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Automatic wildfire monitoring system based on deep learning

EUROPEAN JOURNAL OF remote sensing 2022年第1期55卷 551-567页

作者： Peng, Yingshu Wang, Yi Chinese Acad Sci Lushan Bot Garden Nanchang 332900 Jiangxi Peoples R China Nanjing Forestry Univ Coll Forestry Nanjing Peoples R China Jiangsu Wiscom Technol Co Ltd Smart City Res Inst Nanjing Peoples R China

Fire detection based on computer vision technology can avoid many flaws in conventional methods. However, existing methods fail to achieve a good trade-off in accuracy, model size, speed, and cost. This paper presents a high-performance forest fire recognition algorithm to solve the current problems in forest fire monitoring. Firstly, visual saliency areas in motion images are extracted to improve detection efficiency. Secondly, transfer learning techniques are employed to improve the generalization performance of the constructed deep learning classification model. Finally, fire detection is realized based on C++ deployment algorithms Compared with the existing forest fire detection methods, the proposed method has higher classification accuracy and speed, with a more comprehensive application range and lower cost. The performance of our method can meet the accuracy and speed requirements of real-time fire detection, and it can be deployed and practiced on multiple platforms.

关键词： Forest fire flame detection deep learning image processing model deployment

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Overview of Research Progress of Digital image processing Technology

Overview of Research Progress of Digital Image Processing Te...

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2022 International Conference on Computing Innovation and Applied Physics, CONF-CIAP 2022

作者： Huang, Yitao Department of Electronic Science and Technology Tongji University Shanghai201804 China

Digital image processing technology has gone through rapid development and is extensively applied in daily life and production, with the rapid development of modern information technology. It plays an inestimable role in remote sensing, medicine, recognition and other fields. This paper briefly introduces the basic concept of digital image processing, summarizes and analyses the commonly used digital image processing technology and the latest scientific research achievements from four aspects, and puts forward the future development direction of digital image processing. In the future, it will pay more attention to artificial intelligence algorithms and achieve better processing results by optimizing the logical structure. By using the simplified image algorithm, the application scope of digital image processing will gradually expand, and will develop in the direction of miniaturization, intelligence, and convenience. © Published under licence by IOP Publishing Ltd.

关键词： Artificial intelligence

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Exploring CNN for urban area Extraction from Polarimetric SAR data

Exploring CNN for urban area Extraction from Polarimetric SA...

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Geoscience and remote sensing Symposium (M2GARSS), Mediterranean and Middle-East

作者： I. Hammouda H. Latrache B. Souissi M. Ouarzeddine Faculty of Electronics and Computer Science Image Processing and Radiation Laboratory University of Sciences and Technology HouariBoumediene (USTHB) Algiers Algeria Computational Linguistic Departement CRSTDLA Algiers Algeria

ISBN: (数字)9798350358582

ISBN: (纸本)9798350358599

The convolutional neural networks CNNs have been widely used in pattern recognition and image processing in the last ten years. This paper intends to identify the urban area from polarimetric Synthetic aperture radar (PolSAR) data using the CNN model. This model has shown its effectiveness in classification. For this purpose, we propose to use a feature vector that contains the elements of the circular matrix and the calculated parameters from Cloude decomposition (entropy, alpha angle and anisotropy). The used elements are roll invariants, which make them not sensitive to the orientation angle, which can create ambiguity between oriented buildings and vegetation. To test the performance of the proposed method, ALOS-2/PALSAR-2L-band and AIRSAR C-band over the San-Francisco, USA were used. The results show that man-made targets could be effectively discriminated from natural scenes using the proposed method.

关键词： Radar remote sensing C-band Urban areas Neural networks Vegetation mapping Vectors Polarimetric synthetic aperture radar

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M²-YOLOX: A Novel Method for Object Detection Based on an Improved YOLOX Algorithm Introducing a Global Attention Mechanism and a Feature Enhancement Module

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INTERNATIONAL JOURNAL OF pattern recognition AND ARTIFICIAL INTELLIGENCE 2024年第1期38卷 2359025-2359025页

作者： Bai, Xiaofeng Wu, Kaijun Bai, Chenshuai Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou Gansu Peoples R China

Deep learning-based algorithms for detecting objects in remote sensing images have produced excellent results recently. However, the target recognition and classification process of remote sensing images has problems such as dense targets, uneven distribution, large-scale changes and complex backgrounds. In order to improve the effectiveness of existing detection methods, based on the YOLOX algorithm, a remote sensing image object detection algorithm introducing Global Attention Mechanism (GAM) and Feature Enhancement Module (FEM) proposed, named the M2-YOLOX(GAM+FEM+YOLOX) algorithm. First, a novel GAM module is developed that employs a sequential channel-space attention mechanism and redesigns the Convolutional Block Attention Module (CBAM), to address the issues of low effective information extraction and weak information representation of the feature map in the backbone network. CBAM is capable of amplifying global dimensional interaction features while reducing information dispersion as well. Second, the goal of the FEM is to improve the target feature extraction capabilities of the backbone feature extraction network by fusing numerous perceptual field features in lower-level feature maps. Then, the Flexible Rectified Linear Unit (FReLU) activation function is introduced under the action of feature fusion and global attention mechanism. Four-way feature map output in Feature Pyramid Networks (FPN) with Non-Maximum Suppression (NMS) and score filtering for object detection and output results. In comparison to the YOLOX algorithm, the experimental results show that the mAP value of the M2-YOLOX algorithm is improved by 0.0123, the LAMR value is decreased by 0.0150, the Precision rate value is increased by 0.105450, the Recall value is increased by 0.053250, and the mF1 value is increased by 0.0425.

关键词： Global attention mechanism feature enhancement module FReLU activation function feature Pyramid network feature fusion RSOD remote sensing image dataset

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Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero-shot remote sensing image scene classification

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND remote sensing 2021年 179卷 145-158页

作者： Li, Yansheng Kong, Deyu Zhang, Yongjun Tan, Yihua Chen, Ling Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518000 Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China

Although deep learning has revolutionized remote sensing (RS) image scene classification, current deep learningbased approaches highly depend on the massive supervision of predetermined scene categories and have disappointingly poor performance on new categories that go beyond predetermined scene categories. In reality, the classification task often has to be extended along with the emergence of new applications that inevitably involve new categories of RS image scenes, so how to make the deep learning model own the inference ability to recognize the RS image scenes from unseen categories, which do not overlap the predetermined scene categories in the training stage, becomes incredibly important. By fully exploiting the RS domain characteristics, this paper constructs a new remote sensing knowledge graph (RSKG) from scratch to support the inference recognition of unseen RS image scenes. To improve the semantic representation ability of RS-oriented scene categories, this paper proposes to generate a Semantic Representation of scene categories by representation learning of RSKG (SR-RSKG). To pursue robust cross-modal matching between visual features and semantic representations, this paper proposes a novel deep alignment network (DAN) with a series of well-designed optimization constraints, which can simultaneously address zero-shot and generalized zero-shot RS image scene classification. Extensive experiments on one merged RS image scene dataset, which is the integration of multiple publicly open datasets, show that the recommended SR-RSKG obviously outperforms the traditional knowledge types (e.g., natural language processing models and manually annotated attribute vectors), and our proposed DAN shows better performance compared with the state-of-the-art methods under both the zero-shot and generalized zero-shot RS image scene classification settings. The constructed RSKG will be made publicly available along with this paper (https://***/kdy2021/SR-RSKG).

关键词： Robust deep alignment network (DAN) remote sensing knowledge graph (RSKG) remote sensing image scene classification Zero-shot learning (ZSL) Generalized zero-shot learning (GZSL)

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The recognition of remote sensing image based on Federated Generation Adversarial Network

The Recognition of Remote Sensing Image based on Federated G...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Wang, Jingyu Yao, Zhijie Jing, Xiaojun Mu, Junsheng Beijing University of Posts and Telecommunications Beijing School of Information and Communication Engineering China

ISBN: (纸本)9798350321524

Recently, the processing of Synthetic Aperture Radar(SAR) has been gradually transitioning from traditional electromagnetic simulation to intelligent detection and recognition. As an intelligent architecture, Federated Learning(FL) has attracted increasing attention for the processing of SAR images due to its capacity for data protection and cooperative gain. In this paper, a new FL scheme named Fed-CWGP is proposed, where Wasserstein Generation Adversarial Network with gradient penalty(WGAN-gp) is considered to expand samples locally for the issue of few-shot learning. Additionally, the Conditional Generation Adversarial Network(CGAN) is used to control the direction of sample generation, solving the problem of non-independent and non-identical distribution of data distribution between nodes. This proposed scheme allows local classifiers and cloud servers to exchange and collaboratively learn model parameters, which improves the performance of global and node classification models and reduces the transmission delay between models. To enhance the privacy of model uploading, differential privacy is introduced on the gradient of each node model. Simulation results state that the proposed Fed-CWGP outperforms the classical schemes. © 2023 IEEE.

关键词： Synthetic aperture radar

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Generalized Classification of Satellite image Time Series with Thermal Positional Encoding

Generalized Classification of Satellite Image Time Series wi...

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IEEE/CVF Conference on Computer Vision and pattern recognition (CVPR)

作者： Nyborg, Joachim Pelletier, Charlotte Assent, Ira Aarhus Univ Dept Comp Sci Aarhus Denmark Univ Bretagne Sud IRISA UMR 6074 Lorient France FieldSense AS Aarhus Denmark

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Large-scale crop type classification is a task at the core of remote sensing efforts with applications of both economic and ecological importance. Current state-of-the-art deep learning methods are based on self-attention and use satellite image time series (SITS) to discriminate crop types based on their unique growth patterns. However, existing methods generalize poorly to regions not seen during training mainly due to not being robust to temporal shifts of the growing season caused by variations in climate. To this end, we propose Thermal Positional Encoding (TPE) for attention-based crop classifiers. Unlike previous positional encoding based on calendar time (e.g. day-of-year), TPE is based on thermal time, which is obtained by accumulating daily average temperatures over the growing season. Since crop growth is directly related to thermal time, but not calendar time, TPE addresses the temporal shifts between different regions to improve generalization. We propose multiple TPE strategies, including learnable methods, to further improve results compared to the common fixed positional encodings. We demonstrate our approach on a crop classification task across four different European regions, where we obtain state-of-the-art generalization results. Our source code is available at https://***/jnyborg/tpe.

关键词： Training Satellites image coding Time series analysis Crops Europe Encoding

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CNN-based image Color Consistency for Multi-source Satellite images: A Case Study in China's Water Diversion Project Application 9

CNN-based Image Color Consistency for Multi-source Satellite...

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9th Annual International Conference on Network and Information Systems for Computers, ICNISC 2023

作者： Chen, Sixin Lv, Qiang Cui, Peng Guo, Shukun Anhui Survey and Design Institute of Water Resources and Hydropower Co. Ltd. Hefei China Anhui Provincial Group Limited for Yangtze-to-Huaihe Water Diversion Hefei China

ISBN: (纸本)9798350306682

Color consistency processing is important for optical satellite image mosaicking to obtain necessary geographic information, then to serve land and water resource monitoring and many other public fields. In recent years, convolutional neural networks (CNN) have been successfully used in image processing applications such as image classification, change detection, and fusion. However, there are a few studies on image color consistency, especially based on Chinese high-resolution satellite images. In this study, we proposed a new CNN-based image color consistency method that used high-resolution Google images as a reference. Combined with a CNN-based method of image fusion, we produced large-area color-consistent mosaics with high resolution based on multi-source satellite images. The loss functions and network parameters were optimized based on our dataset. We successfully applied the proposed method in one of China's mega water diversion projects 'Yangtze-Huai River Diversion Project' and found that it had better performance when compared with the existing methods. The proposed method does not only provide a basic tool in image mosaicking for remote sensing applications but also acts as a precedent for subsequent automated image change detection and object recognition based on deep learning. © 2023 IEEE.

关键词： Convolutional neural networks

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In Situ Monitoring and recognition of Printing Quality in Electrohydrodynamic Inkjet Printing via Machine Learning

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JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2024年第11期146卷 110901页

作者： Jiang, Liangkui Wolf, Rayne Alharbi, Khawlah Qin, Hantang Univ WisconsinMadison Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI 53706 USA Univ WisconsinMadison Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI 53706 USA Univ WisconsinMadison Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI 53706 USA Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI 53706 USA

Electrohydrodynamic (EHD) printing is an additive manufacturing technique capable of microscale and nanoscale structures for biomedical, aerospace, and electronic applications. To realize stable printing at its full resolution, the monitoring of jetting behavior while printing and optimization of the printing process are necessary. Various machine vision control schemes have been developed for EHD printing. However, in-line machine vision systems are currently limited because only limited information can be captured in situ toward quality assurance and process optimization. In this article, we presented a machine learning-embedded machine vision control scheme that is able to characterize jetting and recognize the printing quality by using only low-resolution observations of the Taylor Cone. An innovative approach was introduced to identify and measure cone-jet behavior using low-fidelity image data at various applied voltage levels, stand-off distances, and printing speeds. The scaling law between voltages and the line widths enables quality prediction of final printed patterns. A voting ensemble composed of k-nearest neighbor (KNN), classification and regression tree (CART), random forest, logistic regression, gradient boost classifier, and bagging models was employed with optimized hyperparameters to classify the jets to their corresponding applied voltages, achieving an 88.43% accuracy on new experimental data. These findings demonstrate that it is possible to analyze jetting status and predict high-resolution pattern dimensions by using low-fidelity data. The voltage analysis based on the in situ data will provide additional insights for system stability, and it can be used to establish the error functions for future advanced control schemes.

关键词： electrohydrodynamic inkjet printing Taylor cone machine learning high-speed imaging inspection and quality control micro- and nano-machining and processing sensing monitoring and diagnostics

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Multispectral Semantic Land Cover Segmentation From Aerial imagery With Deep EncoderDecoder Network

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IEEE GEOSCIENCE AND remote sensing LETTERS 2022年 19卷

作者： Liu, Chengxin Du, Shuaiyuan Lu, Hao Li, Dehui Cao, Zhiguo Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Developing accurate algorithms for agricultural pattern recognition from aerial imagery has become increasingly important due to the prevalence of unmanned aerial vehicles (UAVs). This letter introduces a deep encoderx2013;decoder network for semantic land cover segmentation, where the goal is to classify six anomaly categories from multispectral aerial imagery. Since aerial imagery exhibits specific characteristics and visual challenges in this imaging domain, existing semantic segmentation models are not plug-and-play. Starting from a state-of-the-art segmentation model, we present a step-by-step analysis of key challenges and also reveal our observations in addressing these challenges. In particular, we investigate on how to exploit data prior knowledge, how to deal with sample imbalance, and how to encode global semantic and contextual information to improve segmentation. Experiments on a recent large-scale aerial land cover data set demonstrate that our method achieves compelling performance against other state-of-the-art approaches. Our results and insights can provide references for practitioners working in this field when dealing with similar segmentation problems.

关键词： image segmentation Semantics Training Convolution Decoding Agriculture Measurement Aerial imagery class imbalance context encoding convolutional neural network (CNN) semantic segmentation

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