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Use of Sentinel-1 Dual Polarization Multi-Temporal Data with Gray Level Co-Occurrence Matrix Textural Parameters for Building Damage Assessment

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pattern recognition AND image ANALYSIS 2021年第2期31卷 240-250页

作者： Akhmadiya, Asset Nabiyev, Nabi Moldamurat, Khuralay Dyussekeyev, Kanagat Atanov, Sabyrzhan S Seifullin Kazakh Agro Tech Univ Dept Radio Engn Elect & Telecommun Nur Sultan 010011 Kazakhstan Eurasian Natl Univ Dept Comp & Software Engn Nur Sultan 010011 Kazakhstan

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. The main task of this research paper is the collection of most relevant scientific research in the field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in the present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in the end, which show, which methods and radar remote sensing data give more accuracy and more available for building damage assessment. Low-resolution Sentinel-1A/B radar remote sensing data are recommended as free available for monitoring of destruction on a degree microregion level. Change detection and texture-based methods are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found better for the separation of collapsed and intact buildings. Dual polarization (VV, VH) backscattering coefficients and coherence coefficients (before the earthquake and coseismic) were fully utilized for this study. There were defined the better multi variable for supervised classification of none building, damaged and intact buildings features in urban areas. In this work, we were achieved overall accuracy 0.77, producer's accuracy for none building is 0.84, for damaged building case 0.85, for intact building 0.64. Amatrice town was chosen as most damaged from the 2016 Central Italy Earthquake.

关键词： radar remote sensing building damage assessment change detection method Gray Level Co-occurrence Matrix

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Learning Soft Estimator of Keypoint Scale and Orientation with Probabilistic Covariant Loss

Learning Soft Estimator of Keypoint Scale and Orientation wi...

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

作者： Yan, Pei Tan, Yihua Xiong, Shengzhou Tai, Yuan Li, Yansheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Estimating keypoint scale and orientation is crucial to extracting invariant features under significant geometric changes. Recently, the estimators based on self-supervised learning have been designed to adapt to complex imaging conditions. Such learning-based estimators generally predict a single scalar for the keypoint scale or orientation, called hard estimators. However, hard estimators are difficult to handle the local patches containing structures of different objects or multiple edges. In this paper, a Soft Self-Supervised Estimator (S3Esti) is proposed to overcome this problem by learning to predict multiple scales and orientations. S3Esti involves three core factors. First, the estimator is constructed to predict the discrete distributions of scales and orientations. The elements with high confidence will be kept as the final scales and orientations. Second, a probabilistic covariant loss is proposed to improve the consistency of the scale and orientation distributions under different transformations. Third, an optimization algorithm is designed to minimize the loss function, whose convergence is proved in theory. When combined with different keypoint extraction models, S3Esti generally improves over 50% accuracy in image matching tasks under significant viewpoint changes. In the 3D reconstruction task, S3Esti decreases more than 10% reprojection error and improves the number of registered images.

关键词： Point cloud compression Three-dimensional displays image matching Self-supervised learning Probabilistic logic Feature extraction Prediction algorithms

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机载广域遥感图像的尺度归一化目标检测方法

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吉林大学学报（工学版） 2024年第8期54卷 2329-2337页

作者：朱圣杰王宣徐芳彭佳琦王远超中国科学院长春光学精密机械与物理研究所长春130033 中国科学院大学大珩学院北京100049 驻长春地区第一军事代表室长春130033 上海机电工程研究所上海201109

针对机载广域遥感图像的目标尺寸变化大、背景噪声复杂以及局部目标密集给目标检测任务带来的困难,本文通过优化分割方法统一输入图像的目标像素尺寸,并以此简化模型结构提出了一种尺度归一化卷积神经网络模型MNNet。为增强局部之间的... 详细信息

针对机载广域遥感图像的目标尺寸变化大、背景噪声复杂以及局部目标密集给目标检测任务带来的困难,本文通过优化分割方法统一输入图像的目标像素尺寸,并以此简化模型结构提出了一种尺度归一化卷积神经网络模型MNNet。为增强局部之间的特征关联,本文设计了全局连接块(SGC),有效提高了检测的精度。针对现有非极大值抑制算法的超参数依赖经验设置的问题,本文提出了一种自适应非极大值抑制方法(DNMS),降低了模型的部署难度。在RSF数据集上的测试结果表明:本文模型的检测平均精度(AP)高于其他模型5.0%以上,在检测速度上达到了57.7帧/s,可以满足遥感图像的检测任务需求。

关键词：模式识别与智能系统计算机视觉目标检测遥感图像卷积神经网络

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RTrPPG: An Ultra Light 3DCNN for Real-Time remote Photoplethysmography

RTrPPG: An Ultra Light 3DCNN for Real-Time Remote Photopleth...

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

作者： Botina-Monsalve, D. Benezeth, Y. Miteran, J. Univ Bourgogne Franche Comte ImViA EA7535 Besancon France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The acquisition of remote photoplethysmography (rPPG) signals is important in multiple applications. Recently, deep-learning-based approaches such as 3D convolutional networks (3DCNNs) have outperformed traditional hand-crafted methods. However, despite their robust modeling ability, it is well known that large 3DCNN models have high computational costs and may be unsuitable for real-time applications. In this paper, we propose a study of the 3DCNN architecture, finding the best compromise between heart rate measurement precision and inference time. The fast inference is obtained decreasing the input size while the precision performance is obtained introducing a new time and frequency-based loss function by adding the signal-to-noise-ratio component to the regular Pearson's correlation loss function. In addition, changing the input color space from RGB to YUV also improved heart rate measurement precision. Using the VIPL-HR database, we retained the HR mean absolute error at 3.99 bpm which is comparable to 3.87 bpm of the state-of-the-art, while the GPU and CPU inference process improved around 88% from 51.77 ms to 2.32 ms in GPU and from 241.57 ms to 28.65 ms in CPU. The resulting network is called Real-Time rPPG (RTrPPG). We release the RTrPPG source code to encourage reproducibility(1).

关键词： Time-frequency analysis Three-dimensional displays image color analysis Computational modeling Graphics processing units Photoplethysmography Real-time systems

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pattern recognition in Apple Orchards During Dormancy and Bud Development 4th

Pattern Recognition in Apple Orchards During Dormancy and ...

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4th International Conference on Smart Multimedia, ICSM 2024

作者： Sehgal, Bhavya Gandhi, Ritik Cheng, Irene Department of Computing Science University of Alberta EdmontonABT6G 2R3 Canada

ISBN: (纸本)9783031824746

Effective management of apple orchards during dormancy and bud development stages is crucial for optimizing fruit production and tree health. Automation using computer vision and deep learning techniques offers a promising solution for tree segmentation and labeling in complex outdoor environments. However, existing methods face challenges in accurately segmenting trees with bare branches and adapting to changing perspectives. This paper presents innovative solutions for bare tree segmentation and labeling, combining depth sensing and advanced image processing with deep learning approaches. The proposed methods address the limitations of current techniques, offering improved accuracy and robustness. We also introduce a novel tree labeling algorithm that utilizes spatial information and YOLOv4 for accurate tree identification and labeling across video frames. Experimental results demonstrate the effectiveness of our approach in handling complex outdoor conditions and bare tree segmentation. Our research contributes to advancing automated orchard management and precise monitoring during dormancy and bud development stages, enhancing fruit quality and overall orchard productivity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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A Convolutional Fully Connected Structure with Hard Sample Memory Pool for Land Use Classification 32

A Convolutional Fully Connected Structure with Hard Sample M...

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Conference on pattern recognition and Tracking XXXII

作者： Xie, Dong Depoian, Arthur C., II Jaques, Lorenzo E. Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (数字)9781510643086

ISBN: (纸本)9781510643086

As one of the classic fields of computer vision, image classification has been booming with the improvement of chip performance and algorithm efficiency. With the rapid progress of deep learning in recent decades, remote sensing land cover and land use image classification has ushered in a golden period of development. This paper presents a new deep learning classifier to classify remote sensing land cover and land use images. The approach first uses multi-layer convolutional neural networks to extract the image features, attached through a fully-connected neural network to generate the sample loss. Then, a hard sample memory pool is created to collect the samples with large losses during the training. A batch of hard samples is randomly extracted from the memory pool to participate in the training of the convolutional fully connected model so that the model becomes more robust. Our method is validated by testing the classic remote sensing land cover and land use dataset. Compared with the previous popular classification algorithm, our algorithm can classify images more accurately with a shorter training iteration.

关键词： computer vision remote sensing deep learning neural networks land use classification

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Automatic detection and identification of ground vehicles with YOLO-based deep learning using MuSES-generated OPIR synthetic imagery 2

Automatic detection and identification of ground vehicles wi...

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Artificial Intelligence for Security and Defence Applications II 2024

作者： Klein, Mark D. Edel, Zachary J. Packard, Corey D. Hendrickson, Jacob N. Gibbs, Scott C. ThermoAnalytics Inc. 23440 Airpark Blvd. CalumetMI49913 United States

ISBN: (纸本)9781510681200

Space-based sensor platforms, including both current and planned future satellites, are capable of surveilling Earth-based objects and scenes from high altitudes. Overhead persistent infrared (OPIR) is a growing surveillance technique where thermal-waveband infrared sensors are deployed on orbiting satellites to look down and image the Earth. Challenges include having sufficient image resolution to detect, differentiate and identify ground-based objects while monitoring through the atmosphere. Demonstrations have shown machine learning algorithms to be capable of processing image-based scenes, detecting and recognizing targets amongst surrounding clutter. Performant algorithms must be robustly trained to successfully complete such a complex task, which typically requires a large set of training data on which statistical predictions can be based. Electro-optical infrared (EO/IR) remote sensing applications, including OPIR surveillance, necessitate a substantial image database with suitable variation for adept learning to occur. Diversity in background scenes, vehicle operational state, season, times of day and weather conditions can be included in training image sets to ensure sufficient algorithm input variety for OPIR applications. However, acquiring such a diverse overhead image set from measured sources can be a challenge, especially in thermal infrared wavebands (e.g., MWIR and LWIR) when adversarial vehicles are of interest. In this work, MuSES™ and CoTherm™ are used to generate synthetic OPIR imagery of several ground vehicles with a range of weather, times of day and background scenes. The performance of a YOLO ("you only look once") deep learning algorithm is studied and reported, with a focus on how image resolution impacts algorithm detection/recognition performance. The image resolution of future space-based sensor platforms will surely increase, so this study seeks to understand the sensitivity of OPIR algorithm performance to overhead image resolution.

关键词： image resolution

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A Multi-Level Convolution Pyramid Semantic Fusion Framework for High-Resolution remote sensing image Scene Classification and Annotation

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IEEE ACCESS 2021年 9卷 18195-18208页

作者： Sun, Xiongli Zhu, Qiqi Qin, Qianqing Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430072 Peoples R China China Univ Geosci Sch Geog & Informat Engn Wuhan 430074 Peoples R China State Key Lab Resources & Environm Informat Syst Beijing Peoples R China

High spatial resolution (HSR) imagery scene classification has become a hot research topic in remote sensing. Scene classification method based on the handcrafted features, such as the bag-of-visual-words (BoVW) model, describes an image by extracting local features of the scene and mapping them to the dictionary space, but usually uses a shallow structure and loses the spatial distribution characteristics of the scene. The method based on deep learning extracts hierarchical features to describe the scene, which can maintain the spatial position information well. However, deep features in different levels have scale recognition restrictions for multi-scale ground objects, and cannot understand complex scenes well. In this paper, the multi-level convolutional pyramid semantic fusion (MCPSF) framework is proposed for HSR imagery scene classification. Differing from previous scene classification methods, which integrate the feature of different levels directly, of which the fusion features have large differences in both sparsity and eigenvalue magnitude, MCPSF integrates multi-level semantic features extracted by BoVW model and convolutional neural network (CNN) model. In MCPSF, two convolution pyramid feature expression strategies are proposed to enhance the ability of capturing multi-scale land objects, i.e., local and convolutional pyramid based BoVW (LCPB) model and local and convolutional pyramid based pooling-stretched (LCPP) model. The effectiveness of the proposed method is verified on 21-class UC Merced (UCM) dataset and 30-class Aerial image Dataset (AID). The framework was also transferred toa case study of scene annotation in Wuhan. The proposed framework significantly improves the performance when compared with other state-of-the-art methods.

关键词： Feature extraction image analysis Semantics remote sensing Visualization Convolution Training High spatial resolution image scene classification bag of visual words feature pyramid multi-level remote sensing

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Research on Intelligent recognition System of Crop Growth Monitoring by UAV remote sensing image

Research on Intelligent Recognition System of Crop Growth Mo...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Wenhuan Xie Institute of Agricultural Remote Sensing and Information Heilongjiang Academy of Agricultural Sciences Harbin China

This paper introduces a remote sensing load data storage system of UAV based on SOPC. This paper presents a method of data storage using multiple solid-state drives. The hard disk can be read and written in parallel. Use SDRAM as a buffer for data. In order to realize effective processing of crop data, a superblock file system is adopted. A number of image experiments with overlapping areas were carried out using unmanned aircraft. A new remote sensing image processing algorithm is proposed. The algorithm uses the spectral features, geometric features and texture features of multiple targets for classification. Relief-Pearson features are used to reduce the dimensionality of images, and redundant features with strong correlation and recognition performance are removed. Finally, according to the selected features, a multi-target recognition method is established and compared with the results of manual recognition. Based on the experimental data collected in China's corn production experiment and demonstration base, it is found that the new algorithm can simulate the real shadow well, and the overall accuracy is 0.9473. The F1 value reached 0.9184.

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SAR Self-Enhanced by Electro-optical Network (SARSEEN) 31

SAR Self-Enhanced by Electro-optical Network (SARSEEN)

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Conference on Signal processing, Sensor/Information Fusion, and Target recognition XXXI

作者： Tran, Kenneth Sakla, Wesam Krim, Hamid North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94550 USA North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (数字)9781510651210

ISBN: (纸本)9781510651210;9781510651203

In this work, we investigate the relationship between paired synthetic aperture radar (SAR) and optical images. SAR sensors have the capabilities of penetrating clouds and capturing data at night, whereas optical sensors cannot. Due to this, we are particularly interested in the case where we have access to both modalities during training, but only the SAR during test time. Our goal is to improve the performance of SAR only models by learning a self-generated prior about optical imagery. To that end, we developed a framework that consists of a deep neural network that inputs a SAR image and predicts a Canny edge map of the optical image. The Canny edge map retains structural information about the optical image, while removing superfluous high frequency details that is hard to generate from the SAR image. For downstream tasks, we concatenate the predicted edge map to the original SAR image and use that as the input. Our experiments show that by using this predicted edge map as additional information, we can outperform the same model that is only given the SAR image.

关键词： S AR multimodal fusion remote sensing

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