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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing XXIV"

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image Editing based on Diffusion Model for remote sensing image Change Captioning 2

Image Editing based on Diffusion Model for Remote Sensing Im...

引用

2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： Cai, Miaoxin Chen, He Li, Can Gan, Shuyu Chen, Liang Zhuang, Yin Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

remote sensing image Change Captioning (RSICC) is a task that utilizes natural language to describe changes in remote sensing images of the same area captured at different times. However, the significant temporal intervals between multi-temporal images, the infrequency of observable changes, and the limitations on observation locations make it difficult to acquire and annotate a large and diverse dataset for analyzing change in multi-temporal images. The scarcity of labeled data hinders the training of RSICC models, leading to poor generalization. Compared to annotated registered bi-temporal image, single-temporal data is easier to obtain. Therefore, to tackle the issue of poor generalization of RSICC models under limited annotated sample conditions, a text-guided image pairs generation (TGIPG) method is proposed to create synthetic RSICC datasets from single-temporal data and randomly sampled text instructions via a diffusion-based controllable image editing model. This approach generates more valid pairs of multi-temporal samples to address the constraints of limited change information. Specifically, this method utilizes language instructions to introduce change information into the diffusion process, gradually transforming the pre-phase image into the post-phase image. Our experiments on the LEVIR-CC dataset show that synthetic data can significantly enhance the performance of any RSICC model, with a restricted number of training samples, by employing this plug-and-play TGIPG method. © 2024 IEEE.

关键词： Spatio-temporal data

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LOCAL AND GLOBAL FEATURE ADAPTIVE ADJUSTMENT NETWORK FOR remote sensing image SCENE CLASSIFICATION 49

LOCAL AND GLOBAL FEATURE ADAPTIVE ADJUSTMENT NETWORK FOR REM...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Cao, Feng Liu, Chang Li, Deyu Qian, Yuhua Zhang, Chao Zhang, Hu Shanxi Univ Sch Comp & Informat Technol Sch Big Data Taiyuan 030006 Peoples R China Shanxi Univ Key Lab Computat Intelligence & Chinese Informat Minist Educ Taiyuan 030006 Peoples R China Shanxi Univ Inst Big Data & Ind Taiyuan 030006 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Convolutional neural network (CNN)-based methods have been extensively used for remote sensing scene classification (RSSC) and have obtained remarkable classification results. However, its limitations in extracting global features have hindered further improvement. Transformers can directly capture global features through self-attention mechanisms, but they have deficiencies in modeling local features. Currently, an approach that directly combines CNN and Transformer features may lead to feature imbalance, and introduce redundant information. To address these problems, we propose a local and global feature adaptive adjustment network (LGFAANet) for RSSC. First, we employ a dual-branch network structure to extract local and global features from remote sensing scene images. Second, we design a local and global feature adaptive adjustment module (LGFAA) to dynamically allocate weights to the features. Third, we use a multi-layer feature aggregation module (MLFA) to aggregate the adjusted features, thereby further enhancing feature representation. Finally, we introduce joint loss to accelerate network convergence, while reducing intra-class distance and increasing inter-class distance. Experimental results demonstrate that our proposed method displays enhanced feature representation ability and outperforms existing state-of-the-art methods.

关键词： CNN Transformer feature adaptive adjust RSSC

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Multi-Source remote sensing image Registration Method Based on Nonlinear Scale Space and Phase Congruency 2

Multi-Source Remote Sensing Image Registration Method Based ...

引用

2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： Chen, Yuejuan Li, Xianglei Huang, Pingping Tan, Weixian Li, Xiujuan Yin, Bo Inner Mongolia University of Technology Mongolia Key Laboratory of Radar Technology and Application College of Information Engineering Hohhot China Inner Mongolia University of Technology Mongolia Key Laboratory of Radar Technology and Application College of Resources and Environmental Engineering Hohhot China

ISBN: (纸本)9798331515669

Due to differences in sensor characteristics, imaging conditions, and time among multi-source remote sensing images, nonlinear changes in image radiance intensity occur, increasing the difficulty of image registration. Existing techniques often face challenges in the robustness and accuracy of feature matching when addressing these nonlinear radiance variations. To solve this problem, this paper proposes a phase congruency remote sensing image registration method based on nonlinear scale space. This method constructs a nonlinear scale space of the image using an improved anisotropic diffusion filter, effectively suppressing noise while preserving the edges and detailed features of the image. It calculates the edge strength and corner strength of the image at different scales using a phase congruency model and extracts feature points from the edge strength map and corner strength map. Multi-scale, multi-direction descriptors are constructed using the response values generated by the Log-Gabor filter to enhance the robustness of feature extraction, thereby achieving precise registration of multi-source remote sensing images. Experimental results show that this method significantly suppresses image noise and nonlinear radiance differences, and outperforms traditional algorithms and other improved registration algorithms regarding the number of correctly matched point pairs and robustness. © 2024 IEEE.

关键词： Gabor filters

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Land cover classification of UAV visible remote sensing based on superpixel

Land cover classification of UAV visible remote sensing base...

引用

2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Zeng, Yushuang College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510680425

Because of the convenience and low cost of obtaining visible remote sensing images of UAV, it is widely used in agricultural production. In land cover classification, in order to obtain more homogeneous superpixels of UAV remote sensing images and achieve accurate classification, a superpixel segmentation algorithm with improved DPC (density peak clustering) of grid point was proposed, and land cover classification was carried out based on this algorithm. The method is as follows: the paper defined color-space distance measurement to calculate local density and relative distance;then, using the connected graph, the connected distance between each grid point to be allocated and the cluster center point connected to the neighborhood is obtained, and it is assigned to the superpixel cluster with the smallest distance. Finally, pixel blocks are generated according to the super-pixel segmentation image and as data to classification by resnet50. Experiments show that the classification accuracy of the proposed algorithm is 99.6%, which is superior to SLIC and the original superpixel segmentation algorithm with DPC of grid point, and proves that the algorithm is effective. © 2024 SPIE.

关键词： image segmentation

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Improved GMM High-Resolution remote sensing image Land Cover Classification 4

Improved GMM High-Resolution Remote Sensing Image Land Cover...

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4th International signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Chunyan, Wang Kaixin, Fu Xiang, Wang School of Software Liaoning Technical University Huludao China Department of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9798331528676

To address the increased complexity of surface cover, increased heterogeneity within homogeneous regions, and increased similarity between different regions in high-resolution remote sensing images, which lead to increased classification difficulty, a supervised learning method based on a double neighborhood relationship Gaussian Regression Mixture Model (GRMM) is proposed. Firstly, supervised sampling of image regions is conducted, fitting histograms using the least squares method to establish Gaussian Mixture Models (GMM) for each land cover to represent the complex gray-scale features of land cover. Secondly, local spatial information of adjacent pixels is introduced into the image gray-scale space to construct a Gaussian Regression Model (GRM). Finally, in the membership degree space, the neighborhood relationship is processed again to achieve classification decision. The kappa coefficients of GRMM on synthetic images and real high-resolution remote sensing images respectively reaches 97.2% and 98.5%. Compared to existing mainstream models, GRMM demonstrates strong classification efficiency, noise reduction capability, and generalization ability, with clear classification boundaries, effectively improved the land cover classification capability of remote sensing images. ©2024 IEEE.

关键词： Self-supervised learning

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Classification of hyperspectral remote sensing images based on three-dimensional convolutional neural networks 4

Classification of hyperspectral remote sensing images based ...

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4th International conference on Advanced Algorithms and signal image processing, AASIP 2024

作者： Zhu, Hongli Luo, Jie Li, Kai Han, Peng Li, Lvzhou Wuhan Metro Group Co. Ltd. Wuhan China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

In recent years, with the rapid development of hyperspectral remote sensing technology, hyperspectral remote sensing data has witnessed progressive utilization in the subway transportation industry. Due to the large amount of redundant information in hyperspectral images and the high correlation between band information, traditional classification algorithms often find it challenging to obtain more accurate classification information. Convolutional neural networks in the field of subway transportation have achieved great success in hyperspectral remote sensing image classification. These networks operate quickly and offer high classification accuracy, thereby greatly advancing hyperspectral remote sensing image classification technology and benefiting the subway industry. This article analyzes and compares the classification accuracy of two hyperspectral remote sensing image classification methods based on three-dimensional convolutional neural networks, capitalizing on the Pavia University and Indian Pines datasets. It specifically examines the classification of transportation infrastructure, particularly subway lines, within these datasets. The two models differ in data input sizes and convolutional layer parameters. By varying the sample size and epochs for analysis, we assess the results using the Kappa coefficient and accuracy to evaluate the models’ classification performance. The findings indicate that increasing the data input size and period can effectively enhance the classification accuracy of subway lines and other land features. Notably, increasing the data input size leads to a more significant improvement, while the effect of increasing the number of epochs has a certain threshold beyond which improvements diminish. © 2024 SPIE.

关键词： Subways

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Application of remote sensing in retrieving combustible fuel load in Guizhou region

Application of remote sensing in retrieving combustible fuel...

引用

2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Zhang, Sihang Yang, Zhi Zhu, Kuanjun Liu, Bin Zhou, Lixian Han, Junke Liu, Chang China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9781510680425

Guizhou Province, situated in the southwest of China, boasts diverse and complex geographical environments and abundant forest resources. However, it faces threats from natural disasters like forest fires. Accurate estimation of combustible fuel load in Guizhou is crucial for assessing fire risks and implementing effective fire management strategies. This paper employs remote sensing technology, utilizing various remote sensing datasets including satellite imagery and ground observation data, combined with geostatistical methods to retrieve combustible fuel load in Guizhou. Furthermore, the application value of the retrieval results in fire prevention management and forest resource protection is discussed. © 2024 SPIE.

关键词： Risk management

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Hazy remote sensing image Semantic Segmentation with Weak Annotations via Pre-training Optimization and Co-training

Hazy Remote Sensing Image Semantic Segmentation with Weak An...

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Xu, Junda Zhang, Libao School of Artificial Intelligence Beijing Normal University Beijing100875 China

ISBN: (纸本)9798350368741

In recent years, weakly supervised semantic segmentation has emerged as a prominent research topic in the field of remote sensing image semantic segmentation due to its cost-effective labeling advantages. However, the presence of haze in remote sensing images poses significant challenges to accurate semantic segmentation. Despite the numerous haze removal methods developed for remote sensing images, their efficacy in the subsequent task of semantic segmentation remains inadequate. To address these issues, this paper aims to enhance the robustness of the segmentation network against haze interference by proposing a weakly supervised semantic segmentation framework based on pre-training optimization and dual-network co-training. The proposed approach employs a pseudo-label optimization network to effectively filter out noise interference and subsequently trains two parallel segmentation networks that mutually guide each other for enhanced robustness. Additionally, an edge optimization loss is introduced to improve prediction accuracy by incorporating both edge and texture information. Experimental comparisons with alternative methods across multiple datasets validate the superior performance of our proposed method. © 2025 IEEE.

关键词： co-training image dehazing remote sensing image Semantic segmentation weakly supervised learning

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K2NN: Self-Supervised Learning with Hierarchical Nearest Neighbors for remote sensing 48

K2NN: Self-Supervised Learning with Hierarchical Nearest Nei...

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48th IEEE International conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Yuan, Jianlong Xu, Yuanhong Wang, Zhibin Alibaba Group China

ISBN: (纸本)9781728163277

Self-supervised learning aims to learn applicable pre-trained models from massive unlabeled data. Besides image-level pretext tasks, many recent pixel-level studies have been pro-posed to learn dense information in each image. However, most of those methods focus on obtaining pair of matched patches from the same image with different augmentation. At the same time, little effort is devoted to exploiting matched patches from different images. In this work, we develop a novel pixel-level task that leverages an ensemble of nearest neighbors from multiple images to explore diverse objects in each image, especially for remote sensing data. Besides, a sampling strategy with a submodular function is adopted to efficiently update the memory bank consisting of patches. The extensive experiments on remote sensing data confirm the effectiveness of our method. © 2023 IEEE.

关键词： remote sensing

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Removal of thin clouds from high-resolution optical images based on multiscale feature fusion

Removal of thin clouds from high-resolution optical images b...

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2024 International conference on Advanced image processing Technology, AIPT 2024

作者： Xiao, Yunlong Hubei University of Technology Hubei Province Wuhan430000 China

ISBN: (纸本)9781510682542

High-resolution optical images are susceptible to atmospheric influences during their formation, and thin clouds are the most important influencing factor. Feature information loss due to thin-cloud coverage is a common problem. To solve this problem, a thin cloud removal method based on multi-scale feature fusion for high-resolution optical remote sensing images is proposed;this method can capture more image details. The multi-scale feature fusion module is introduced into the improved structure of the generative network model. By fusing multiple image scale information, the model can enhance the ability of extracting features such as shape, texture and colour. Experimental results on high-resolution remote sensing dataset show that the method achieves 29.4758 and 0.8902 for the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), respectively. Comparing the method with existing methods, it proves that the method has a more accurate effect in remote sensing imagery thin cloud removal. © 2024 SPIE.

关键词： Optical remote sensing

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