSM

关键词： Bilateral and spatial filtering (BSF) Modified contrast-limited adaptive histogram equalization (MCLAHE) Modified gauss log ratio (MGLR) Proportional factor-based firefly optimization (PFFO) Multilayer perceptron and deep neural network (MPDNN)

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Deep learning landslide extraction based on multi-temporal image difference local spatial information

Deep learning landslide extraction based on multi-temporal i...

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2023 International Conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Miao, Sheng Qu, Yu Liu, Xirong College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781510666351

Landslide as a natural disaster, it is important to obtain accurate information of landslide spatial distribution. The current method of landslide extraction is mainly based on the spectral features, spatial structure and texture features of landslides on remote sensing images, which is difficult to accurately extract the spatial distribution of landslides for surface structures with similar spectral features (bare soil, villages, exposed rocks, roads, water bodies, etc.) and does not make full use of local spatial information features and causes loss of landslide boundary information. To address the above problems, this paper proposes a method to extract landslides based on the fusion of local spatial information with multi-temporal image differences. The high-resolution images before and after the landslide are used to calculate the difference images, extract each band and combine the temporal difference images to combine the bands and use them as input features for deep learning. The ASPP module is introduced to the U-net network model for expanding the local spatial information perception field, enhancing the preservation of landslide boundary information, and guiding the attention mechanism of feature downsampling for extracting multi-scale contextual information. The evaluation shows that the method can ensure the detection error of landslide area range is less than 8.6%, the extraction accuracy is more than 95%, and the Kappa is 0.90. The experimental results prove the feasibility and accuracy of the method in rainfall landslide detection. © 2023 SPIE.

关键词： Landslides

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