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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Mu, HaoDong Li, Haipeng Chen, Zhong Huazhong University of Science and Technology 1037 Luoyu Road HuBei WuHan430074 China Beijing Aerospace Automatic Control Institute Bejing100854 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

As remote sensing technology continues to advance, the accuracy and quantity of remote sensing images have significantly improved. The generation of a vast amount of available data has facilitated the widespread application of various deep learning methods in the field of remote sensing data processing, such as object detection, semantic segmentation, and change detection. In the aforementioned tasks, Change detection is used to identify alterations occurring on the Earth's surface by utilizing remote sensing (RS) data. In recent years, deep learning based methods have exhibited significantly superior performance compared to traditional change detection techniques. The fundamental strategy enabling these advancements involves extracting appropriate deep learning features from input remote sensing images through various backbone networks such as VGG, ResNet, DenseNet etc. Nevertheless, the features extracted by the aforementioned backbone networks may not fully cater to the specific requirements of remote sensing image change detection tasks. Consequently, our goal is to explore the influence of features extracted by different backbone networks on change detection tasks and introduce a specialized backbone network tailored for change detection. This endeavor aims to produce features that are better suited for the of change detection. The experimental results indicate that our specifically designed feature extraction network for remote sensing image change detection outperforms traditional networks in extracting task-specific features. These features are better suited for subsequent decoder modules, enhancing the generation of image-based change detection results. At the same time, we found that when using general backbone networks for change detection, ResNet achieves the highest metric accuracy, while DenseNet has the lowest memory usage and the fastest training and testing speed. Depending on the specific task, we can choose the appropriate backbone network as need

关键词： Feature extraction

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image Inpainting by Machine Learning Algorithms

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pattern recognition AND image ANALYSIS 2024年第2期34卷 237-243页

作者： Wan, Wei Leonov, Ivan Nanjing Res Inst Elect Engn Nanjing 210007 Jiangsu Peoples R China Belarusian State Univ Fac Appl Math & Comp Sci Minsk BELARUS

image inpainting is the process of filling in missing or damaged areas of images. In recent years, this area has received significant development, mainly owing to machine learning methods. Generative adversarial networks are a powerful tool for creating synthetic images. They are trained to create images similar to the original dataset. The use of such neural networks is not limited to creating realistic images. In areas where privacy is important, such as healthcare or finance, they help generate synthetic data that preserves the overall structure and statistical characteristics, but does not contain the sensitive information of individuals. However, direct use of this architecture will result in the generation of a completely new image. In the case where it is possible to indicate the location of confidential information on an image, it is advisable to use image inpainting in order to replace only the secret information with synthetic information. This paper discusses key approaches to solving this problem, as well as corresponding neural network architectures. Questions are also raised about the use of these algorithms to protect confidential image information, as well as the possibility of using these models when developing new applications.

关键词： image inpainting Wasserstein generative adversarial network generative adversarial network image imputation context encoders

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Learnable Prompt for Few-Shot Semantic Segmentation in remote sensing Domain

Learnable Prompt for Few-Shot Semantic Segmentation in Remot...

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

作者： Immanuel, Steve Andreas Sinulingga, Hagai Raja TelePIX Seoul South Korea

ISBN: (纸本)9798350365474

Few-shot segmentation is a task to segment objects or regions of novel classes within an image given only a few annotated examples. In the generalized setting, the task extends to segment both the base and the novel classes. The main challenge is how to train the model such that the addition of novel classes does not hurt the base classes performance, also known as catastrophic forgetting. To mitigate this issue, we use SegGPT as our base model and train it on the base classes. Then, we use separate learnable prompts to handle predictions for each novel class. To handle various object sizes which typically present in remote sensing domain, we perform patch-based prediction. To address the discontinuities along patch boundaries, we propose a patch-and-stitch technique by re-framing the problem as an image inpainting task. During inference, we also utilize image similarity search over image embeddings for prompt selection and novel class filtering to reduce false positive predictions. Based on our experiments, our proposed method boosts the weighted mIoU of a simple fine-tuned SegGPT from 15.96 to 35.08 on the validation set of few-shot Open-EarthMap dataset given in the challenge.

关键词： learnable prompt patch-and-stitch

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Rotated Multi-Scale Interaction Network for Referring remote sensing image Segmentation

Rotated Multi-Scale Interaction Network for Referring Remote...

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

作者： Liu, Sihan Ma, Yiwei Zhang, Xiaoqing Wang, Haowei Ji, Jiayi Sun, Xiaoshuai Ji, Rongrong Xiamen Univ Key Lab Multimedia Trusted Percept & Efficient Co Minist Educ China Xiamen 361005 Peoples R China

ISBN: (纸本)9798350353006

Referring remote sensing image Segmentation (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring image Segmentation (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery, leading to suboptimal segmentation results. To address these challenges, we introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS. RMSIN incorporates an Intra-scale Interaction Module (IIM) to effectively address the fine-grained detail required at multiple scales and a Cross-scale Interaction Module (CIM) for integrating these details coherently across the network. Furthermore, RMSIN employs an Adaptive Rotated Convolution ARC) to account for the diverse orientations of objects, a novel (contribution that significantly enhances segmentation accuracy. To assess the efficacy of RMSIN, we have curated an expansive dataset comprising 17,402 image-caption-mask triplets, which is unparalleled in terms of scale and variety. This dataset not only presents the model with a wide range of spatial and rotational scenarios but also establishes a stringent benchmark for the RRSIS task, ensuring a rigorous evaluation of performance. Experimental evaluations demonstrate the exceptional performance of RM-SIN, surpassing existing state-of-the-art models by a significant margin. Datasets and code are available at https://***/Lsan2401/RMSIN.

关键词： image segmentation

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Content-Adaptive Non-Local Convolution for remote sensing Pansharpening

Content-Adaptive Non-Local Convolution for Remote Sensing Pa...

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

作者： Duan, Yule Wu, Xiao Deng, Haoyu Deng, Liang-Jian Univ Elect Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350353006

Currently, machine learning-based methods for remote sensing pansharpening have progressed rapidly. However, existing pansharpening methods often do not fully exploit differentiating regional information in non-local spaces, thereby limiting the effectiveness of the methods and resulting in redundant learning parameters. In this paper, we introduce a so-called content-adaptive non-local convolution (CANConv), a novel method tailored for remote sensing image pansharpening. Specifically, CANConv employs adaptive convolution, ensuring spatial adaptability, and incorporates non-local self-similarity through the similarity relationship partition (SRP) and the partition-wise adaptive convolution (PWAC) sub-modules. Furthermore, we also propose a corresponding network architecture, called CANNet, which mainly utilizes the multi-scale self-similarity. Extensive experiments demonstrate the superior performance of CANConv, compared with recent promising fusion methods. Besides, we substantiate the method's effectiveness through visualization, ablation experiments, and comparison with existing methods on multiple test sets. The source code is publicly available at https://***/duanyll/CANConv.

关键词： Contrastive Learning

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SlotFusion: Object-Centric Audiovisual Feature Fusion with Slot Attention for remote sensing Scene recognition

SlotFusion: Object-Centric Audiovisual Feature Fusion with S...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Han, Fangzhou Yu, Tianyi Zhang, Lamei Si, Lingyu Zhang, Yiqi Dept. of Information Engineering Harbin Institute of Technology Harbin China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Despite significant advancements in remote sensing multimodal learning, particularly in image-image feature fusion, the exploration of audio-image feature fusion remains insufficient. Given the complexity and redundancy of ground objects in remote sensing images, accurately aligning audio features with image features during the fusion process is a critical challenge. In this paper, we introduce an object-centric feature fusion method named SlotFusion. By employing a slot attention-based feature decoupling module and a slot-based audiovisual feature fusion module, we transform modality features with complex semantic information into a set of slot features corresponding to object units and use gated activation units to adaptively implement object-centric feature fusion. Experiments on the Audio Visual Aerial Scene recognition dataset (ADVANCE) demonstrate that the proposed SlotFusion significantly improves remote sensing scene recognition performance, with a 7.04% increase in overall accuracy compared to previous methods, achieving state-of-the-art results. © 2025 IEEE.

关键词： Audiovisual feature fusion Gated feature fusion Object-centric learning Slot attention

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remote sensing image processing technology based on mobile augmented reality technology in surveying and mapping engineering

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SOFT COMPUTING 2023年第1期27卷 423-433页

作者： Lu, Wei Zhao, LiJun Xu, Rong Univ Army Engn Inst Commun Engn Nanjing 210001 Jiangsu Peoples R China Unit 32142 Baoding 071000 Hebei Peoples R China

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 remote sensing image processing 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 remote sensing feature classification. Experimental data show that the recognition success rate of the algorithm is 84%. The results show that mobile augmented reality technology and remote sensing image processing technology can improve the efficiency and accuracy of surveying and mapping engineering, and have strong ease of use and stability.

关键词： Mobile terminal Augmented reality technology remote sensing image processing technology Surveying and mapping engineering Target positioning

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SFFAFormer: An Semantic Fusion and Feature Accumulation Approach for remote sensing image Change Detection 7th

SFFAFormer: An Semantic Fusion and Feature Accumulation Appr...

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7th Chinese Conference on pattern recognition and Computer Vision

作者： Hong, Yile Liu, Xiangfu Chen, Mingwei Pang, Yan Huang, Teng Wei, Bo Lang, Aobo Zhang, Xi Guangzhou Univ Inst Artificial Intelligence Guangzhou Peoples R China Beihang Univ Beijing Peoples R China Sun Yat Sen Univ Sch Art Guangzhou Peoples R China

ISBN: (纸本)9789819784929;9789819784936

The change detection task of remote sensing images 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.

关键词： Change Detection remote sensing Semantic Fusion

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An enhanced feature matching multi-temporal port remote sensing image registration network E-SuperGlue

An enhanced feature matching multi-temporal port remote sens...

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

作者： He, Xinyu Feng, Shuyi Shao, Wenbo He, Hengxiang Wu, Liming Shanghai Aerospace Electronic Technology Institute Department of Aerospace Electronic Research Shanghai China

ISBN: (纸本)9781510680425

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 network E-SuperGlue with enhanced feature matching is proposed to solve the problems such as the difficulty of extracting feature points and matching feature descriptors for port multi-temporal image registration. Our network takes SuperGlue network as the basic framework. Firstly, Focus is introduced into the feature extraction network to increase the number to increase the number and detection rate of feature points. Secondly, LFPE module is added to feature matching network coding module to improve the information efficiency of feature descriptor coding. Finally, an improved multi-layer sensing structure E-MLP is added to the feature matching network to improve the utilization rate of channel information. © 2024 SPIE.

关键词： Graph neural networks

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Classification of Land Cover remote-sensing images Based on pattern recognition

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SCIENTIFIC PROGRAMMING 2022年第0期2022卷

作者： Xie, Haoyan Huang, Hai Nanjing 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 remote sensing technology, remote sensing image data plays an active role in the dynamic monitoring of global resource changes and land cover utilization. remote sensing image land cover classification is an important application direction of remote sensing data;how to further improve the accuracy of remote sensing image land cover classification is very important for the effective application of remote sensing data. The traditional remote sensing image land cover classification is mainly to classify remote sensing data according to the spectral data of ground objects. However, due to the complex environment of remote sensing images 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 remote sensing image based on pattern recognition. This paper is based on the experimental results of remote sensing 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

关键词： Pixels

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