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1st International Conference on Cyber Warfare, Security and Space Research, SpacSec 2021

作者： Mathur, Pratistha Kavita Manipal University Jaipur Jaipur India

ISBN: (纸本)9783031157837

Every human being is capable of recognizing patterns. There are various problems in domains like bioinformatics, data mining, document classification, image analysis, remote sensing, and speech recognition where automatic recognition is needed. The rapid growth in computing power enables the development of such applications. remote sensing is one of the areas where a huge amount of data is available. In this paper, we have explored the supervised and unsupervised ANN for classifying satellite images. The adaptive pattern recognition capability of ANN is used to simulate the spatial variations in the images of an object/area. Multi-layer feed-forward neural network using backpropagation learning method and self-organizing feature map (SOFM) has experimented. Experiments are implemented using MATLAB. It is hoped that this multidisciplinary work will be useful for researchers and image processing users in the field of computer science and remote sensing. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： MATLAB

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CVIPPR 2024 - 2nd Asia Conference on Computer Vision, image processing and pattern recognition

CVIPPR 2024 - 2nd Asia Conference on Computer Vision, Image ...

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2nd Asia Conference on Computer Vision, image processing and pattern recognition, CVIPPR 2024

ISBN: (纸本)9798400716607

The proceedings contain 59 papers. The topics discussed include: 3D point clouds simplification based on low-dimensional contour feature extraction;3D human pose estimation using pressure images on a smart chair;combining doses from internal and external radiotherapies for cervical cancer with successive image registration;attention mechanism-based feature fusion generative network for infrared-visible person re-identification;a vision-based remote assistance method and its application in object transfer;research on model-free 6D object pose estimation based on vision 3D matching;active exploration of modality complementarity for multimodal sentiment analysis;self-attention-based multi-scale feature fusion network for road ponding segmentation;and low light image enhancement algorithm based on edge and color information.

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High-resolution sliding-spotlight spaceborne SAR imaging algorithm for Ka-band

High-resolution sliding-spotlight spaceborne SAR imaging alg...

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IET International Radar Conference 2023, IRC 2023

作者： Xiao, Feng Jing, Maoqiang He, Dehua Zhang, Guo Zu, JiaGuo Yu, SongBai Sun, HanWei Wang, XinMin Beijing Institute of Radio Measurement Beijing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing of Wuhan University Wuhan China Beijing China

ISBN: (纸本)9781839539954

High-band spaceborne SAR offers image-like optics and broadens the application range of microwave remote sensing on space-based platforms. The sliding-spotlight mode fulfils the high-resolution detection requirements for target recognition in remote sensing and has gained significant attention and application in the industry. Given the differences between high-band spaceborne SAR data processing and traditional low-band spaceborne SAR data processing in terms of orbital accuracy analysis, key parameter calculation of imaging, algorithm acceleration, and parallelization, this study conducts an analysis of data accuracy, imaging, and image quality using real data from Luojia-2. The results demonstrate that the proposed imaging process method achieves high-precision imaging of the sliding-spotlight SAR real data from Luojia-2. © The Institution of Engineering & Technology 2023.

关键词： Network security

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Novel Vegetation Mapping Through remote sensing images Using Deep Meta Fusion Model

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Intelligent Automation & Soft Computing 2023年第6期36卷 2915-2931页

作者： S.Vijayalakshmi S.Magesh Kumar Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiTamilnaduIndia

Preserving biodiversity and maintaining ecological balance is essential in current environmental *** is challenging to determine vegetation using traditional map classification *** primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral *** is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed *** proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation *** architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation *** novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight *** system considers detailing feature areas to improve classification accuracy and reduce processing *** proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 *** training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM *** system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.

关键词： Vegetation mapping deep learning machine learning remote sensing data image processing

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Evaluation of Internal and External Starch Changes in Apples Using Hyper-Spectral Analyses in the NIR-SWIR 14

Evaluation of Internal and External Starch Changes in Apples...

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14th Workshop on Hyperspectral Imaging and Signal processing: Evolution in remote sensing, WHISPERS 2024

作者： Perez, A. K. Ruvalcaba Siess, G. Janunts, N. Boehm, V. Tuennermann, A. Institute of Applied Physics Friedrich Schiller University Abbe Center of Photonics Jena07745 Germany Ams OSRAM Group Jena07745 Germany Institute of Nutritional Sciences Friedrich Schiller University Jena07743 Germany Fraunhofer Institute for Applied Optics and Precision Engineering IOF Jena07745 Germany

ISBN: (纸本)9798331513139

Starch pattern Index (SPI) is one of the most common parameters used to determine the degree of ripeness of fruits. To predict the degree of ripeness, SPI is combined with chemometric analysis. Nevertheless, the spectral correlation between external and internal starch changes has not been evaluated. This study evaluates the internal and external changes of starch in Gala apples during the 2024 season by covering the near-infrared (NIR) and short-wave infrared (SWIR) spectral ranges from 700nm to 2300nm. The evaluation consisted of a multispectral NIR-SWIR camera analysis and spectral measurements along the surface. Results revealed a pattern on the apple cortex similar to SPI without the use of iodine implementation of a false negative RGB image. Furthermore, the different SPIs were also distinguished by the spectral measurements along the surface, allowing a correlation in both instances with the same phenomenon. © 2024 IEEE.

关键词： Hyper-spectral analysis Post-Harvesting Spectral sensors Starch pattern index Sustainability

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Hyperspectral image classification based on spectral feature extraction

Hyperspectral image classification based on spectral feature...

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

作者： Wang, Li Wang, Wei Department of Electronic Engineering Xi'an Aeronautical Institute Shaanxi Xi'an710077 China

ISBN: (纸本)9781510666351

Hyperspectral remote sensing can synchronously obtain the surface coverage space image and continuous spectral data, and can realize fine classification and recognition of ground objects. The motivation for this paper is to search an effective specteal feature extraction method for hyperspectral classification. The main methods used to extract spectral features of hyperspectral images are principal component analysis (PCA) and linear discriminant analysis (LDA). The realization of classification based on k-nearest neighbor (KNN) classifier is discussed. The training set and test set of Pavia University, Indian Pines and Salinas are selected as the data source, and the selection experiment of algorithm parameters is set, and the classification performance under different feature extraction methods is compared and analyzed. The experimental results show that with the parameters of OA, PA and Kappa based on the confusion matrix as the evaluation indicators, the classification accuracy based on LDA method is higher, and the effective classification of ground objects can be achieved. The results could provide a basis for further research on hyperspectral image classification based on spatial spectral feature extraction. © 2023 SPIE.

关键词： Principal component analysis

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Multi-Plant Leaf Disease Detection and Classification using Convolution Neural Network Aad image processing

Multi-Plant Leaf Disease Detection and Classification using ...

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2024 IEEE International Conference on Recent Advances in Science and Engineering Technology, ICRASET 2024

作者： Aishwarya, S.S. Ravikumar, Pushpa Chaithra, I.V. Sagar, Deeksha AIT Adichunchanagiri Institute of Technology Karnataka Chikmagalur India AIT Adichunchanagiri Institute of Technology Department of Computer Science and Engineering Karnataka Chikmagalur India

ISBN: (纸本)9798350388602

Every nation's basic need is the agricultural products. Disease-ridden leaves have an effect on the nation's agricultural output and financial resources. This research provides a deep learning based plant leaf disease classification and detection method. The website (Plant Village dataset) provided the photos that were used. Since tomatoes, peppers, and potatoes are the most prevalent plant species worldwide and in Iraq specifically, we have used them as examples of certain plant species in our study. There are 20636 photos of plants and their illnesses in this data set. Plant leaf diseases are sorted into 15 class labels in the proposed system using a convolutional neural network (CNN). Of these classes, 12 are dedicated to diseases. Several various plants that were found, including three groups for healthy leaves and various microorganisms including fungus. Consequently, we achieved exceptional precision in both training and testing;specifically, we achieved 98.29% accuracy in training and 98.029% accuracy in testing across all utilized data sets. pattern recognition is necessary in order to diagnose these disorders. Using remote sensing techniques to investigate multi- and hyper-spectral picture captures is a frequent approach in this situation. Techniques that use this strategy frequently use digital image processing capabilities to accomplish their objectives. There are numerous kinds of pattern recognition algorithms that provide accurate disease detection. Plant diseases are currently detected using principal component analysis and back propagation. These algorithms are acquired by neural network training supervision. © 2024 IEEE.

关键词： Convolutional neural networks

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A fuzzy spectral clustering algorithm for hyperspectral image classification

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IET image processing 2021年第12期15卷 2810-2817页

作者： Li, Kang Xu, Jindong Zhao, Tianyu Liu, Zhaowei YanTai Univ Sch Comp & Control Engn Yantai Peoples R China

Spectral clustering is an unsupervised clustering algorithm, and is widely used in the field of pattern recognition and computer vision due to its good clustering performance. However, the traditional spectral clustering algorithm is not suitable for large-scale data classification, such as hyperspectral remote sensing image, because of its high computational complexity, and it is difficult to characterize the inherent uncertainty of the hyperspectral remote sensing image. This paper uses fuzzy anchors to process hyperspectral image classification and proposes a novel spectral clustering algorithm based on fuzzy similarity measure. The proposed algorithm utilizes the fuzzy similarity measure to obtain the similarity between the data points and the anchors, and then gets the similarity matrix. Finally, spectral clustering is performed on the similarity matrix to compute the classification results. The experimental results on the hyperspectral remote sensing image data sets have demonstrated the effectiveness of the proposed algorithm, and the introduction of fuzzy similarity measure gives rise to a more robust similarity matrix. Compared with existing methods, the proposed algorithm has a better classification result on the hyperspectral remote sensing image, and the kappa coefficient obtained by the proposed algorithm is 2% higher than the traditional algorithms.

关键词： Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Optical, image and video signal processing Computer vision and image processing techniques Data handling techniques Combinatorial mathematics

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Building edge detection from very high-resolution remote sensing imagery using deep learning

Building edge detection from very high-resolution remote sen...

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2023 Annual Conference, ASPRS 2023

作者： Prabhakar, D. Garg, P.K. Civil Engineering Department Indian Institute of Technology Roorkee Uttarakhand India

Detection of Building edges is crucial for building information extraction and description. Extracting structures from large-scale aerial images has been utilized for years in cartography. With commercially available high-resolution satellites, many aerial photography usages can now employ satellite imagery. Edge detection is focused on pinpointing distinct transitions between greyscale image regions and attributing their origins to underlying physical processes. Detecting building boundaries from very high-resolution (VHR) remote sensing data is essential for many geo-related applications, such as urban planning and management, surveying and mapping, 3D reconstruction, motion recognition, image registration, image enhancement and restoration, image compression, and more. The rapid evolution of convolutional neural networks (CNNs) has led to substantial breakthroughs in edge detection in recent years. Sharp, localized changes in brightness characterize edges in digital images. In most cases, edge detection requires some kind of image smoothing and separation. Differentiation is an ill-conditioned problem, and smoothing leads to information loss. It is challenging to create an edge detection method that works everywhere and adapts to any future processing stages. Therefore, throughout the development of digital image processing, numerous edge detectors have been created, each with its own unique set of mathematical and algorithmic properties. Several edge detectors have been developed due to application needs and the subjective nature of edge definition and characterization. We propose a deep learning technique, particularly convolutional neural networks(CNNs), that offers a promising approach to automatically learn and extract features from very high-resolution remote sensing imagery, leading to more accurate and efficient building edge detection. © 2023 International Society for Photogrammetry and remote sensing. All rights reserved.

关键词： remote sensing

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Intensity-Sensitive Similarity Indexes for image Quality Assessment 26

Intensity-Sensitive Similarity Indexes for Image Quality Ass...

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26th International Conference on pattern recognition / 8th International Workshop on image Mining - Theory and Applications (IMTA)

作者： Li, Xiaotong Armour, Wesley Univ Oxford Dept Engn Sci Oxford E Res Ctr Oxford England

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

The importance of image quality assessment (IQA) is ever increasing due to the fast paced advances in imaging technology and computer vision. Among the numerous IQA methods, Structural SIMilarity (SSIM) index and its variants are better matched to the perceived quality of the human visual system. However, SSIM methods are insufficiently sensitive, when images contain low information, where the important information only occupies a low proportion of the image while most of the image is noise-like, which is common in scientific data. Therefore, we propose two new IQA methods, InTensityWeighted SSIM index and Low-Information Similarity Index, for such low information images. In addition, auxiliary indexes are proposed to assist with the assessment. The application of these new IQA methods to natural images and field-specific images, such as radio astronomical images, medical images, and remote sensing images, are also demonstrated. The results show that our IQA methods perform better than state-of-the-art SSIM methods for differences in high-intensity parts of the input images and have similar performance to that of the original and gradient-based SSIM for differences in low-intensity parts. Different similarity indexes are suitable for different applications, which we demonstrate in our results.

关键词： image quality Computer vision Sensitivity Visual systems pattern recognition Indexes remote sensing

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