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3rd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Yin, Yutong Hohai University School of Computer and Information Nanjing211100 China

ISBN: (纸本)9781665472784

In recent years, with the increasing global climate problems, our country has been relying on remote sensing technology for early warning and post-disaster assessment in the field of disaster prevention and control for some time. However, there are many kinds of natural disasters, especially sudden natural disasters, such as earthquakes, floods, typhoons and mudslides, which are often too late to prevent, and there is also a strong recurrence between disasters, which is very likely to cause massive data interference to remote sensing recognition. Compared with the traditional convolutional neural network-based remote sensing image recognition processing, this project proposes to use the Faster-RCNN target detection algorithm, which adds the Region Proposal Networks (RPN) module to the traditional Fast CNN to generate the algorithm, making the target change detection speed and accuracy greatly improved. The change detection algorithm is based on the traditional Fast CNN. This change detection model has the natural advantage of processing remote sensing data, which can automatically and multi-levelly extract the features of complex objects, learn to discover the geometric and spatial relationships of feature targets in images from massive data, and solve complex classification or regression problems through multi-layer nonlinear networks, thus significantly improving the accuracy of remote sensing image interpretation. After the model design is completed, in order to improve the detection accuracy of the model, it is proposed to train the Faster-RCNN change detection model by acquiring a large number of original samples of remote sensing images with manually expanded samples. © 2023 IEEE.

关键词： remote sensing

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Knowledge Based Classifier and pattern recognition Technique for Satellite image Analysis 3

Knowledge Based Classifier and Pattern Recognition Technique...

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3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

作者： Nimbarte, Nita Nagpure, Aniket Sanodiya, Badal Sevatkar, Harshal Balamwar, Sanjay Yeshwantrao Chavan College of Engineering Electronics & Telecommunication Department Nagpur India Vnit Campus Maharashtra Remote Sensing Application & Centre Nagpur India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

pattern recognition is quickly becoming a popular topic of image processing. It is a branch of remote sensing, and it can be useful where it is difficult to visit and analyze geographical locations such as forestry or islands, and it can also be difficult to visit areas affected by natural disasters. To do this, a system to distinguish areas such as buildings, greenery, cultivated land, land, water, and so on must be devised. Previously, research on these themes had been conducted, but it was confined to one or two remote sensor items. This work introduces a method for identifying items such as buildings, greenery, water, and land. Because the knowledge basis for this recognition is based on analysis, it is also unbound to specific types of locations. This method is useful for determining the area under civilization as well as the percentage area of a given pattern. The image classification technique uses supervised and unsupervised classification methods. The supervised classification uses a maximum likelihood classifier. The unsupervised classification uses the ISO Cluster classifier to classify images. ArcGIS PRO and ERDAS IMAGINE software are used for algorithm analysis. © 2022 IEEE.

关键词： remote sensing

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image enhancement model for ultrasonic nondestructive testing based on deep learning super-resolution 12

Image enhancement model for ultrasonic nondestructive testin...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Zhu, Jinxuan Zhang, Xinfang He, Ruixian Kang, Luo Wang, Guoyou Huang, Yan School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Institute of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Ultrasonic nondestructive testing has been widely used in industry due to its various advantages. However. with the increasing demand for non-destructive testing accuracy, low-resolution ultrasonic images can easily lead to misidentification of defects. How to improve the resolution of ultrasonic images has become a key issue restricting development of the non-destructive testing industry. Therefore. this paper proposes a super-resolution model for ultrasonic NDT images consisting of up and down sampling layers and deep residual networks. The model learns the degraded features of the image through the up and down sampling layers, and learns the intrinsic features of the image through the deep residual network, so as to learn the feature information of the image more completely to complete the super-resolution task of the image. Experimental results on ultrasound image datasets also validate the effectiveness of the model, outperforming other models in peak signa-to-noise ratio (PSNR) and structural similarity (SSIM) metrics. © 2024 SPIE.

关键词： Nondestructive examination

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A Deep Neural Network Supported by Ground Verification Data for Active Fire Detection in MODIS

A Deep Neural Network Supported by Ground Verification Data ...

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

作者： Chen, Jinpeng Xie, Feifei Zheng, Chunkai College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781510666351

At present, the use of MODIS to detect fires mainly uses a series of thresholds to identify fire. However, in areas with high heterogeneity, the division of thresholds is more difficult, resulting in many false detections of fire, and cold fire are easily missed. The emergence of deep learning technology has improved the classification accuracy of images, but the surface information of hotspots cannot be determined only by visual interpretation of MODIS images, and the acquisition of high-quality fire samples further limits the detection performance of deep learning methods. In order to solve the above problems, this paper proposes a deep neural network fire detection method based on ground truth data. First, the data structure of the US ground wildfire dataset is studied, a spatiotemporal matching strategy between ground data and remote sensing images is designed, and a priori sample is constructed. library. Second, robust classification features are selected combining the fire physical properties and the spectral distribution of the dataset. Finally, a DNN (Deep Neural Network) for MODIS fire detection is constructed according to the image characteristics. Application experiments were carried out in three typical scenarios and compared with MODIS fire products. The results show that the improved method improves the temporal and spatial adaptability of fire detection, and the extraction of small fire in farming areas is better than MODIS fire products;especially in the suburbs Significant reduction of spurious fire in region extraction, false positives decreased by 19.8%. This method provides a new reference for coarse resolution satellite data to detect fires, and effectively improves the efficiency of fire detection. © 2023 SPIE.

关键词： remote sensing

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SDNet: Sandwich decoder network for waterbody segmentation in remote sensing imagery

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IEEE Journal of Selected Topics in Applied Earth Observations and remote sensing 2025年 18卷 13895-13911页

作者： Ni, Hao Li, Jianfeng Wang, Chenxu Zhou, Zhiquan Wang, Xinsheng Harbin Institute of Technology at Weihai School of Information Science and Engineering Weihai264209 China

Waterbody extraction is essential for monitoring surface changes and supporting disaster response. However, differences in morphology, dimensions, and spectral reflectance, make it problematic to segregate waterbodies accurately in remote sensing (RS) photographs. Although self-attention (SA) and convolutional neural networks (CNN) have demonstrated strong modeling capabilities on RS image recognition, their efficiency and expressiveness still deserve further optimization. In this paper, we propose SDNet, an innovative deep learning structure designed to improve segmentation accuracy and scalability in waterbody extraction while minimizing computational costs. SDNet introduces a Two-Step Self-Attention (TSSA) strategy to selectively aggregate spatial features and a Sparse-Tight pattern Fusion (STPF) module to capture context across various manipulation spaces, enhancing axial and space information fusion. The network's sandwich-shaped decoder structure allows for easy integration with mainstream encoders. Compared to previous influential methods, SDNet exhibits superior sensitivity in detecting small waterbodies and delivers better overall performance. Experiments performed on public benchmark datasets, like MSRWD, UAVid, and LoveDA, indicate that SDNet exceeds prior state-of-the-art techniques, with a 2%-10% increase in mIoU and a 3%-9% improvement in F1-score. On the MSRWD, SDNet attained an IoU of 94.21% for the water class, with an F1-score of 97.03% and an overall accuracy of 98.87%. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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Revisiting Near/remote sensing with Geospatial Attention

Revisiting Near/Remote Sensing with Geospatial Attention

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

作者： Workman, Scott Rafique, M. Usman Blanton, Hunter Jacobs, Nathan DZYNE Technol Fairfax VA 22031 USA Kitware Inc Clifton Pk NY USA Univ Kentucky Lexington KY 40506 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This work addresses the task of overhead image segmentation when auxiliary ground-level images are available. Recent work has shown that performing joint inference over these two modalities, often called near/remote sensing, can yield significant accuracy improvements. Extending this line of work, we introduce the concept of geospatial attention, a geometry-aware attention mechanism that explicitly considers the geospatial relationship between the pixels in a ground-level image and a geographic location. We propose an approach for computing geospatial attention that incorporates geometric features and the appearance of the overhead and ground-level imagery. We introduce a novel architecture for near/remote sensing that is based on geospatial attention and demonstrate its use for five segmentation tasks. The results demonstrate that our method significantly outperforms the previous state-of-the-art methods.

关键词： image segmentation Computer vision Social networking (online) Computational modeling Computer architecture Geospatial analysis Sensors

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image Augmentations in Planetary Science: Implications in Self-Supervised Learning and Weakly-Supervised Segmentation on Mars 26

Image Augmentations in Planetary Science: Implications in Se...

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

作者： Kossmann, Dominik Matei, Arthur Wilhelm, Thorsten Fink, Gernot A. TU Dortmund Univ Dept Comp Sci Dortmund Germany TU Dortmund Univ Dept Elect Engn & Informat Technol Dortmund Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Research on the use of augmentations in physically constrained remote sensing scenarios, like the analysis of Martian surface data, is largely unexplored. In this work we present an analysis on how reasonable augmentation strategies can be selected which are class agnostic and respect physical plausibility in supervised and weakly-supervised tasks. Additionally, we present the first results of self-supervised learning on Martian surface data, discuss the importance of physically plausible augmentations in the context of self-supervised learning, specifically contrastive learning, and provide a comprehensive overview of the generalization properties induced by different augmentation strategies with the help of geomorphic maps.

关键词： Mars image segmentation Self-supervised learning pattern recognition Task analysis remote sensing

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Self-Correcting Capsule Network on remote sensing Scene Classification

Self-Correcting Capsule Network on Remote Sensing Scene Clas...

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2023 Asia-Pacific Conference on image processing, Electronics and Computers, IPEC 2023

作者： Kan, Dongdonz Cao, Yue School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Cork Ireland

ISBN: (纸本)9781665457033

Most of the current capsule network methods have good classification effects mainly on simple content datasets such as MNIST and CIFAR10. However, for remote sensing scene images with complex objects, these methods cannot well resolve the main objects in the images, which in turn affects the recognition of scene categories. To address the above problems, this paper proposes a self-correcting capsule network with the ability to enhance the main object information contained in the features. First, the original features of the image are encapsulated into capsules. Then, the part extraction layer goes to learn the part information of the main targets in the scene and corrects the information of the primary capsule features, so as to parse the parts of different objects in the image. Since the traditional dynamic routing algorithms cannot be applied well in remote sensing scenes, in order to get the category information effectively, a class activation routing is proposed to act into the part capsules by learning the class activation weights in the original features, and using the routing to learn the spatial relationship between different parts to combine different part capsules to calculate the capsules rich in category information. The method in this paper effectively improves the classification performance of the network. © 2023 IEEE.

关键词： remote sensing

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Comprehensive Review on Lossy and Lossless Compression Techniques

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Journal of The Institution of Engineers (India): Series B 2022年第3期103卷 1003-1012页

作者： Elakkiya, S. Thivya, K.S. Department of Electronics and Communication Engineering Dr.M.G.R. Educational and Research Institute Tamil Nadu Chennai India

images are now employed as data in a variety of applications, including medical imaging, remote sensing, pattern recognition, and video processing. image compression is the process of minimizing the size of images by removing or grouping certain parts of an image file without affecting the quality, thereby saving storage space and bandwidth. image compression plays a vital role where there is a need for images to be stored, transmitted, or viewed quickly and efficiently. There are different techniques through which images can be compressed. This paper mainly focuses on the survey of basic compression techniques available and the performance metrics that are used to evaluate them. In addition to this, it also provides a review of important pieces of the literature relating to advancements in the fundamental lossy and lossless compression algorithms. © 2021, The Institution of Engineers (India).

关键词： image compression

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A Transfer Learning Approach for Deformation pattern recognition in InSAR Time Series

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Yang, Mengshi Li, Saiwei Yu, Hang Yunnan Univ Sch Earth Sci Kunming 650500 Peoples R China Yunnan Univ Inst Int Rivers & Ecosecur Kunming 650500 Peoples R China

The multiepoch interferometry synthetic aperture radar (InSAR) technique is a widely applied geodetic tool for measuring surface displacement. Yet, traditional interpretations of InSAR results have primarily centered on linear displacement velocities, often neglecting the rich insights that InSAR displacement time-series data can offer. This study innovatively addresses this gap by proposing a deep learning (DL)-based method for interpreting InSAR deformation time series in urban environments. We first introduce six canonical deformation patterns: stable, linear, stepwise, piecewise linear, power-law, and undefined. A novel postprocessing approach integrates DL models-bidirectional long short-term memory (BiLSTM), temporal convolutional network (TCN), and Transformer-with transfer learning techniques. The strategy involves pretraining models on simulated data and fine-tuning with real-world data, significantly reducing dependence on extensive labeled datasets. This study demonstrates the effectiveness of these DL models in processing InSAR deformation sequences, illustrating how transfer learning can tackle the challenge of limited labeled InSAR datasets. The experimental results reveal that the TCN model achieves the best performance, with an accuracy of 91%. Tested on InSAR data from Kunming City, the proposed approach effectively classified deformation sequences into predefined categories. The findings demonstrate that time-series analysis reveals more detailed deformation insights-particularly in regions with low deformation rates-than traditional velocity-based methods. Furthermore, incorporating transfer learning significantly reduces the dependency on extensive real-world datasets, enhancing overall model performance and facilitating future advancements in the field.

关键词： Deformation Time series analysis Deformable models Mathematical models Urban areas Data models Transformers Market research Logic gates Deep learning (DL) deformation pattern interferometry synthetic aperture radar (InSAR) displacement time series transfer learning DL transfer learning DL

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