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Study on Modeling Method of Forest Tree image recognition Based on CCD and Theodolite

IEEE ACCESS 2020年 8卷 159067-159076页

作者： Shi, Yeqiong Wang, Sainan Zhou, Shuna Kamruzzaman, M. M. Northeast Forestry Univ Harbin 150040 Peoples R China Harbin Normal Univ Harbin 150025 Peoples R China Shenyang Inst Sci & Technol Dept Informat & Control Engn Shenyang 110100 Peoples R China Inner Mongolia Univ Manzhouli Coll Manzhouli 021400 Peoples R China Jouf Univ Dept Comp & Informat Sci Sakaka 72311 Saudi Arabia

Forest vegetation is the main body that constitutes forest resources. Accurate identification of the types of forest trees can lay the foundation for the research and utilization of forest resources. With the development of remote sensing technology, traditional optical remote sensing can only describe the horizontal pattern of ground features, which makes it difficult to identify single tree species. Therefore, it is of great significance to study the method of forest tree image recognition. This article mainly studies the forest image recognition system based on CCD and theodolite. In this article, the forest image recognition system based on CCD and theodolite uses near, middle, and far CCD cameras to detect the infrared radiation of the target and collect the target image. The image processing algorithm is designed for the image processing module, and the flow chart of the image processing algorithm is given. The processing function has designed the interface of the image processing module. The image processing module can extract the main information of the target from simple background and complex background. In this article, an experimental optical path is built, the forest image recognition simulation platform is verified, and the data obtained from the experiment is processed. The implemented color detection algorithm can achieve a detection accuracy rate of more than 91% for forest tree image recognition detection. The test results show that the image acquisition, transmission and display functions of the camera system realized by this subject are normal, and the system can achieve accurate recognition of the target.

关键词： CCD forest tree image image preprocessing image processing system image recognition

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A real-time SVM-based hardware accelerator for hyperspectral images classification in FPGA

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MICROPROCESSORS AND MICROSYSTEMS 2024年 104卷

作者： Martins, Lucas Amilton Viel, Felipe Seman, Laio Oriel Bezerra, Eduardo Augusto Zeferino, Cesar Albenes Univ Vale Itajai UNIVALI Polytech Sch Itajai Brazil Fed Univ Santa Catarina UFSC Dept Elect Engn Florianopolis Brazil

Hyperspectral imaging can be conceptualized as a three-dimensional dataset of spectral information related to a particular landscape. Generally speaking, these are aerial photographs captured by Earth observation satellites. A useful analogy for a hyperspectral image is one of a cube formed with the image acquired along the X and Y axes and a third dimension of spectral bands of varying wavelengths. Given the wealth of data contained within these images, they have been employed in both civilian and military applications such as terrain recognition, urban development supervision, recognition of rare minerals, and various other objectives. The increased utilization of these images has garnered the interest of researchers striving to create solutions that may enable faster processing of the images via parallel processing. In this context, FPGA technology is an option capable of facilitating the implementation of such a system for observation satellites. This research is situated within this framework and aims to develop an FPGA-synthesized hardware accelerator to facilitate real -time hyperspectral image categorization. By taking this approach, hardware-specific solutions can be implemented for embedded applications that process hyperspectral images and can also be integrated with further image processing steps. The proposed accelerator was constructed based on an advanced algorithmic model, resulting in outcomes consistent with those generated by the software -based solution. The experimental results demonstrate that the engineered accelerator can attain a pixel classification time equal to or less than the pixel acquisition time, thus conforming to the real -time processing criteria concerning classification time. Further, the manufactured accelerator exhibits scalability that can classify distinct datasets with varying classes concurrently while maintaining a uniform logic resource utilization.

关键词： remote sensing Hyperspectral imaging Machine learning Classification Hardware acceleration FPGA

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Edge feature enhancement approach using hilbert transform of Cauchy distribution and its applications

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IET image processing 2021年第12期15卷 2891-2909页

作者： Wang, Ke Rohde, Gustavo K. Xiao, Jian Hohai Univ Dept Geog Informat Sci Nanjing 210093 Jiangsu Peoples R China Univ Virginia Dept Biomed Engn Charlottesville VA USA Hohai Univ Res Inst Appl Comp Technol Nanjing Jiangsu Peoples R China

Building on past work showing that the Hilbert transform can be used for edge feature enhancement, a new edge feature enhancement method is developed using a two-dimensional (2D) isotropic Hilbert transform of the Cauchy distribution. First, both the shape of the Hilbert kernel and the Hilbert transform of edge feature models (step and delta) and various simulated signals result in edge feature enhancement, the properties of which are derived and confirmed. Second, Cauchy distribution as low-pass filter is introduced in Hilbert transform to get new edge feature enhancement operator, and its efficiency under various criteria is comprehensively discussed. Third, a 2D isotropic extension is presented using a circularly symmetric window function. Finally, two experiments, including edge detection and image segmentation, are performed to validate the proposed edge feature enhancement method. The experimental results of the method applied to the Berkeley Segmentation Dataset and remote sensing images demonstrate that the new method is effective for edge detection and image segmentation.

关键词： Optical, image and video signal processing image recognition Computer vision and image processing techniques

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AN IQE CRITERION-BASED METHOD FOR SAR imageS CLASSIFICATION NETWORK PRUNING

AN IQE CRITERION-BASED METHOD FOR SAR IMAGES CLASSIFICATION ...

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IEEE International Geoscience and remote sensing Symposium (IGARSS)

作者： Wang, Jielei Cui, Zongyong Cao, Zongjie Wang, Hanzeng Cao, Changjie Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781665403696

Deep convolutional neural networks (DCNNs) have been widely used for SAR image target recognition. However, the huge demands of DCNNs for computing, storage, and energy resources limit their use on edge computing devices. In this article, we propose a method based on image quality evaluation (IQE) criterion to prune deep neural networks. We use IQE criterion to identify unimportant filters, and then remove them, to obtain a lightweight network while maintaining the performance of the neural network as much as possible. Besides, we verified the effectiveness of our method on the MSTAR dataset with cheap edge computing devices.

关键词： Deep convolutional neural network SAR-ATR Network pruning DCNN acceleration Knowledge processing Unit (KPU)

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Direction-Guided Multiscale Feature Fusion Network for Geo-Localization

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2024年 62卷 1页

作者： Lv, Hongxiang Zhu, Hai Zhu, Runzhe Wu, Fei Wang, Chunyuan Cai, Meiyu Zhang, Kaiyu Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201602 Peoples R China Zhejiang Univ Jiaxing Res Inst Intelligent Opt & Photon Res Ctr Jiaxing 314000 Peoples R China

Cross-view geo-localization has been widely used as an important technique for determining the geographical location of unmanned aerial vehicles (UAVs). Despite various image retrieval methods proposed, drone and satellite image cross-view geo-localization still remain challenging due to their wildly inconsistent view angles. In this article, we propose a new framework, the Swin-radial-locality network (SRLN), to extract robust image feature representations. Specifically, SRLN is based on a pruned version of the Swin transformer, which integrates multiscale feature aggregation within a Siamese network structure, featuring shared weights and equipped with multiclassification heads. SRLN is mainly comprised of a radial-slicer-network (RSN) and a local-pattern-network (LPN), which is designed to effectively harmonize directional information from drone-captured images and broader environmental features from satellite imagery, crucial for capturing angle and feature details between drone and satellite images. The RSN part focuses on capturing fine-grained features that represent the drone's directional information, while the LPN is utilized for a more comprehensive analysis of broader environmental features. Extensive experiments are carried out on widely used public benchmark datasets, i.e., University-1652 and SUES-200. With more than 3% improvement over existing methods in both drone-view target localization tasks and drone navigation applications, the results validate the superior performance of our multiscale feature fusion model, achieving a state-of-the-art performance record.

关键词： Transformers Feature extraction Autonomous aerial vehicles Location awareness Drones Visualization Task analysis Cross-view geo-localization local pattern network multiscale image processing radial slicer network (RSN) Swin transformer unmanned aerial vehicle (UAV) image localization

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Research on Rotated Object Detection Based on YOLOv5s

Research on Rotated Object Detection Based on YOLOv5s

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International Congress on image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Jie Xu Yanyun Cheng School of Automation and Artificial Intelligence Nanjing University of Posts and Tel-ecommunications Jiangsu Nanjing China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

To address the issues of low detection accuracy and high miss rates in remote sensing object detection, this study proposes an improved YOLOv5s_CSL method integrated with attention mechanisms. Firstly, by employing phase-shift encoding for angle precision, this method uses cosine functions of varying phases and frequencies to replace traditional CSL discontinuous encoding, thus enabling automatic derivation and continuous optimization of angles, and effectively resolving the class square problem inherent in CSL encoding. Secondly, an innovative dynamic receptive field feature fusion attention mechanism is introduced, which enhances the model's response to targets of varying scales by automatically adjusting the receptive field, while also facilitating effective cross-space information transfer, significantly improving detection capabilities in complex environments. Lastly, a new angle regression loss function was designed, combining mean squared error loss with the geometric characteristics of the bounding boxes, significantly enhancing the flexibility of angle detection for objects of various shapes and sizes, and effectively reducing the miss rate for objects with large aspect ratios. Experimental results demonstrate that this algorithm performed exceptionally well on the DOTA-v1.0 test set, achieving a detection accuracy of 77.47%, a significant improvement over the YOLOv5s_CSL method. These innovations not only enhance the accuracy of remote sensing object detection but also provide new effective tools for image recognition in complex scenarios, offering high application value and prospects for widespread use.

关键词： YOLO Training Adaptation models Accuracy Attention mechanisms Computational modeling Signal processing algorithms Feature extraction Encoding remote sensing

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Intelligent road extraction from high resolution remote sensing images based on optimized SVM

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JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2024年第4期17卷

作者： Yang, Yuntao Wu, Qichen Yu, Ruipeng Wang, Li Zhao, Yize Ding, Cui Yin, Yunpeng Shandong Prov Bur Geol & Mineral Resources 1 Geol Team Jinan 250010 Shandong Peoples R China Shandong Prov Bur Geol & Mineral & Mineral Resourc Key Lab Cableways Intelligent Deformat Monitoring Jinan 250000 Peoples R China Shandong Urban Construct Vocat Coll Jinan 250000 Peoples R China Management Comm Zaozhuang Natl Hitech Ind Dev Zone Zaozhuang 277300 Peoples R China

Road recognition and extraction based on high-resolution remote sensing images is currently a hot issue at the forefront of image processing and other disciplines, and its results not only help to enrich geographic information, but also have important application value in national defence construction and other aspects. For the intelligent extraction of roads in high-resolution remote sensing images, the study firstly preprocesses these images, and then extracts the roads by using variational function and support vector machine. As the extraction results are not satisfactory enough, the 3D wavelet transform technique is used to extract the spectral features of roads in the frequency domain, combined with the texture in the time domain, and optimized the support vector machine in order to extract the road images more accurately. The outcomes denote that in contrast with other methods, the optimized method can have optimal correctness and quality while guaranteeing higher integrity. The satellite road image of Chengdu A industrial park is rich in extracted information, having the highest completeness of 94.23%, correctness of 60.73%, and quality of 59.42%, and the satellite road image of Chengdu B industrial park is similarly rich in extracted information, having the highest completeness of 83.47%, correctness of 85.31%, and quality of 72.85%. The highest value of CPU usage for road extraction was 2000 kb at 37 s, and the average value of CPU usage for the whole 60 s was 500 kb. It shows that the research method has good results in high resolution remote sensing image road extraction.

关键词： High-resolution remote sensing image Road Extraction Variational function SVM Time domain Frequency domain

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Rotation Invariant Aerial image Retrieval with Group Convolutional Metric Learning 25

Rotation Invariant Aerial Image Retrieval with Group Convolu...

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25th International Conference on pattern recognition (ICPR)

作者： Chung, Hyunseung Nam, Woo-Jeoung Lee, Seong-Whan Korea Univ Dept Artificial Intelligence Seoul South Korea Korea Univ Dept Comp & Radio Commun Engn Seoul South Korea

ISBN: (纸本)9781728188089

remote sensing image retrieval (RSIR) is the process of ranking database images depending on the degree of similarity compared to the query image. As the complexity of RSIR increases due to the diversity in shooting range, angle, and location of remote sensors, there is an increasing demand for methods to address these issues and improve retrieval performance. In this work, we introduce a novel method for retrieving aerial images by merging group convolution with attention mechanism and metric learning, resulting in robustness to rotational variations. For refinement and emphasis on important features, we applied channel attention in each group convolution stage. By utilizing the characteristics of group convolution and channel-wise attention, it is possible to acknowledge the equality among rotated but identically located images. The training procedure has two main steps: (i) training the network with Aerial image Dataset (AID) for classification, (ii) fine-tuning the network with triplet-loss for retrieval with Google Earth South Korea and NWPU-RESISC45 datasets. Results show that the proposed method performance exceeds other state-of-the-art retrieval methods in both rotated and original environments. Furthermore, we utilize class activation maps (CAM) to visualize the distinct difference of main features between our method and baseline, resulting in better adaptability in rotated environments.

关键词： Convolution

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Ultrawideband ISAR Imaging of Maneuvering Targets With Joint High-Order Motion Compensation and Azimuth Scaling

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2022年 60卷

作者： Shao, Shuai Liu, Hongwei Zhang, Lei Wang, Penghui Wei, Jiaqi Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Guangzhou 510275 Peoples R China Northwest Univ Sch Informat Sci & Technol Xian 710069 Peoples R China

Ultrawideband (UWB) radar can achieve ultrahigh-resolution inverse synthetic aperture radar (ISAR) imaging of noncooperative targets by transmitting UWB signals. However, the spatial-variant (SV) high-order migration through range cell (MTRC) and phase errors produced by the UWB radar system have seriously challenged the feasibility of conventional ISAR imaging algorithms. Moreover, maneuvering targets has exacerbated this problem compared with the steady ones. In this article, a UWB ISAR imaging algorithm of maneuvering targets with joint high-order motion compensation and azimuth scaling (JHOMCAS) is proposed. For the azimuth SV linear MTRC and 2-D SV high-order MTRC caused by the maneuvering rotational motion of the targets, the cascaded generalized keystone transform (GKT) is adopted for precise correction. It is worth noting that, when eliminating the SV MTRC by the cascaded GKT, the 2-D SV high-order phase errors induced by the maneuvering rotational motion must be accurately compensated, or MTRC correction will fail. The traditional autofocus methods usually only address the phase errors shared by the total target without due attention to the fine SV property. In response to this problem, this article first develops a joint 2-D SV autofocus and azimuth scaling algorithm (JSVAAS) to achieve the integration of SV high-order phase error compensation and azimuth scaling. A JHOMCAS algorithm is proposed to perform the joint processing of GKT and JSVAAS, ``GKT-JSVAAS-GKT.'' This approach helps accomplish the high-precision UWB ISAR imaging of maneuvering targets, and the well-focused and scaled UWB ISAR images obtained will build a sound foundation for target classification and recognition. Extensive experiments based on both scattering point simulation data and electromagnetic calculation data verify that the proposed algorithm outperforms conventional ISAR imaging approaches in UWB ISAR imaging of maneuvering targets.

关键词： Imaging Azimuth Radar imaging Radar Signal processing algorithms image resolution Ultra wideband radar 2-D spatial-variant (SV) high-order motion compensation azimuth scaling inverse synthetic aperture radar (ISAR) maneuvering targets ultrawideband (UWB) radar

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Research on remote sensing image Target recognition Based on Deep Convolution Neural Network

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INTERNATIONAL JOURNAL OF pattern recognition AND ARTIFICIAL INTELLIGENCE 2020年第5期34卷

作者： Han, Xiaofeng Jiang, Tao Zhao, Zifei Lei, Zhongteng Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Geomat Qingdao 266590 Peoples R China

Target recognition is an important application in the time of high-resolution remote sensing images. However, the traditional target recognition method has the characteristics of artificial design, and the generalization ability is not strong, which makes it difficult to meet the requirement of the current mass data. Therefore, it is urgent to explore new methods for feature extraction and target recognition and location in remote sensing images. Convolutional neural network in deep learning can extract representative and discriminative multi-level features of typical features from images, so it can be used for multi-target recognition of remote sensing big data in complex scenes. In this study, NWPU VHR-10 data was selected, 50% was used for training, and the remainder was used for verification. The target recognition effects of two kinds of convolutional neural network models, Faster R-CNN and SSD, were studied and compared, and the mean average precision (mAP) was used for evaluation. The evaluation results show that the Faster R-CNN has three categories with an accuracy of more than 80%, and the SSD has seven categories with an accuracy of more than 80%, all of which show good results. The SSD model is particularly prominent in running time and recognition results, which proves convolutional neural networks have broad application prospects in the target recognition of remote sensing image data.

关键词： remote sensing images target recognition deep convolution neural network Faster R-CNN SSD

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