Despite significant advancements in remotesensing multimodal learning, particularly in image-image feature fusion, the exploration of audio-image feature fusion remains insufficient. Given the complexity and redundan...
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With the continuous advancement of science and technology, the improvement of mobile terminal hardware performance and the large-scale popularization of smart phones have brought new experiences and methods to surveyi...
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With the continuous advancement of science and technology, the improvement of mobile terminal hardware performance and the large-scale popularization of smart phones have brought new experiences and methods to surveying and mapping work. This article mainly studies the application of remotesensingimageprocessing technology based on mobile augmented reality technology in surveying and mapping engineering. First, perform grayscale processing on the image in the experiment, then remove the noise in the image and smooth the image through the median filter method and finally use the Canny operator to perform edge detection to obtain a binarized image containing only the target object, and this is done by image feature extraction. After using three-dimensional scanning modeling to extract the image feature points, the target manager is used for sample analysis. Obtain the projection matrix through the interface, and then perform coordinate conversion to complete the positioning of the target scene. In this paper, the BRISK feature point detection algorithm with fast speed and small calculation is used to detect the target, and SVM is used for remotesensing feature classification. Experimental data show that the recognition success rate of the algorithm is 84%. The results show that mobile augmented reality technology and remotesensingimageprocessing technology can improve the efficiency and accuracy of surveying and mapping engineering, and have strong ease of use and stability.
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 ...
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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 remotesensing (RS) photographs. Although self-attention (SA) and convolutional neural networks have demonstrated strong modeling capabilities on RS imagerecognition, their efficiency and expressiveness still deserve further optimization. In this article, 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 SA strategy to selectively aggregate spatial features and a sparse-tight pattern fusion 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, such as MSRWD, UAVid, and LoveDA, indicate that SDNet exceeds prior state-of-the-art techniques, with a 2-10% increase in mean intersection over union (IoU) 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% .
The change detection task of remotesensingimages provides an effective means and technology to detect changes on the Earth's surface, providing data support for disaster management. Although current methods most...
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ISBN:
(纸本)9789819784929;9789819784936
The change detection task of remotesensingimages provides an effective means and technology to detect changes on the Earth's surface, providing data support for disaster management. Although current methods mostly adopt hierarchical structures and variations of transformer-base models, they overlook the rich detailed features provided by shallow layers during the restoration process, as well as the accurate global features of deep layers, leading to the loss of edge details in the final change detection structure. As a solution to this problem, we suggest SFFAFormer, which employs a module design with enhanced channel learning in shallow layers to enhance edge details and feature transfer, and utilizes transformer-base modules with semantic accumulation computation in deep layers to ensure the accuracy of global information. Experimental results demonstrate that SFFAFormer surpasses many leading baselines and achieves outstanding performance on the LEVIR-CD and DSIFN-CD datasets.
Multi-temporal collaborative analysis of port scenes can enhance the representation ability of image scenes, and image registration is required before multi-temporal analysis. In this paper, an image registration netw...
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作者：
Xie, HaoyanHuang, HaiNanjing Univ
Philosophy Dept Nanjing 210023 Peoples R China Xuchang Univ
Sch Marxism Studies Xuchang 461000 Peoples R China Tongji Univ
Mobile Source Emiss Aftertreatment Inst Shanghai 200082 Peoples R China
With the development of remotesensing technology, remotesensingimage data plays an active role in the dynamic monitoring of global resource changes and land cover utilization. remotesensingimage land cover classi...
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With the development of remotesensing technology, remotesensingimage data plays an active role in the dynamic monitoring of global resource changes and land cover utilization. remotesensingimage land cover classification is an important application direction of remotesensing data;how to further improve the accuracy of remotesensingimage land cover classification is very important for the effective application of remotesensing data. The traditional remotesensingimage land cover classification is mainly to classify remotesensing data according to the spectral data of ground objects. However, due to the complex environment of remotesensingimages and the dynamic changes of the environment, traditional classification methods based only on pixel spectral data are often unable to achieve. A satisfactory classification result is achieved. In addition, some researchers have also proposed to combine pixel neighborhood texture information to supplement spectral feature data. Although the traditional classification method based on spectral features solves the problem of time-consuming visual interpretation, to a certain extent, due to the limited semantic expression ability and poor generalization ability of the design features, the classification accuracy is still not very satisfactory. This paper mainly studies the classification method of land cover remotesensingimage based on patternrecognition. This paper is based on the experimental results of remotesensing data in Nanjing Yuhuatai District in 2018 and 2019. The ground resolution of the data is 2.5 meters. Data is projected, corrected, and equalized. Half of the images covering 43.75 square kilometers are used as training samples, and the remaining 50 square kilometers are used for detection. In the classification results of this IndianPines data, OA only increased by nearly 10% to 86.2%, AA increased by 13%, r was 82.77%, and Kappa coefficient was 0.84. In the classification results of Salinas data, bot
The acquisition of sufficient labeled samples is often a significant challenge in the field of remotesensingimagery, due to the time-consuming and high cost nature of field data collection. As a result, researchers ...
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ISBN:
(纸本)9789819784929;9789819784936
The acquisition of sufficient labeled samples is often a significant challenge in the field of remotesensingimagery, due to the time-consuming and high cost nature of field data collection. As a result, researchers have recently aimed to explore and develop effective few-shot learning methods that can overcome the shortage of labeled data in remotesensingimagery. Few-shot learning aims to enable machine learning algorithms to learn from a few labeled data or even from a single sample. The classification of remotesensing data is challenging because of the high interclass similarity and intraclass diversity found within remotesensing scenes. Direct computation of similarities between query and support data in current methods can lead to confusion. We propose a discriminative representation-based classifier (DRC) for few-shot remotesensing scene categorization to overcome this issue. Specifically, we introduce two discriminative constraint terms in the objective function: intraclass and interclass constraints. The intraclass constraint term enhances the concentration of the learned representation vectors in same class learned by the classifier, while the interclass constraint term reduces the correlation between the representation vectors of different categories. The experimental findings on the difficult remotesensing datasets NWPU-RESISC45 and RSD46-WHU demonstrate that our proposed DRC method delivers cutting-edge results in few-shot remotesensing scene image classification.
Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single system and has gained widespread acceptance as a non-destructive scientific instrument for a wide range of applications. The c...
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Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single system and has gained widespread acceptance as a non-destructive scientific instrument for a wide range of applications. The current state of imaging spectroscopy spans diverse applications including but not limited to air-borne and ground-based computer vision systems. This paper presents the current state of research and industrial applications including precision agriculture, material classification, medical science, forensic science, face recognition and document image analysis, environment monitoring, and remotesensing, which can be aided through imaging spectroscopy. In this regard, we further discuss a comprehensive list of applications of imaging spectroscopy, pre-processing techniques, and spectral image acquisition systems. Likewise, publicly available databases and current software tools for spectral data analysis are also documented in this review. This review paper, therefore, could potentially serve as a reference and roadmap for people looking for literature, databases, applications, and tools to undertake additional research in imaging spectroscopy.
With the continuous development of Internet of Things technology, laboratory equipment management is gradually changing to the direction of intelligence and remote. In this paper, aiming at the data detection of labor...
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ISBN:
(纸本)9798400716607
With the continuous development of Internet of Things technology, laboratory equipment management is gradually changing to the direction of intelligence and remote. In this paper, aiming at the data detection of laboratory equipment, a solution of laboratory equipment image data patrol device based on Internet of Things technology is proposed. Through the acquisition, processing and transmission of equipment image data, the real-time monitoring and evaluation of equipment operation status and performance are realized. The research in this paper has certain reference value for improving the management efficiency and operation performance of laboratory equipment.
With the rapid development of computer vision, the applications of image texture recognition and classification are increasingly prevalent across various domains, particularly in medical imaging, industrial inspection...
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With the rapid development of computer vision, the applications of image texture recognition and classification are increasingly prevalent across various domains, particularly in medical imaging, industrial inspection, and remotesensingimage analysis, these applications hold significant practical importance. Traditional texture recognition techniques often rely on manually designed feature extraction methods, which tend to perform poorly in complex environments, are sensitive to noise and lighting variations, and are limited when dealing with non-uniform or multiscale textures. To address these shortcomings, this paper introduces two novel texture analysis methods that enhance the robustness of texture features and improve classification accuracy. The first part of the study presents the contourlet-kernel spectral regression (KSR) image texture feature extraction technique, which, by integrating Contourlet transform with Krawtchouk polynomials, effectively enhances the descriptive power and adaptability of features. The second part explores a texture image classification method based on domain-multiresolution cooccurrence matrices (MCM), which significantly improves the accuracy and robustness of the classification process by analyzing the co-occurrence characteristics of images at multiple resolutions. The introduction of these methods not only optimizes texture recognition performance but also advances the application of imageprocessing technologies in complex scenarios.
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