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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

共 707 条记录，以下是1-10 订阅

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Water area extraction method based on Res2Net-Unet 24

Water area extraction method based on Res2Net-Unet

引用

1st International conference on image processing Machine Learning and pattern recognition

作者： Liu, Xiaoqun Li, Ning Commun Univ China Nanjing Peoples R China Vimicro Technology Corp Beijing Peoples R China

ISBN: (纸本)9798400707032

Regarding the issues that traditional water area extraction algorithms for remote sensing images require preset thresholds, are vulnerable to interference from mountain or vegetation shadows, and have poor robustness at different spatiotemporal scales, a model based on the Res2Net-UNet is proposed to extract water area from remote sensing images. The model adopts the UNet's encoder-decoder architecture, deepens the model's level with the ResNet network, and performs multi-scale feature extraction and fine-grained feature expression by combining Res2Net. Moreover, the model integrates an attention mechanism to increase the weight of key features and fully utilize the spatial and channel information in the images. After that, the model adds a skip connection to better fuse the encoder and decoder to preserve the detailed features of the image. Experimental findings on the GF-1 dataset demonstrate that the Res2Net-UNet model significantly enhances the accuracy of water extraction in remote sensing images.

关键词： water area extraction remote sensing image Res2Net-Unet attention mechanism

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RSID: A remote sensing image Dehazing Network 6th

RSID: A Remote Sensing Image Dehazing Network

引用

6th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Li, Yuan Zhao, Yafeng Northeast Forestry Univ 26 Hexing Rd Harbin 150036 Heilongjiang Peoples R China

ISBN: (纸本)9789819985364;9789819985371

Hazy images often lead to problems such as loss of image details and dull colors, which significantly affects the information extraction of remote sensing images, so it is necessary to research image dehazing. In the field of remote sensing, remote sensing images are characterized by large-size and rich information, so the processing of remote sensing images often has the problems of GPU memory overflow and difficult removal of non-uniform haze. For remote sensing image characteristics, an efficient and lightweight end-to-end dehazing method is proposed in this paper. We use the FA attention combined with smoothed dilated convolution instead as the main structure of the encoder, which can achieve imbalanced handling of hazy images with different levels of opacity while reducing parameter count. Channel weight fusion self-attention is added in the decoder part to realize the automatic learning and pixel-level processing of different receptive field features We tested the proposed method on both public datasets RESIDE and real large-size hazy remote sensing images. The proposed method achieved satisfactory results in our experiments, which proves the effectiveness of the proposed method.

关键词： Hazy images FA attention Channel weight fusion self-attention UAV remote sensing smoothed dilated convolution

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Forward-Forward Algorithm for Hyperspectral image Classification

Forward-Forward Algorithm for Hyperspectral Image Classifica...

引用

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

作者： Reyes-Angulo, Abel A. Paheding, Sidike Michigan Technol Univ 1400 Townsend Dr Houghton MI 49931 USA Fairfield Univ 1073 N Benson Rd Fairfield CT 06824 USA

ISBN: (纸本)9798350365474

The back-propagation algorithm has long been the defacto standard in optimizing weights and biases in neural networks, particularly in cutting-edge deep-learning models. Its widespread adoption in fields like natural language processing, computer vision, and remote sensing has revolutionized automation in various tasks. The popularity of back-propagation stems from its ability to achieve outstanding performance in tasks such as classification, detection, and segmentation. Nevertheless, back-propagation is not without its limitations, encompassing sensitivity to initial conditions, vanishing gradients, overfitting, and computational complexity. The recent introduction of a forward-forward algorithm (FFA), which computes local goodness functions to optimize network parameters, alleviates the dependence on substantial computational resources and the constant need for architectural scaling. This study investigates the application of FFA for hyperspectral image classification. Experimental results and comparative analysis are provided with the use of the traditional back-propagation algorithm. Preliminary results show the potential behind FFA and its promises.

关键词： Forward-Forward Algortihm Hyperspectral image Classification remote sensing

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HSDFormer: an improved transformer for remote sensing image semantic segmentation

HSDFormer: an improved transformer for remote sensing image ...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Bai, Yang Gao, Suixiang University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Zhongguancun Laboratory Beijing100049 China

ISBN: (纸本)9781510680425

The extraction of building locations is crucial in the field of remote sensing, commonly applied in tasks such as emergency response, urban planning, and environmental monitoring. Existing methods often employ convolutional models such as ResNet and U-Net. however, these models struggle to capture long-range features of geographical objects, limiting their practical effectiveness. Meanwhile, Transformer based models face challenges due to their quadratic computational complexity. Additionally, the characteristics of remote sensing images, including dense arrangement, small scale, and class imbalance, pose difficulties for traditional patch-merging modules in downsampling, leading to the loss of significant building information. To address such limitations, we present the HSDFormer model in this paper. The model achieves a reduction in the computational complexity of self-attention calculation through a straightforward sequential reduction process. Additionally, our model has optimized the downsampling process specifically for remote sensing semantic segmentation tasks, effectively minimizing the loss of crucial information during layer-wise feature extraction. We evaluate the HSDFormer on the WHU Building Dataset and Satellite Dataset ii (East Asia). The results demonstrate the model's effectiveness in accurately segmenting building locations, surpassing performance compared to other baseline models. © 2024 SPIE.

关键词： Semantic Segmentation

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Triplet Network with Autoencoder for remote sensing image scene recognition and classification

Triplet Network with Autoencoder for Remote sensing image sc...

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2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Hu, Fangchun Dai, Yifeng Wu, Lie Anhui University Anhui China Chuzhou University Anhui China

ISBN: (纸本)9781510680425

remote sensing image scene categorization is a crucial process that involves assigning semantic labels to images based on their content,which has wide-ranging applications in a number of *** inherent potential of deep neural networks to learn attractive features has substantially sped up progress in the field, resulting in considerable breakthroughs in remote sensing and scene classification powered by deep ***, the existing literature lacks a comprehensive evaluation of research findings in deep learning for remote sensing image scene categorization, despite the notable advancements in this *** CNN has shown remarkable efficacy in extracting image features, ranging there are persistent challenges that hinder its optimal performance in the domain of categorization of images from data collected from remote *** intrinsic attributes linked to remote sensing image data, such as intraclass variability and inter-class similarities, result in a notable decrease in the classification accuracy of the ***, the discernible impact of noise and interference further exacerbates limitations in CNN performance within this specific *** response to these challenges, this study introduces an innovative classification methodology that seamlessly integrates a triplet network and ***, the paper systematically conducts a rigorous comparative assessment, evaluating the performance of diverse image classification models across various remote sensing *** holistic approach aims to contribute to the evolving landscape of remote sensing image scene classification by addressing existing gaps and advancing our understanding of effective model architectures. © 2024 SPIE.

关键词： Deep neural networks

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A remote sensing image de-noising method based on wavelet packet decomposition and information entropy threshold function

A remote sensing image de-noising method based on wavelet pa...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Ma, Hongjin Zhao, Jiayu Zhang, Weiwei School of Science Xi'an University of Architecture and Technology Shaanxi Xi'an710055 China School of Mathematics and Statistics Northwestern Polytechnical University Shaanxi Xi'an710072 China

ISBN: (纸本)9781510680425

remote sensing images are widely used in various fields, such as geographic information systems, environmental monitoring, and agricultural management. However, remote sensing images are often corrupted by various noises during acquisition, transmission, and storage, which degrade the image quality and affect the image analysis and utilization. Therefore, de-noising remote sensing images is an important step to improve the image quality and application performance. In this paper, we propose a remote sensing image de-noising method based on wavelet packet decomposition and information entropy threshold function. The proposed method uses wavelet packet decomposition to perform multi-resolution analysis on remote sensing images, and combines information entropy threshold function to adaptively suppress noises. The experimental results show that the proposed method can effectively remove Gaussian noise and salt-and-pepper noise, and preserve the edge and detail information of remote sensing images. The proposed method outperforms the existing methods in terms of peak signal-to-noise ratio, structural similarity index, and visual quality. © 2024 SPIE.

关键词： Wavelet decomposition

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A fusion algorithm of SAR and panchromatic remote sensing images for intertidal extraction

A fusion algorithm of SAR and panchromatic remote sensing im...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Li, Wenchao Liu, Sheng Ding, Hao He, Liang Chen, Zhenhua Li, Qingliang Xue, Kaichuang China Wenchang Spacescraft Launch Site 799 Shugang Ave Hainan Wenchang471300 China National University of Defense Technology 109 Deya Road Hunan Changsha410073 China

ISBN: (纸本)9781510680425

Intertidal terrain extraction based on satellite remote sensing images is of great military application and civilian value. In this paper, we propose a fusion algorithm based on SAR and optical remote sensing image to enhance intertidal feature. With the SAR and panchromatic images from different phases, we use an improved shift-invariant discrete wavelet transform algorithm to obtain higher quality images that can not only highlight the shape of the intertidal zone, but also highlight the characteristics of the coastline. The real data experiment and comparison experiments with other algorithms show that the algorithm proposed in this paper is effective in enhancing the features of the intertidal zone. © 2024 SPIE.

关键词： Optical remote sensing

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remote sensing image scene recognition based on densenet-169

Remote sensing image scene recognition based on densenet-169

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Cui, Xiyue School of Information Science and Technology Dalian Maritime University Dalian116026 China

ISBN: (纸本)9781510680425

Scene classification has become an effective technique for classifying high spatial resolution remote sensing ***, in the traditional deep learning convolutional neural network, as the remote sensing image passes through the layers in the network, some of the features will be gradually lost, resulting in a significant decrease in the classification accuracy and precision of scene recognition, and there is the problem of underutilization of *** addition, remote sensing images themselves have high *** overcome these challenges, we adopt the DenseNet ***, we first train remote sensing images from the UCMerced dataset as network ***, we introduced the DenseNet-169 model based on migration *** with DenseNet-121, DenseNet-169 has more neural network layers, and this difference is mainly manifested in the number of convolutional layers in the dense ***-169 has more convolutional layers, which increases the complexity and the number of parameters of the model, bringing the following advantages: stronger expressive power, which enables the extraction of more complex feature patterns;faster training time, thanks to the densely-connected nature, which efficiently utilizes the gradient flow;and better generalization ability, especially on large-scale complex *** our experiments, the introduced DenseNet-169 shows excellent performance compared to other state-of-the-art deep convolutional networks on the UCMerced dataset, with an accuracy of 95.14%, a precision of 95.31%, a Kappa coefficient of 94.90%, and an F1-score of 95.11%.The experimental results show that the method can make full use of the features of remote sensing images and show good visual effect, providing a good method for scene recognition. © 2024 SPIE.

关键词： Convolutional neural networks

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remote sensing image super-resolution reconstruction using depth-dense residual and sub-pixel convolutional model

Remote sensing image super-resolution reconstruction using d...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Liu, Haoguang Fu, Yuwen Wang, Zhoujie Chinese Flight Test Establishment Xian China

ISBN: (纸本)9781510680425

Existing super-resolution reconstruction algorithms for remote sensing images often struggle to fully extract and utilize features in complex scenes, and the reconstruction results are not optimal due to the influence of *** propose a reconstruction network model that combines deep dense residual module and sub-pixel *** model connects multiple dense residual modules through recursive linking and introduces a channel attention mechanism to extract multiscale features from the *** the feature reconstruction process, a sub-pixel convolution structure is introduced to reduce the impact of noise and enhance reconstruction *** model is tested on the UC Merced Land Use public dataset, and the results demonstrate that the reconstruction results of our proposed model algorithm have better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) at different scales compared to the current mainstream reconstruction algorithm *** is proven that the proposed model can significantly improve the reconstruction quality through comparative analysis experiment, meeting the needs for high-resolution remote sensing image processing. © 2024 SPIE.

关键词： image reconstruction

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remote sensing aircraft small object detection algorithm based on YOLOv5

Remote sensing aircraft small object detection algorithm bas...

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2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Qiu, Yijuan Xue, Jiefeng Zhang, Jie Jiang, Ping Zhang, Gang Lei, Tao National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of Sciences Sichuan Chengdu610209 China Institute of Optics and Electronics Chinese Academy of Sciences Sichuan Chengdu610209 China University of Chinese Academy of Sciences Beijing100049 China Southwest University of Science and Technology 621010 China

ISBN: (纸本)9781510680425

With the rapid development of remote sensing technology, remote sensing images play an important role in the agricultural field, geological field, and natural disaster detection. The size of aircraft in complex scenes in remote sensing images is extremely small, and aircraft of different models have similar shapes. Therefore, improving the accuracy of aircraft target recognition is a challenging task. We propose an improved aircraft small target recognition method based on yolov5, which can improve the recognition accuracy of aircraft targets while ensuring the speed of the model. The specific content is as follows: To address the problem of lack of remote sensing aircraft training sets, we use existing public remote sensing images to combine with aircraft model images. Most of the aircraft models only occupy a dozen to twenty pixels in the 1k*1k image, and perform Scale the generated data set;in order to better combine features of different scales and obtain higher-level feature fusion, introduce the BiFPN module with more residual connections and more complete feature fusion;use the SE attention mechanism to learn the weights of different features, extract information that is more important for detection and improve model performance;in view of the small size of the aircraft, a detection method based on Wasserstein distance such as NWD (Normalized Wasserstein Distance) is selected as the loss function. © 2024 SPIE.

关键词： Training aircraft

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