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

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Improved YOLOv5's remote sensing image detection algorithm

Improved YOLOv5's remote sensing image detection algorithm

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

作者： Chen, Naikang Zhang, Jinmin School of Mechanical Engineering Lanzhou Jiaotong University Gansu Lanzou730070 China

ISBN: (纸本)9781510680425

Addressing the challenges inherent in remote sensing image detection, notably the YOLOv5 detector's subpar accuracy due to limited detection target features, intricate detection backgrounds, and predominantly small targets, this paper presents the RS-YOLOv5 algorithm, an enhancement of the YOLOv5s model. Primarily, the conventional convolution in the backbone network is substituted with full-dimensional dynamic convolution to enrich the information gathered across multiple dimensions, thereby enhancing the extraction of target features. Additionally, a decoupled detection head replaces the traditional detection head, refining classification and positioning tasks while strategically leveraging features from different levels to mitigate background interference on small target detection. Next, adding the Normalized Wasserstein distance (NWD) measurement to the loss function is intended to enhance the accuracy of small target detection. The ablation analysis conducted on the DOTA dataset showcases a noteworthy improvement in the average accuracy of object detection in remote sensing imagery facilitated by the proposed RS-YOLOv5 algorithm, culminating in a 2.5% increase in average accuracy compared to the original algorithm. © 2024 SPIE.

关键词： Optical remote sensing

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Noise Removal Method for SAR images with Oblique Bands Based on Coherent Diffusion 4

Noise Removal Method for SAR Images with Oblique Bands Based...

引用

4th International conference on signal image processing and Communication, ICSIPC 2024

作者： Xu, Suqin Chi, Zonghao Li, Tingting Yu, Zhentao Navy Submarine Academy Shandong Qingdao China Qingdao Institute of Collaborative Innovation Shandong Qingdao China

ISBN: (纸本)9781510682467

Synthetic Aperture Radar (SAR) is an active earth observation system. The process of acquiring images by SAR system shows periodic characteristic stripe noise thus decreasing image quality, which seriously limits the application of SAR image interpretation and information extraction. Oblique band noise usually exists in system-level geometrically corrected remote sensing products obtained after geometric positioning, map projection, and resampling, and such remote sensing products have higher scientific application value and practical applicability. Therefore, this paper proposes a coherence-diffusion-based oblique band noise removal method for remote sensing images, and processes the simulated band noise and the actual presence of the band noise images, and qualitatively and quantitatively compares it with existing band removal methods, and then compares it with existing band removal methods. The method is compared qualitatively and quantitatively to verify the feasibility, effectiveness and practicality of the algorithm in this paper. The experimental results show that the method of this paper can be applied to strip noise at any angle, and the processing results are obviously better than the existing methods. © 2024 SPIE.

关键词： image quality

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

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

引用

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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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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Enhancing remote sensing Vision-Language Models for Zero-Shot Scene Classification

Enhancing Remote Sensing Vision-Language Models for Zero-Sho...

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

作者： El Khoury, Karim Zanella, Maxime Gérin, Benoît Godelaine, Tiffanie Macq, Benoît Mahmoudi, Saïd De Vleeschouwer, Christophe Ayed, Ismail Ben UCLouvain Belgium UMons Belgium ÉTS Montreal Canada

ISBN: (纸本)9798350368741

Vision-Language Models for remote sensing have shown promising uses thanks to their extensive pretraining. However, their conventional usage in zero-shot scene classification methods still involves dividing large images into patches and making independent predictions, i.e., inductive inference, thereby limiting their effectiveness by ignoring valuable contextual information. Our approach tackles this issue by utilizing initial predictions based on text prompting and patch affinity relationships from the image encoder to enhance zero-shot capabilities through transductive inference, all without the need for supervision and at a minor computational cost. Experiments on 10 remote sensing datasets with state-of-the-art Vision-Language Models demonstrate significant accuracy improvements over inductive zero-shot classification. Our source code is publicly available on Github: https://***/elkhouryk/RS-TransCLIP. © 2025 IEEE.

关键词： remote sensing scene classification transductive inference vision-language models zero-shot

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

Application of remote sensing in retrieving combustible fuel...

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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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