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remote sensing image Classification Methods Based on CNN:Challenge and Trends

Remote Sensing Image Classification Methods Based on CNN:Cha...

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2021 International conference on signal processing and Machine Learning (CONF-SPML)

作者： Liyao Yuan Beijing University of Posts and Telecommunications

ISBN: (纸本)9781665417358

remote sensing image classification occupies a vital place in earth observation and has many applications in military and civil *** can be divided into two typical tasks:high-resolution remote sensing images and hyperspectral image ***,high-resolution remote sensing and hyperspectral image classification cannot facilitate all features and achieve good accuracy with traditional *** deep learning methods,especially the convolutional neural networks(CNN),are developing rapidly,image classification methods based on CNN can perform well and provide new ideas for remote sensing *** this paper,we first review the background of typical remote sensing images and ***,we provide an overview of the development of the CNN *** that,we point out some existing problems that we need to overcome for the CNN ***,the corresponding solutions are provided,and future work is presented with the analysis of some popular methods.

关键词： Convolutional neural networks hyperspectral image high-resolution remote sensing image image classification

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Chest CT image Super Resolution using Deep Learning Network Models

Chest CT Image Super Resolution using Deep Learning Network ...

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2021 International conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021

作者： Rajeshwari, P. Shyamala, K. Anurag University Department of CSE Ghatkesar India Osmania University College of Engineering Deptarment of CSE Hyderabad India

ISBN: (纸本)9781665425032

image Super Resolution is a process of converting a Low Resolution image into High Resolution image with better visual quality and improved details. It is one of the most popular technique in image processing and computer vision. Super Resolution is applied in wide range of real world applications like medical imaging, surveillance applications and reconstruction of high quality remote sensing images. In the recent years, Super Resolution using deep learning techniques significantly improved accuracy. In this paper, Implemented four deep learning network models to generate High Resolution images of chest CT scans and evaluated the quantitative measure Peak signal-to-Noise Ratio to compare accuracy of each CNN based deep leaning network models such as Super-Resolution Convolutional Neural Network (SRCNN), Enhanced Deep Super-Resolution (EDSR), Very Deep Super-Resolution (VDSR) and Deep Recursive Convolutional Network (DRCN). © 2021 IEEE.

关键词： Medical imaging

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Estimation of flooded areas during the El Niño Costero 2017 event using multisensor satellite data. Case Study: Lower Piura watershed (Peru)

Estimation of flooded areas during the El Niño Costero 2017...

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2021 IEEE International conference on Aerospace and signal processing, INCAS 2021

作者： Quintana-Ortiz, Jesus M. Santillan, Rodolfo Domingo Moreno Pasapera-Gonzalez, Jose J. Lima Peru

ISBN: (纸本)9781665405737

Estimation of flooded areas is a crucial parameter for disaster impact assessment, as it can be an indicator of the degree of economic loss and damage to structures. It is also an indicator of spatial persistence and the evolution of flood events. Therefore, it helps to better understand hydrological conditions and surface water availability and also provides valuable information for post-disaster land use planning. This work proposes a methodology to estimate flooded areas with the use of multitemporal flood extension masks on which the procedure is based. To guarantee a sufficiently high frequency of observation of the flooded areas, data from the Peruvian Satellite System (PeruSAT-1, SPOT, KompSAT-3A) and free access (Sentinel-1, Landsat-8) are analyzed. Satellite image processing and analysis is carried out with an integrated system of dedicated process chains for delimiting the extent of flooding from the aforementioned range of sensors. The effectiveness of the proposed method was demonstrated in the 2017 flood, due to the ElNiño phenomenon, in the northern region of Peru. © 2021 IEEE.

关键词： Floods optical satellite PeruSAT-1 remote sensing SAR satellite

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Research on the color deviation detection for the satellite remote sensing image

Research on the color deviation detection for the satellite ...

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International conference on Optical Instruments and Technology - Optoelectronic Imaging/Spectroscopy and signal processing Technology

作者： Tao Dongxing Zhang Chao Bi Yanqiang Shang Yonghong Wang Jing Yin Zhongke Beijing Inst Spacecraft Environm Engn Beijing 100094 Peoples R China Beijing Inst Remote Sensing Informat Beijing 100192 Peoples R China

ISBN: (纸本)9781510636552

The color deviation, which refers to the different between the obtained image and the image under the standard light, is from the light source and reflection characteristic of the object. For satellite remote sensing, the length of the radiation transmission path is different for imaging of different latitudes, especially considering the effects of the atmosphere. Therefore, imaging for high latitude in the winter maybe brings some color deviation into the data. There have some color deviation detection methods for digital camera photo, but they are not fit for the remote sensing data, because of the large image range. In the work, a color deviation detection method for the satellite remote sensing image is developed. And the method is validated using the Landsat 8 images obtained in the winter and summer respectively.

关键词： remote sensing image Color deviation detection Two-dimensional histograms algorithm

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Object Recognition and Detection in remote sensing images: A Comparative Study

Object Recognition and Detection in Remote Sensing Images: A...

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International conference on Artificial Intelligence and signal processing (AISP)

作者： Fatima, Syed Aley Kumar, Ashwani Pratap, Ajay Raoof, Syed Saba Vardhaman Coll Engn Comp Sci & Engn Hyderabad 501218 India AIIT Member Secretary Res Cell Comp Sci & Engn Lucknow Campus Lucknow Uttar Pradesh India

ISBN: (纸本)9781728144580;9781728144566

In this paper, we have provided a brief literature survey of object recognition & detection in remote sensing. These scene recognition images have more components and more challenging issues in the range of aerial image resolutions. It also shows a critical act as a limited area based on functions. The object detection in optical remote sensing has the best critical part of over supposing conditions of characteristic against affecting a model inputs. Certain moving characteristic act as a set of quality to define a based on personal action. This paper provides a brief summary of different object detection in remote sensing images and also discuss about their strength and limitation. The main focus of this review paper is on the satellite image. A subsists in patent of scene recognition of current model act on a freshly process. Inter relates to the land uses analysis system, other model analysis process as well as turn to the appropriate function. We have also discussed the problems and advancement of current scenario and give three research directions for deep learning for medical image recognition, image classification, and health care. We ensure this review article will provide adequate directions and scope for the betterment of the research community in the field of object recognition & detection in remote sensing.

关键词： Object Recognition Object Detection remote sensing Scene Recognition Deep learning

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15th International conference on ICT Innovations, ICT Innovations 2023

15th International Conference on ICT Innovations, ICT Innova...

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15th International conference on ICT Innovations, ICT Innovations 2023

The proceedings contain 17 papers. The special focus in this conference is on ICT Innovations. The topics include: A Review of Dew and Edge Computing: Two Sides of a Modern Internet of Things ...

ISBN: (纸本)9783031543203

The proceedings contain 17 papers. The special focus in this conference is on ICT Innovations. The topics include: A Review of Dew and Edge Computing: Two Sides of a Modern Internet of Things Solution;implementation of the Time Series and the Convolutional Vision Transformers for Biological signal processing - Blood Pressure Estimation from Photoplethysmogram;graph Neural Networks for Antisocial Behavior Detection on Twitter;image Classification Using Deep Neural Networks and Persistent Homology;Enhancing Knee Meniscus Damage Prediction from MRI images with Machine Learning and Deep Learning Techniques;multiplex Collaboration Network of the Faculty of Computer Science and Engineering in Skopje;the Curious Case of Randomness in Deep Learning Models for Heartbeat Classification;preface;understanding Worldwide Natural Gas Trade Flow for 2017 to 2022: A Network-Based Approach;identifying Drug - Disease Interactions Through Link Prediction in Heterogeneous Graphs;disaster-Resilient Messaging Using Dew Computing;semantic Segmentation of remote sensing images: Definition, Methods, Datasets and Applications;extracting Entities and Relations in Analyst Stock Ratings News;on the Construction of Associative and Commutative Bilinear Multivariate Quadratic Quasigroups of Order 2n;introducing Probabilities in Networks of Polarized Splicing Processors.

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Hardware Implementation of Convolutional Neural Network-Based remote sensing image Classification Method

Hardware Implementation of Convolutional Neural Network-Base...

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7th International conference on Communications, signal processing, and Systems (CSPS)

作者： Chen, Lei Wei, Xin Liu, Wenchao Chen, He Chen, Liang Beijing Inst Technol Sch Informat & Elect Radar Res Lab Beijing 100081 Peoples R China Beijing Inst Technol Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China

ISBN: (纸本)9789811365041;9789811365034

The convolutional neural networks have achieved very good results in the field of remote sensing image classification and recognition. However, the cost of huge computational complexity with the significant accuracy improvement of CNNs makes a huge challenge to hardware implementation. A promising solution is FPGA due to it supports parallel computing with low power consumption. In this paper, LeNet-5-based remote sensing image classification method is implemented on FPGA. The test images with a size of 126 x 126 are transformed to the system from PC by serial port. The classification accuracy is 98.18% tested on the designed system, which is the same as that on PC. In the term of efficiency, the designed system runs 2.29 ms per image, which satisfies the real-time requirements.

关键词： CNN remote sensing image FPGA Classification

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Monitoring of Leaf Nitrogen Content in A Citrus Orchard by Landsat 8 OLI imagery 12

Monitoring of Leaf Nitrogen Content in A Citrus Orchard by L...

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12th International conference on signal processing Systems

作者： Liu, Lingjie Li, Yong Wu, Tong Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing 210098 Peoples R China

ISBN: (纸本)9781510642768;9781510642751

Nitrogen is an essential nutrient for citrus growth. Thus, the chemical analysis of leaf tissues is needed to determine nitrogen in the traditional agronomic method, which is time consuming, labor intensive, and costly. Satellite remote sensing (RS) can quickly acquire multispectral images of large-scale orchards and thus can support low-cost and periodic monitoring of nitrogen content in orchards. RS data have been widely used for the monitoring of nitrogen content in various crops and performed quite well in related researches. However, few studies have been conducted to evaluate the leaf nitrogen content (LNC) of citrus on the basis of the data acquired by satellite RS. In this study, Landsat 8 RS image data are used to estimate the distribution of LNC in an orchard, and the effectiveness of different estimation methods for monitoring LNC value is studied. Linear regression, partial least square regression (PLSR), support vector regression (SVR), random forest regression (RF), and deep neural network (DNN) models are constructed and compared. Experimental results demonstrate the feasibility of using satellite RS data in determining LNC in sugar citrus. In terms of evaluating LNC, the PLSR algorithm outperforms other algorithms in testing data, reaching a determination coefficient of 0.864, a root mean squared error of 1.217, and a mean relation error of 3.5%. An accurate spatial distribution of nitrogen content in an orchard can be obtained by our model, which can be used to provide powerful support for the practical management and operation of the orchard.

关键词： Leaf nitrogen content Landsat8 OLI remote sensing citrus orchard machine learning

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A Novel Framework for Satellite image Dehazing Using Advanced Computational Techniques

A Novel Framework for Satellite Image Dehazing Using Advance...

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Innovation in Technology (ASIANCON), Asian conference on

作者： Sandeep Vishwakarma Anuradha Deepika Punj J.C. Bose University of Science and Technology Faridabad India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Satellite imagery information is used in many different ways; some of the more common ones are environmental and urban planning and disaster response. That is why haze has a highly negative impact on the quality and visibility of such images and their further usage. The following paper proposes an innovative approach to satellite images dehazing to enhance its value to professionals and researchers in the field. The ideas for the proposed method involve incorporating the use of machine learning algorithms into image enhancement procedures to adequately dehaze the images, increase image resolution, and enhance the efficiency of getting information from satellite images. Our approach involves a three-step process: seen as consisting of preprocessing, a dehazing network, and postprocessing. The preliminary processing generally includes initial enhancement operations such as histogram equalization and color stretch. The foundation of our framework is a deep learning-based dehazing network that uses CNNs, residual blocks, attention mechanisms, and GANs to dehaze and restore the images’ quality. Additional operations are performed in postprocessing; they include details enlargement, the removal of noise , and color correction so as to achieve natural and quality results. We evaluate our framework using two standard satellite image datasets: The users of the mentioned services are SateHaze1k and RICE. The effectiveness of the proposed method is evaluated by means of two quality measures, namely Peak signal-to-Noise Ratio and Structural Similarity Index. Test results portray the efficiency of the proposed technique in dehazing with enhanced PSNR and SSIM values over other conventional and state-of-art works on machine learning-based dehazing methods. That is why our framework can provide the workable and scalable solution for the improvement of the satellite images’ clarity that would be the beneficial for its usage in numerous applications.

关键词： Technological innovation image resolution Satellites image color analysis Urban planning Stability criteria Satellite images Colored noise Standards remote sensing

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Efficient destriping of remote sensing images using an oriented super-Gaussian filter 26

Efficient destriping of remote sensing images using an orien...

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conference on image and signal processing for remote sensing XXVI

作者： Lloyd, David T. Bouali, Marouan Abela, Aaron Farrugia, Reuben Valentino, Gianluca Univ Malta Dept Commun & Comp Engn MSD-2080 Msida Malta Univ Sao Paulo Inst Oceanog Sao Paulo Brazil

ISBN: (纸本)9781510638808

Satellite imagery provides information crucial for remote sensing applications. However, the images themselves can suffer from systematic and random artefacts which reduce the utility and accuracy of datasets. In particular, radiometric miscalibration due to temporal variation of the detector response may result in stripe noise. We report a method for suppressing striping in remote sensing images by use of a Fourier filter shaped like a super-Gaussian function. In comparison to both established 'traditional' and deep-learning-based destriping techniques, our method demonstrates superior destriping performance for both remote sensing images with native striping as well as those with stripes added to them. Our method simultaneously meets the three criteria of fidelity, speed and flexibility, enabling an efficient improvement in the radiometric accuracy of images from a wide range of satellite sources.

关键词： destriping denoising filtering calibration

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