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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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Lightweight Convolutional Neural Network for High-Spatial-Resolution remote sensing Scenes Classification 12

Lightweight Convolutional Neural Network for High-Spatial-Re...

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12th International conference on Wireless Communications and signal processing (WCSP)

作者： Luo, Wang Yang, Wenqing Yu, Xian Wang, Ye Tan, Kai State Grid Elect Power Res Inst Co Ltd NARI Grp Co Ltd Nanjing 211000 Peoples R China Nanjing NARI Informat & Commun Technol Co Ltd Nanjing 211000 Peoples R China

ISBN: (纸本)9781728172361

High spatial resolution remote sensing (HSRRS) image classification and detection are playing an increasingly important role in land planning and resource utilization. At present, convolutional neural networks (CNN) have made great achievements in computer vision and natural language processing. CNN is also widely used in various tasks such as HSRRS image classification and detection. However, existing neural networks generally have the redundant parameters and the slow computation speeds, which are inconvenient to implement them into the mobile terminals. To solve this problem, in this paper, we propose a lightweight CNN model, which is named as the MobileNet. Simulation results are given to confirm that MobileNet can greatly reduce the parameters and accelerate the computation while ensuring high accuracy.

关键词： Convolutional neural network (CNN) lightweight high spatial resolution remote sensing classification MobileNet

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FEW-SHOT LEARNING FOR remote sensing image RETRIEVAL WITH MAML

FEW-SHOT LEARNING FOR REMOTE SENSING IMAGE RETRIEVAL WITH MA...

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IEEE International conference on image processing (ICIP)

作者： Zhong, Qian Chen, Ling Qian, Yuntao Zhejiang Univ Coll Comp Sci Hangzhou Peoples R China

ISBN: (纸本)9781728163956

Few-shot remote sensing image retrieval is devoted to add new retrieval categories with a small number of labeled samples, and simultaneously achieve favorable retrieval performance for new categories and keep the primary retrieval performance for the original categories as far as possible. Few-shot learning has received considerable attention, however its applications in image retrieval, especially for remote sensing image retrieval, are still very few. In this paper, we redefine the few-shot image retrieval problem formally and further propose a few-shot retrieval method under model-agnostic meta-learning (MAML) framework, combined with ResNet and GeM as feature extraction module. Moreover, the optimal mean average precision (mAP) is used as ranking loss for defining the loss function of learning model, and a histogram binning approximation of mAP, which is differential, is thus employed so that the whole few-shot retrieval model can be end-to-end trained. The retrieval effectiveness and efficiency of our method are verified on UC-Merced and AID data sets.

关键词： remote sensing image retrieval few-shot learning deep neural networks MAML

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Super-resolution of Satellite imagery Using a Wavelet Multiscale-based Deep Convolutional Neural Network Model 26

Super-resolution of Satellite Imagery Using a Wavelet Multis...

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conference on image and signal processing for remote sensing XXVI

作者： Aburaed, Nour Panthakkan, Alavikunhu Al-Saad, Mina El Rai, Marwa Chendeb Al Mansoori, Saeed Al-Ahmad, Hussain Marshall, Stephen Univ Dubai Dubai U Arab Emirates Mohammed Bin Rashid Space Ctr Dubai U Arab Emirates Univ Strathclyde Glasgow Lanark Scotland

ISBN: (纸本)9781510638808

Nowadays, satellite images are used in various governmental applications, such as urbanization and monitoring the environment. Spatial resolution is an element of crucial impact on the usage of remote sensing imagery. As such, increasing the spatial resolution of an image is an important pre-processing step that can improve the performance of various image processing tasks, such as segmentation. Once a satellite is launched, the more practical solution to improve the resolution of its captured images is to use Single image Super Resolution (SISR) techniques. In the recent years, Deep Convolutional Neural Networks (DCNNs) have been recognized as a highly effective tool to reconstruct a High Resolution (HR) image from its Low Resolution (LR) counterpart, which is an open problem due to the inherent difficulty of estimating the missing high frequency components. The aim of this research paper is to design and implement a satellite image SISR algorithm by estimating high frequency details through training Deep Convolutional Neural Network (DCNNs) with respect to wavelet analysis. The goal is to improve the spatial resolution of multispectral remote sensing images captured by DubaiSat-2 satellite. The accuracy of the proposed algorithm is assessed using several metrics such as Peak signal-to-Noise Ratio (PSNR), Wavelet-based signal-to-Noise Ratio (WSNR) and Structural Similarity Index Measurement (SSIM).

关键词： remote sensing Wavelet Transform Super Resolution Convolutional Neural Network Deep Learning

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A haze removal method based on additional depth information and image fusion 2nd

A haze removal method based on additional depth information ...

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2nd International conference on Sensor Networks and signal processing, SNSP 2019

作者： Tian, Tian Zhang, Bin Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9789811549168

To address the problem of image degradation in foggy days, we propose a haze removal method based on additional depth information and image fusion. With recent advances in depth-sensing technology, it has been realized that sensing devices can produce depth images in which the depth value are quite accurate. We adopt the depth estimation dataset of Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) which contains images collected from different real-world environments. The additional information includes the LiDaR scanning points and original depth images which can be used to estimate the optical depth of each point in the scene. In this paper, we investigate how to use additional depth information to remove haze for a single image. Our method focuses on LiDaR depth imaging, image fusion, and the atmospheric scattering model. We use LiDaR scanning points as input and then deduce a rough depth image with prominent features. The rough depth image is then combined with original depth image to improve reliability of depth estimation by image fusion. Using the atmospheric scattering model, we can remove haze for a single image. Experimental results show that our proposed approach provides better performance of dehazing under different fog conditions and holding the details of remote sensing images than current research methods. © Springer Nature Singapore Pte Ltd 2021.

关键词： image fusion

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Evaluating the Effect of Stage Removal on the Performance of Phase Gradient Autofocus (PGA) Algorithm

Evaluating the Effect of Stage Removal on the Performance of...

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Telecommunications conference (ITC-Egypt), International

作者： Ahmed ELBohy Ahmed Azouz Ahmed S. Mashaly Ashraf Khaled Helmy Electrical Engineering Branch Military Technical College Cairo Egypt National Authoritty for Remote Sensing & Space Sciences Cairo Egypt

Autofocus is a crucial step for Synthetic Aperture Radar (SAR) image formation. The choice of autofocus strategy is highly dependent on the compromise between SAR processing speediness and image quality demanded for trustworthy target analysis applications. The Phase Gradient Autofocus (PGA) algorithm for SAR phase error correction was released at the end of the 1980s. Since then, it has been evinced as a vigorous technique that can afford outstanding results over an assortment of SAR data not only on scene content but also for the hierarchy of the degrading phase error. Undeniably, PGA is adapted to restore defocused SAR images to their designed quality. The accomplishment of the algorithm is entrenched on four elemental signal processing steps, which comprise circular shifting, windowing, estimation of phase gradient, and lastly iterative correction. The question has been highlighted, which of these four elements is verily decisive to the efficacy of the algorithm. Breaking into the algorithm to dispense with one of these four steps reduces the computational cost and processing time while keeping algorithm performance and not affecting the image quality. The Breaking into the algorithm and the deep understanding of these four steps, the role of each part, and the importance of its implementation according to its arrangement within the algorithm is an attempt to reduce the computational complexity by reducing the steps of PGA which, may allow the creation of a customized algorithm to take the advantages of the outstanding PGA and overcome its slow implementation, This enables us to reduce the computational cost and processing time while keeping algorithm performance and not affecting the image quality using the algorithm in real time and without human intervention. Decomposition will be executed in three phases by masking one of the basic algorithm components in a cascaded manner to assess the effect on image quality, then elect which step may be eliminated while m

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You Only Look Once Based-C2fGhost Using Efficient SIoU Loss Function for Airplane Detection

You Only Look Once Based-C2fGhost Using Efficient SIoU Loss ...

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International conference on Frontiers of signal processing (ICFSP)

作者： Manh-Tuan Do Manh-Hung Ha Duc-Chinh Nguyen Oscal Tzyh-Chiang Chen Faculty of Applied Sciences International School Vietnam National University Hanoi Vietnam Department of Electrical Engineering National ChungCheng University & International School Vietnam National University

ISBN: (数字)9798350353235

ISBN: (纸本)9798350353242

Detecting airplanes in satellite imagery presents significant challenges due to the intricate backgrounds and varying conditions of data acquisition influenced by sensor geometry and atmospheric effects. Although deep learning algorithms are rapidly advancing, their primary application and evaluation have been on widely-used ground-based imagery. This research provides a comprehensive evaluation and comparison of several sophisticated object detection algorithms tailored for aircraft identification in satellite imagery. By utilizing the extensive HRPlanesV2 dataset alongside a stringent validation process on the GDIT dataset, we train a state-of-the-art object detection model using YOLOv10. Furthermore, this research proposes YOLOv10-C2fGhost, which improves the C2f module to C2f-Ghost, further reducing model complexity. Additionally, the replacement of the SIoU loss function with CIoU in YOLOv10s demonstrates significant improvements in accuracy and detection of small objects. On the GDIT dataset, experimental results show that, compared to the original YOLOv10s, the number of parameters was reduced from 8M to 6.6M, and GFLOPs decreased from 24.4 to 17.2, while maintaining a similar accuracy of 92.2%. Similarly, experiments on the HRPlanev2 dataset showed a significant reduction in model complexity and a high accuracy of over 97%.

关键词： YOLO Airplanes Accuracy image resolution Target recognition Military computing Atmospheric modeling Satellite images Complexity theory remote sensing

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FANM: Fuzzy-Aware Nested Mamba for Infrared Dense Small Target Detection

FANM: Fuzzy-Aware Nested Mamba for Infrared Dense Small Targ...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Yi Sui Xiyang Zhi Yuanxin Huang Xiaoyang Liang Zheng Lu Wei Zhang Research Center for Space Optical Engineering Harbin Institute of Technology Harbin China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The task of infrared dense small target detection aims to accurately locate densely distributed thermal radiation targets in complex scenes. However, in complex scenes, dense small targets often suffer from occlusion, shadows, and blurriness, leading to unclear detection bounding boxes and inaccurate localization. To address the aforementioned issues, this paper introduces an innovative approach for infrared dense small target detection, termed the Fuzzy-Aware Nested Mamba (FANM) network. Specifically, the network takes a single-frame infrared image as its input and leverages shallow, middle, and deep Mamba networks to fully exploit and integrate local and global multi-scale features. The extracted features are subsequently passed to a feature pyramid network (FPN) to balance the spatial resolution and semantic information of each feature map layer, ensuring that feature maps of different scales can be effectively utilized. Then, the output features at each level are fed into the detection head, which consists of two branches: one for regressing the positions of arbitrarily distributed bounding boxes, and the other for predicting the quality of the bounding boxes. The proposed fuzzy perceptual loss function is used to optimize these branches, ultimately outputting images with accurately detected target bounding boxes. Experiments were performed on the DMIST dataset, which includes images with varying numbers of targets, and the results validate the effectiveness of our method.

关键词： Location awareness Head Accuracy Uncertainty Semantics Object detection Feature extraction Data mining Spatial resolution Optimization

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Land Sustainability Evaluation Using remote sensing Techniques with Google Earth Engine

Land Sustainability Evaluation Using Remote Sensing Techniqu...

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Information Technology and Nanotechnology (ITNT), International conference on

作者： Komal Choudhary Alexander Kupriyanov Mukesh Singh Boori Ayrat Valiev Samara National Research University Samara Russia Kazan State Agrarian University Tatarstan Russia Department of Land Surveying and Geo-Informatics Smart Cities Research Institute The Hong Kong Polytechnic University Hong Kong China "Crystallography and Photonics" RAS Samara National Research University Image Processing Systems Institute of RAS - Branch of the FSRC Samara Russia GBAO University of Central Asia Khorog Tajikistan

Agricultural sustainability is essential in the moderate Republic of Tatarstan region to improve land agriculture production and preserve natural resources, which must adapt to the increasing population with the expansion of newly reclaimed land. The purpose of this study is to assess agricultural sustainability conditions and possibilities by assimilating economic viability and biophysical viability into a sustainable agricultural structure. In determining the suitability classes, topographic features such as slope, aspect, and elevation, as well as soil texture, pH, and soil depth, were applied as criteria. Weighted overlay analysis was applied to merge various thematic layers to find suitable areas for crop production. The Analytic Hierarchy Process (AHP) model is used to determine the weights for all features. The highest weights were acquired for elevation (0.201) and soil (0.223), and the lowest weights were calculated for aspect (0.045). The findings showed that assimilating the AHP method with GIS technology is a practical way to accurately analyze the suitability of land for the production of a particular crop and reduce the harmful environmental effects of farming methods. This research finding has implications for developing a management plan for sustainable agriculture, which will increase crop productivity in a sustainable way. It can assist in maintaining the ecosystem to enhance the potential for agricultural production and independent village economies throughout the district as well as the Tatarstan region.

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REGION OF INTEREST EXTRACTION BASED ON CO-SALIENCY ANALYSIS AND FEEDBACK STRATEGY FOR remote sensing imageS

REGION OF INTEREST EXTRACTION BASED ON CO-SALIENCY ANALYSIS ...

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IEEE International conference on image processing (ICIP)

作者： Zhang, Libao Liu, Yanan Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781728163956

Saliency analysis has been revealed an effective method to extract the region of interest (ROI) in remote sensing images. However, most existing saliency detection methods mainly focus on extracting the ROIs from a single image, which usually are not able to generate satisfactory results because of the background interference of remote sensing images. The employment of co-saliency detection which focuses on detecting common salient objects in a set of images can provide an effective solution to this issue. In this paper, we propose a novel ROI extraction model based on co-saliency analysis and feedback strategy for remote sensing images. We combine the bottom-up measures including consistency, central prior and contrast on spectral and texture feature of multiple images together, with subsequent adjusting operation by feedback strategy to enhance the common salient objects. Experiment results reveal that our model outperforms seven state-of-the-art models.

关键词： Object extraction region of interest inter-saliency intra-saliency feedback strategy

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ANALYSIS of DENOISING INTERPRETATION of remote sensing image BASED on ICA-WAVELET TRANSFORM

ANALYSIS of DENOISING INTERPRETATION of REMOTE SENSING IMAGE...

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2020 International conference on Geomatics in the Big Data Era, ICGBD 2020

作者： Sun, L. Gan, X.S. Beijing HeadSpring Technology Co.Ltd Beijing China China Transport Telecommunications and Information Center Beijing China

The noise will blur the key information of the remote sensing image, such as edge texture and important feature information, which will result in the loss of key information contained in the remote sensing image, resulting in the degradation of the overall quality of the image, which will bring difficulties to the interpretation work. Therefore, in order to obtain higher precision, signal-to-noise ratio and improve the quality of remote sensing image, denoising the remote sensing image containing noise is a crucial step and processing step for image remote sensing image *** this paper, the ICA wavelet analysis algorithm is applied to the application of real-time remote sensing image denoising. A series of pre-processing procedures such as control point correction, image fusion and image mosaic are carried out on the Asian sub-level remote sensing image, and the signal-to-noise ratio of the remote sensing image is adopted. (SNR/dB) and mean square error (RMSE) verify the image quality after denoising. © 2020 L. Sun.

关键词： image quality

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