检索结果-内蒙古大学图书馆

Application of remote sensing image processing based on artificial intelligence in landscape pattern analysis

ENVIRONMENTAL EARTH SCIENCES 2024年第23期83卷 1-14页

作者： Zhang, Qi Hubei Polytech Univ Acad Arts Huangshi 435000 Hubei Peoples R China

The spatial arrangement of various land cover types within a landscape is referred to as the Landscape pattern (LSP). An essential component of landscape ecology, LSP examination is significant for a variety of causes, like species conservation, sustainable development, environmental monitoring, landscape planning, and management of accepted resources. The development of remote sensing (RS) images permits urban planners to additional systematically and economically. 0 identify the land use of a specified area on a slighter time scale. The objective of this study is to build up an artificial intelligence (AI)-based RS image processing performance for LSP. This study, proposed a novel refined flamingo search-dynamic recurrent neural network (RFS-DRNN) to analyze the LSP. RS image data were gathered from landscape characteristics. The Discrete Wavelet Transform (DWT) utilizes pre-processed data to eliminate noise, though maintenance is important distinctiveness. Convolutional Neural Network (CNN) using extracted features from image data. RFS could be used to progress the constraint of a DRNN model that is used to analyze patterns in the landscape. It can be used to regulate an RNN's hyper parameters to enhance its ability to recognize and categorize landscape features. The results showed that the proposed method is effective at analyzing LSPs. The significance indicates that the proposed method has achieved superior performance in including accuracy [98.90%], precision [94.82%], recall [93.75%], and F1-score [95.29%]. The hierarchical land-cover mapping reveal process creates thorough LSP analysis possible by using satellite images and sophisticated algorithms. High training accuracy and decreasing training loss indicate effective model learning and generalization for landscape analysis. The execution times at the end highlight the important it is to maximize processing methods and computational capacity to build quick decisions while analyzing LSP.

关键词： Landscape pattern (LSP) analysis remote sensing (RS) image processing Refined flamingo search-dynamic recurrent neural network (RFS-DRNN)

来源：评论

学校读者我要写书评

暂无评论

Few-shot RF fingerprinting recognition for secure satellite remote sensing and image processing

引用

Science China(Information Sciences) 2023年第8期66卷 277-278页

作者： Di LIN Su HU Weiwei WU Gang WU National Key Laboratory of Science and Technology on Communication University of Electronic Science and Technology of China

We consider the security issues in a satellite remote sensing(SRS) and image processing system. Specifically, we present a novel architecture of a secure SRS image recognition system by using a radio frequency(RF) fingerprint to identify whether a satellite user is authenticated or *** unauthenticated users, we address the method of adding perturbation into their received SRS images,

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on spatial pattern recognition of landscape architecture based on multi-source remote sensing images

引用

INTERNATIONAL JOURNAL OF ENVIRONMENTAL TECHNOLOGY AND MANAGEMENT 2024年第3期27卷 216-231页

作者： Wang, Shujing Jilin Univ Architecture & Technol Sch Art & Design Changchun 130114 Peoples R China

In order to overcome the low recall rate, peak signal-to-noise ratio and correct recognition rate of spatial pattern in traditional spatial pattern recognition methods, a landscape spatial pattern recognition method based on multi-source remote sensing images was proposed. First, we obtain multi-source remote sensing images of landscape architecture, use ORB algorithm to extract multi-source remote sensing image feature points, and fuse multi-source remote sensing images. Then, MSRCR algorithm is used to enhance the fused image, LOG edge detection operator is used to obtain the image edge, and MCR model is used to determine the landscape patch characteristics. Finally, the spatial pattern recognition model of landscape architecture is built, and the spatial pattern recognition results are obtained. The experimental results show that the maximum recall rate of this method is 97%, the maximum peak signal to noise ratio of image is 59.3 dB, and the correct recognition rate varies from 97% to 99%.

关键词： multi-source remote sensing image landscape architecture spatial pattern recognition MSRCR algorithm LOG edge detection calculation MCR model

来源：评论

学校读者我要写书评

暂无评论

Learning transferable land cover semantics for open vocabulary interactions with remote sensing images

引用

ISPRS JOURNAL OF PHOTOGRAMMETRY AND remote sensing 2025年 220卷 621-636页

作者： Zermatten, Valerie Castillo-Navarro, Javiera Marcos, Diego Tuia, Devis Ecole Polytech Fed Lausanne Environm Computat Sci & Earth Observat Lab CH-1951 Sion Switzerland CEDRIC Conservatoire Natl Arts & Metiers F-75141 Paris France Univ Montpellier INRIA F-34090 Montpellier France

Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datasets. Open vocabulary recognition, i.e. using natural language to define a specific object or pattern in an image, breaks free from predefined nomenclature and offers flexible recognition of diverse categories with amore general image understanding across datasets and labels. The open vocabulary framework opens doors to search for concepts of interest, beyond individual class boundaries. In this work, we propose to use Text As supervision for COntrastive Semantic Segmentation (TACOSS), and we design an open vocabulary semantic segmentation model that extends its capacities beyond that of a traditional model for land cover mapping: In addition to visual pattern recognition, TACOSS leverages the common sense knowledge captured by language models and is capable of interpreting the image at the pixel level, attributing semantics to each pixel and removing the constraints of a fixed set of land cover labels. By learning to match visual representations with text embeddings, TACOSS can transition smoothly from one set of labels to another and enables the interaction with remote sensing images in natural language. Our approach combines a pretrained text encoder with a visual encoder and adopts supervised contrastive learning to align the visual and textual modalities. We explore several text encoders and label representation methods and compare their abilities to encode transferable land cover semantics. The model's capacity to predict a set of different land cover labels on an unseen dataset is also explored to illustrate the generalization capacities across domains of our approach. Overall, TACOSS is a general method and permits adapting between different sets of land cover labels with minimal computational overhead. Code

关键词： Land cover mapping Open vocabulary semantic segmentation Vision-language model for remote sensing

来源：评论

学校读者我要写书评

暂无评论

Target Detection of High-Resolution remote sensing images Based on Convolutional Neural Network with Salient Features

引用

CHEMISTRY AND TECHNOLOGY OF FUELS AND OILS 2025年第6期60卷 1627-1638页

作者： Chen, Yang Zhang, Xiuying Zhao, Qiulan Bao, Bowen Yang, Jing Li, Bin Zhou, Ruiyi Changchun Univ Technol Sch Comp Sci & Engn Changchun Peoples R China PetroChina Qinghai Oilfield Branch Qinghai Prov Oil Prod Plant 3 Mangya Haixi Peoples R China PetroChina Qinghai Oilfield Branch Qinghai Prov Oil Prod Plant 1 Mangya Haixi Peoples R China China Natl Petr Corp Gas Prod Plant Changqing Oilfield Branch 3 Ordos Inner Mongolia Peoples R China PetroChina Qinghai Oilfield Gas Prod Plant Infrastruct Project Ctr Golmud Qinghai Peoples R China Daqing Oilfield Co Ltd Oil Prod Plant 8 Harbin Heilongjiang Peoples R China

The processing technology of remote sensing images has attracted more and more attention. Since remote sensing image target detection technology has a wide range of applications in, terrain exploration and post-disaster reconstruction, etc. remote sensing image target detection refers to finding the target of interest in the remote sensing image and giving the specific location, while remote sensing image target recognition is the further classification of a certain target, which is a long-term concern in the field of remote sensing image processing. Convolutional Neural Network CNN (Convolutional Neural Network) has achieved great success in the field of computer vision with its deep semantic features, and in recent years, it has been increasingly applied to remote sensing image target detection and recognition tasks. Aiming at the task of remote sensing image target detection, this paper proposes a new deep feature-based remote sensing image target detection method. The depth feature extracted by CNN is used to extract the region of interest, and the target confirmation of the region of interest is carried out through multiple scales of CNN. This method does not require bounding box data for training, and improves the detection accuracy and reduces the false alarm rate.

关键词： remote sensing images convolutional neural networks deep features convolutional feature fusion object detection leakage monitoring pipeline corrosion remote pressure and flow monitoring geological disaster warning

来源：评论

学校读者我要写书评

暂无评论

Multimodal self-supervised learning for remote sensing data land cover classification

引用

pattern recognition 2025年 157卷

作者： Xue, Zhixiang Yang, Guopeng Yu, Xuchu Yu, Anzhu Guo, Yinggang Liu, Bing Zhou, Jianan Beijing Aviat Meteorol Inst Beijing 100085 Peoples R China North China Univ Water Resources & Elect Power Zhengzhou 450046 Peoples R China Informat Engn Univ Zhengzhou 450001 Peoples R China Northwest Inst Nucl Technol Xian 710024 Peoples R China Lanzhou Jiaotong Univ Fac Geomat Lanzhou 730070 Peoples R China

Deep learning has revolutionized the remote sensing image processing techniques over the past few years. Nevertheless, annotating high-quality samples is difficult and time-consuming, which limits the performance of deep neural networks because of insufficient supervision information. Aiming to solve this contradiction, we investigate the multimodal self-supervised learning (MultiSSL) paradigm for pre-training and classification of remote sensing image. Specifically, the proposed self-supervised feature learning model consists of asymmetric encoder-decoder structure, in which deep unified encoder learns high-level key information characterizing multimodal remote sensing data and task-specific lightweight decoders are developed to reconstruct original data. To further enhance feature extraction capability, the cross-attention layers are utilized to exchange information contained in heterogeneous characteristics, thus learning more complementary information from multimodal remote sensing data. In fine-tuning stage, the pre-trained encoder and cross-attention layer serve as feature extractor, and leaned characteristics are combined with corresponding spectral information for land cover classification through a lightweight classifier. The self-supervised pre-training model can learn high-level key features from unlabeled samples, thereby utilizing the feature extraction capability of deep neural networks while reducing their dependence on annotated samples. Compared with existing classification paradigms, the proposed multimodal self-supervised pre-training and fine-tuning scheme achieves superior performance for remote sensing image land cover classification.

关键词： remote sensing image Unsupervised pre-training Multimodal self-supervised Feature learning

来源：评论

学校读者我要写书评

暂无评论

Application of remote sensing image processing for Classification and recognition 15

Application of Remote Sensing Image Processing for Classific...

引用

15th IEEE International Conference on Advanced Infocomm Technology, ICAIT 2023

作者： Shi, Xiaolong Huang, Dan Li, Wenjie Wang, Xianjie College of Computer Science and Engineering Chongqing University of Technology Chongqing China China Research and Development Academy of Machinery Equipment Beijing China Chongqing High-tech Zone Pegasus Innovation Institute Chongqing University of Technology Chongqing China

ISBN: (纸本)9798350314120

Aiming at the difficulties in object detection and recognition in remote sensing images caused by high background complexity, large scale variations of targets, and the presence of numerous small objects, an improved method for remote sensing image object detection based on YOLOv7-tiny is proposed. This method combines the loss function based on normalized Gaussian Wasserstein distance (NWD) with the CIoU loss function to address the problem of sensitivity to positional deviation of small objects by IoU-Loss. The addition of a global attention mechanism (GAM) in the backbone network reduces information diffusion and enhances the interaction at the global dimension to mitigate the interference of complex backgrounds in remote sensing images on the model, enabling the model to focus on the feature extraction of the desired targets. Finally, the coupled detection head (Coupled Head) of the model is replaced with a decoupled detection head (Decoupled Head), allowing the classification and regression tasks to output from different branches to achieve decoupling and avoid the decrease in detection accuracy caused by conflicts between classification and regression. The experimental results of this method on the public dataset DIOR achieved 88.73% accuracy, which is an improvement of 1.78% compared to the unimproved method's accuracy of 86.95%. Furthermore, compared to other researchers' methods tested on DIOR, the proposed method also shows improvement, thus validating its effectiveness. © 2023 IEEE.

关键词： image classification

来源：评论

学校读者我要写书评

暂无评论

Military Aircraft recognition Method Based on Attention Mechanism in remote sensing images

引用

IET image processing 2025年第1期19卷

作者： Liu, Kun Xu, Zhengfan Liu, Yang Xu, Guofeng Naval Aviat Univ Qingdao Campus Qingdao Shandong Peoples R China Harbin Engn Univ Qingdao Innovat & Dev Base Qingdao Shandong Peoples R China

remote sensing images play a crucial role in fields such as reconnaissance and early warning, intelligence analysis, etc. Due to factors such as climate, season, lighting, occlusion and even atmospheric scattering during remote sensing image acquisition, targets of the same model exhibit significant intra-class variability. This article applies deep learning technology to the field of military aircraft recognition in remote sensing images and proposes a You Only Look Once Version 8 Small (YOLOv8s) remote sensing image military aircraft recognition algorithm based on an attention mechanism-YOLOv8s-TDP (YOLOv8s+TripletAttention+dysample+PIoU). First, the TripletAttention attention module is used in the neck network, which captures cross-dimensional interactions and utilises a three-branch structure to calculate attention weights. This further enhances the network's ability to preserve details and restore colours in the process of image fusion. Secondly, an efficient dynamic upsampler, dysample, is used to achieve dynamic upsampling through point sampling, which improves the problems of detail loss, jagged edges, and image distortion that may occur with nearest neighbour interpolation. Finally, replacing the original model loss function with PIoU (Pixels Intersection over Union), IoU (Intersection over Union) is calculated at the pixel level to more accurately capture small overlapping areas, reduce missed detection rates, and improve accuracy. On the publicly available dataset The remote sensing image Military Aircraft Target recognition Dataset(MAR20), our proposed YOLOv8s-TDP model achieved a Precision${\mathrm Precision} $ of 82.96%, Recall${\mathrm Recall} $ of 80.71%, mAP0.5$mA{P}_{0.5}$ of 87.11% and mAP0.5-0.95$mA{P}_{0.5 - 0.95}$ of 65.88%, outperforming the original YOLOv8s model, RT-DETR model, YOLOv5 series model, YOLOv7 series model, and YOLOv11 series model. Compared with the original YOLOv8s model, the YOLOv8s-TDP model improves Precision$Precision$ by 0

关键词： image processing object detection

来源：评论

学校读者我要写书评

暂无评论

A Texture Reconstructive Downsampling for Multi-Scale Object Detection in UAV remote-sensing images

引用

SENSORS 2025年第5期25卷 1569-1569页

作者： Zheng, Wenhao Xiong, Bangshu Chen, Jiujiu Ou, Qiaofeng Yu, Lei Nanchang Hangkong Univ Sch Informat Engn Nanchang 330063 Peoples R China Key Lab Jiangxi Prov Image Proc & Pattern Recognit Nanchang 330063 Peoples R China

Unmanned aerial vehicle (UAV) remote-sensing images present unique challenges to the object-detection task due to uneven object densities, low resolution, and drastic scale variations. Downsampling is an important component of deep networks that expands the receptive field, reduces computational overhead, and aggregates features. However, object detectors using multi-layer downsampling result in varying degrees of texture feature loss for various scales in remote-sensing images, degrading the performance of multi-scale object detection. To alleviate this problem, we propose a lightweight texture reconstructive downsampling module called TRD. TRD models part of the texture features lost as residual information during downsampling. After modeling, cascading downsampling and upsampling operators provide residual feedback to guide the reconstruction of the desired feature map for each downsampling stage. TRD structurally optimizes the feature-extraction capability of downsampling to provide sufficiently discriminative features for subsequent vision tasks. We replace the downsampling module of the existing backbone network with the TRD module and conduct a large number of experiments and ablation studies on a variety of remote-sensing image datasets. Specifically, the proposed TRD module improves 3.1% AP over the baseline on the NWPU VHR-10 dataset. On the VisDrone-DET dataset, the TRD improves 3.2% AP over the baseline with little additional cost, especially the APS, APM, and APL by 3.1%, 8.8%, and 13.9%, respectively. The results show that TRD enriches the feature information after downsampling and effectively improves the multi-scale object-detection accuracy of UAV remote-sensing images.

关键词： UAV object detection remote sensing downsampling back-projection

来源：评论

学校读者我要写书评

暂无评论

Research on computer multi feature fusion SVM model based on remote sensing image recognition and low energy system

RESULTS IN ENGINEERING

引用

RESULTS IN ENGINEERING 2025年 26卷

作者： Wu, Yangming Wu, Hao Tang, Xin Lv, Jianwei Zhang, Rufei Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Nanjing Vocat Inst Transport Technol Sch Elect & Informat Engn Nanjing 211188 Peoples R China Beijing Inst Control & Elect Technol Beijing 100045 Peoples R China

With the development of remote sensing technology, remote sensing image is widely used in environmental monitoring, land use and other fields. However, traditional image recognition methods often face the problems of high consumption of computing resources and slow processing speed, especially in low-energy systems. Therefore, this paper aims to explore a low-energy multi-feature fusion support vector machine (SVM) model based on remote sensing image recognition. The characteristics of remote sensing images and their application requirements in low energy consumption systems are analyzed, and a multi-feature extraction method combining spectral features, texture features and shape features is proposed. Aiming at the limitations of the traditional SVM model in processing high-dimensional data, an optimization algorithm is designed to reduce the computational complexity and improve the recognition accuracy of the model through dimensionality reduction and feature selection. Based on the model, a series of experiments were carried out on a low-energy hardware platform to test its performance in different scenarios. The experimental results show that the proposed multi-feature fusion SVM model has significantly improved recognition accuracy compared with the single feature method, and can effectively control the computing resource consumption, and can realize real-time recognition on low-energy systems.

关键词： remote sensing image recognition Low energy consumption system Computer Multi-feature fusion SVM model

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：