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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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Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for remote sensing

arXiv

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arXiv 2025年

作者： Zi, Xing Jin, Kairui Tao, Xian Li, Jun Braytee, Ali Shah, Rajiv Ratn Prasad, Mukesh University of Technology Sydney Australia Chinese Academy of Sciences China Indraprastha Institute of Information Technology Delhi India

Pixel-level segmentation is essential in remote sensing, where foundational vision models like CLIP and Segment Anything Model(SAM) have demonstrated significant capabilities in zero-shot segmentation tasks. Despite their advances, challenges specific to remote sensing remain substantial. Firstly, The SAM without clear prompt constraints, often generates redundant masks, and making post-processing more complex. Secondly, the CLIP model, mainly designed for global feature alignment in foundational models, often overlooks local objects crucial to remote sensing. This oversight leads to inaccurate recognition or misplaced focus in multi-target remote sensing imagery. Thirdly, both models have not been pre-trained on multi-scale aerial views, increasing the likelihood of detection failures. To tackle these challenges, we introduce the innovative VTPSeg pipeline, utilizing the strengths of Grounding DINO, CLIP, and SAM for enhanced open-vocabulary image segmentation. The Grounding DINO+(GD+) module generates initial candidate bounding boxes, while the CLIP Filter++(CLIP++) module uses a combination of visual and textual prompts to refine and filter out irrelevant object bounding boxes, ensuring that only pertinent objects are considered. Subsequently, these refined bounding boxes serve as specific prompts for the FastSAM model, which executes precise segmentation. Our VTPSeg is validated by experimental and ablation study results on five popular remote sensing image segmentation datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： image segmentation

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image processing based data reduction technique in WVSN for smart agriculture

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COMPUTING 2023年第12期105卷 2675-2698页

作者： Koteich, Jana Salim, Christian Mitton, Nathalie Inria Villeneuve Dascq France Junia Comp Sci & Math F-59000 Lille France

Nowadays, to improve animal well being in livestock farming application, a wire-less video sensor network (WVSN) can be deployed to early detect injury and moni-tor animals. They are composed of small embedded video and camera motes that capture video frames periodically and send them to a specific node called a sink. Sending all the captured images to the sink consumes a lot of energy on every sensor and may cause a bottleneck at the sink level. Energy consumption and bandwidth limitation are two important challenges in WVSNs because of the limited energy resources of the nodes and the medium scarcity. In this work, we introduce two mechanisms to reduce the overall number of frames sensed and sent to the sink. The first approach is applied on each sensor node, where the FRABID algorithm, a joint data reduction, and frame rate adaptation on sensing and transmission phases mechanism is introduced. This approach reduces the number of sensed frames based on a similarity method. The aim is to adapt the number of sensed frames based on the degree of difference between two consecutive sensed frames in each period. This adaptation technique maintains the accuracy of the video while capturing frames holding new information. This approach is validated through simulations using real data-sets from video sensors (Wang et al. in: 2014 IEEE conference on computer vision and pattern recognition workshops, pp 393-400, 2014). The results show that the amount of sensed data is reduced by more than 70% compared to a recent algorithm in Christian et al. (Multimed Tools Appl 79(3):1801-1819, 2020) while guaranteeing the detection of all the critical events at the sensor node level. The sec-ond approach exploits the Spatio-temporal correlation between neighboring nodes to reduce the number of captured frames. For that purpose, Synchronization with Frame Rate Adaptation SFRA algorithm is introduced where overlapping nodes capture frames in a synchronized fashion every N - 1 periods, wher

关键词： Smart agriculture Spatio-temporal correlation Data reduction Data prediction WSN

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Design of Cloud Platform for Efficient remote sensing Big Data processing Based on Open Stack

Design of Cloud Platform for Efficient Remote Sensing Big Da...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Yan Huang Weimeng Zhang Xi’an Traffic Engineering Institute Xi’an Shaanxi China Xi’an Technical School Xi’an Shaanxi China

The rapid preparation of computing resources is an important step to realize efficient remote sensing image processing. It takes a lot of time for researchers to prepare hardware resources and deploy computing framework. It often mistakes in the preparation and careless deployment of the framework which can lead to high research costs. To solve this problem, this paper uses Open Stack to virtualize hardware resources and uses Linux Shell scripting language to quickly prepare and deploy the Spark parallel computing environment. In this paper, based on the remote sensing image change detection method of connection condition, for example, the data processing algorithm in advance import platform algorithms library, component library, used to support the remote sensing image change detection of business flow of creation and recognition, resources rapid preparation, execution and presentation of the results of the algorithm and implement of massive remote sensing image change detection and efficient. It effectively improves the efficiency of remote sensing data management.

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Toward On-Board Panoptic Segmentation of Multispectral Satellite images

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2023年 61卷 1页

作者： Fernando, Tharindu Fookes, Clinton Gammulle, Harshala Denman, Simon Sridharan, Sridha Queensland Univ Technol Signal Proc Artificial Intelligence & VisionTechno Brisbane Qld 4000 Australia

With tremendous advancements in low-power embedded computing devices and remote sensing instruments, the traditional satellite image processing pipeline which includes an expensive data transfer step prior to processing data on the ground is being replaced by on- board processing of captured data. This paradigm shift enables critical and time-sensitive intelligence to be acquired in a timely manner on- board the satellite itself. However, at present, the on- board processing of multispectral satellite images is limited to classification and segmentation tasks. Extending this processing to the next logical level, we take the first step toward on- board panoptic segmentation of multispectral satellite images and evaluate the applicability of state-of-the-art panoptic segmentation models to an on- board setting. Panoptic segmentation offers major economic and environmental insights, ranging from yield estimation from agricultural lands to intelligence for complex military applications. Nevertheless, the on- board intelligence extraction poses several challenges due to the loss of temporal observations and the need to generate predictions from a single sample. To address this challenge, we propose a multimodal teacher network with a cross modality attention-based fusion strategy to improve segmentation accuracy by exploiting data from multiple modes. We also propose an online knowledge distillation framework to transfer the knowledge learned by this multimodal teacher network to a unimodal student, which receives only a single frame input, and is more appropriate for an on- board environment. We benchmark our approach against existing state-of-the-art panoptic segmentation models using the PASTIS multispectral panoptic segmentation dataset considering an on- board processing setting. Our evaluations demonstrate a substantial 10.7%, 11.9%, and 10.6% increase in segmentation quality (SQ), recognition quality (RQ), and panoptic quality (PQ) metrics compared to the existing

关键词： image segmentation Satellites Computer architecture Benchmark testing Pipelines Knowledge engineering Task analysis Knowledge distillation multimodality fusion multispectral image processing on-board satellite image processing panoptic segmentation

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ANALYSIS: IMPACT OF image MATCHING METHODS ON JITTER COMPENSATION 24

ANALYSIS: IMPACT OF IMAGE MATCHING METHODS ON JITTER COMPENS...

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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Ye, G. Pan, J. Wang, M. Zhu, Y. Jin, S. Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China

The degradation of image quality caused by satellite jitter has drawn attention, and many researches have illustrated the importance of the jitter compensation. As the essential component of jitter compensation, image matching involves in the determination of jitter processing accuracy. Hence, the impact of imaging matching methods on jitter compensation is explored in this paper. At first, a framework based on imaging matching is built for jitter compensation. Two typical sub-pixel accuracy matching methods (i.e. correlation coefficient and least squares matching, as well as phase correlation matching) could then be served for the framework. The experiments are designed by using multispectral images of GF-1 satellite, and quantitative evaluations show that compared with correlation coefficient and least squares matching, phase matching method makes the accuracy of obtained jitter curve increase with the amplitude error decreasing by more than 0.012 pixel as well as phase error decreasing by more than 0.0119 rad, and makes the quality of restored images improved on both geometry and radiation. It indicates that phase matching method has better performance with respect to the framework of jitter compensation in this paper.

关键词： Satellite jitter Jitter compensation Multispectral images image matching GF-1 satellite

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An Intelligent Registration Method of Heterogeneous remote sensing images Based on Style Transfer 22

An Intelligent Registration Method of Heterogeneous Remote S...

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Proceedings of the 2022 5th International Conference on Artificial Intelligence and pattern recognition

作者： Haoyang Tang Xin Miao Jiakun Shi Zhifan Hua Dongfang Yang College of Automation Xi 'an University of Posts and Telecommunications China The graduate school Rocket Force Engineering University China

ISBN: (纸本)9781450396899

Intelligent registration of heterogeneous remote sensing images is a hot issue in the field of remote sensing and has important research and application values. Due to the significant differences in data sources, image texture, color space and other factors of heterogeneous remote sensing images, the traditional image registration algorithms are difficult to be directly applied to the intelligent registration of images from different sources. In this paper, a new method of intelligent registration of heterogenous remote sensing images based on style transfer is proposed. First, the content features of the baseline image and the style features of the image to be aligned are extracted by using the generative adversarial nets, and the remote sensing images from different sources are fused in terms of content and style to obtain the remote sensing image transfer results with the same style as the image to be aligned and keeping the content of the baseline image unchanged. Then, an intelligent image registration algorithm is used to match the migrated remote sensing images. Finally, a heterogenous remote sensing image dataset is constructed around the heterogenous remote sensing image registration task, and the intelligent registration method of this paper is experimentally validated based on this dataset. The experimental results show that the intelligent image registration method based on style transfer can effectively improve the accuracy of heterogenous remote sensing image registration compared with the direct image registration method.

关键词： Heterogenous remote sensing image data set Homography transformation N-point method Style transfer image registration

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Partial discharge pattern recognition method based on Transfer Learning and DenseNet model

Partial discharge pattern recognition method based on Transf...

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2022 IEEE International Conference on High Voltage Engineering and Applications, ICHVE 2022

作者： Fu, Yuwei Liang, Liejuan Zhang, Guowei Zhang, Zhiyu Chen, Chi Wang, Chuang Xi'an University of Technology Xi'an China Xi'an Xd High Voltage Apparatus Co. Ltd Xi'an710018 China

ISBN: (数字)9781665407502

ISBN: (纸本)9781665407502

With the development of sensing technology, a large number of partial discharge (PD) time domain data are generated in the field of gas-insulated integrated electrical appliances (GIS). Traditional pattern recognition methods cannot identify the defect types of such data. At the same time, deep learning methods generally face the problem of small samples. In order to solve the above problems, this paper proposes a PD pattern recognition method based on transfer learning and DenseNet model. Firstly, the finite-difference-time-domain(FDTD) method is used to simulate PD in GIS, and the time domain waveform image dataset of four PD defects was established. Secondly, image processing techniques such as image enhancement, normalization and image compression are used to process the time-domain waveform spectrum. Finally, Using CNN network and transfer learning, the recognition accuracy of the model is improved to 96.38%, and the convergence is obviously improved. The identification performance of different CNN network structures is studied, and the results show that the accuracy of DenseNet model is significantly higher than other structures. This study can be used to diagnose PD defects in GIS equipment, © 2022 IEEE.

关键词： Partial discharges

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Improved Identification of Forest Types in the Loess Plateau Using Multi-Source remote sensing Data, Transfer Learning, and Neural Residual Networks

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remote sensing 2024年第12期16卷 2096页

作者： Zhang, Mei Yin, Daihao Li, Zhen Zhao, Zhong Northwest A&F Univ Key Comprehens Lab Forestry Xianyang 712100 Peoples R China Northwest A&F Univ Coll Forestry Key Lab Silviculture Loess Plateau State Forestry Xianyang 712100 Peoples R China

This study aims to establish a deep learning-based classification framework to efficiently and rapidly distinguish between coniferous and broadleaf forests across the Loess Plateau. By integrating the deep residual neural network (ResNet) architecture with transfer learning techniques and multispectral data from unmanned aerial vehicles (UAVs) and Landsat remote sensing data, the effectiveness of the framework was validated through well-designed experiments. The study began by selecting optimal spectral band combinations, using the random forest algorithm. Pre-trained models were then constructed, and model performance was optimized with different training strategies, considering factors such as image size, sample quantity, and model depth. The results indicated substantial improvements in the model's classification accuracy and efficiency for reasonable image dimensions and sample sizes, especially for an image size of 3 x 3 pixels and 2000 samples. In addition, the application of transfer learning and model fine-tuning strategies greatly enhanced the adaptability and universality of the model in different classification scenarios. The fine-tuned model achieved remarkable performance improvements in forest-type classification tasks, increasing classification accuracy from 85% to 93% in Zhengning, from 89% to 96% in Yongshou, and from 86% to 94% in Baishui, as well as exceeding 90% in all counties. These results not only confirm the effectiveness of the proposed framework, but also emphasize the roles of image size, sample quantity, and model depth in improving the generalization ability and classification accuracy of the model. In conclusion, this research has developed a technological framework for effective forest landscape recognition, using a combination of multispectral data from UAVs and Landsat satellites. This combination proved to be more effective in identifying forest types than was using Landsat data alone, demonstrating the enhanced capability and accu

关键词： deep learning transfer learning forest classification multi-source data residual neural network (ResNet)

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Disaster Site Structure Analysis: Examining Effective remote sensing Techniques in Blue Tarpaulin Inspection

Disaster Site Structure Analysis: Examining Effective Remote...

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作者： Miles, Madeline Grace Jacksonville State University

学位级别：M.G.I.S., Master of Geographic Information Science

This thesis aimed to evaluate three methods of analyzing blue roofing tarpaulin (tarp) placed on homes in post natural disaster zones with remote sensing techniques by assessing the different methods- image segmentation, machine learning (ML), and supervised classification. One can determine which is the most efficient and accurate way of detecting blue tarps. The concept here was that using the most efficient and accurate way to locate blue tarps can aid federal, state, and local emergency management (EM) operations and homeowners. In the wake of a natural disaster such as a tornado, hurricane, thunderstorm, or similar weather events, roofs are the most likely to be damaged (Esri Events., 2019). Severe roof damage needs to be mitigated as fast as possible: which in the United States is often done at no cost by the Federal Emergency Management Agency (FEMA). This research aimed to find the most efficient and accurate way of detecting blue tarps with three different remote sensing practices. The first method, image segmentation, separates parts of a whole image into smaller areas or categories that correspond to distinct items or parts of objects. Each pixel in a remotely sensed image is then classified into categories set by the user. A successful segmentation will result when pixels in the same category have comparable multivariate, grayscale values and form a linked area, whereas nearby pixels in other categories have distinct values. Machine Learning, ML, a second method, is a technique that processes data depending on many layers for feature identification and pattern recognition. ArcGIS Pro mapping software processes data with ML classification methods to classify remote sensing imagery. Deep learning models may be used to recognize objects, classify images, and in this example, classify pixels. The resultant model definition file or deep learning software package is used to run the inference geoprocessing tools to extract particular item positions, categorize

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