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

作者： Ghosh, Arthita Ehrlich, Max Shah, Sohil Davis, Larry Chellappa, Rama Univ Maryland College Pk MD 20742 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present a semantic segmentation algorithm for RGB remote sensing images. Our method is based on the Dilated Stacked U-Nets architecture. This state-of-the-art method has been shown to have good performance in other applications. We perform additional post-processing by blending image tiles and degridding the result. Our method gives competitive results on the DeepGlobe dataset.

关键词： Semantics image segmentation remote sensing Convolution Training Satellites Computer architecture

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Detection of Archaeological Looting from Space: Methods, Achievements and Challenges

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remote sensing 2019年第20期11卷 2389-2389页

作者： Tapete, Deodato Cigna, Francesca Italian Space Agcy ASI Via Politecn Snc I-00133 Rome Italy

Illegal excavations in archaeological heritage sites (namely "looting") are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR);(ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature);(iii) image processing workflows;and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstrati

关键词： looting archaeological remote sensing change detection feature extraction pattern recognition SAR VHR optical WordView-2 Sentinel-2 COSMO-SkyMed

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Hyperspectral remote sensing image Analysis with SMACC and PPI Algorithms for Endmember Extraction 2nd

Hyperspectral Remote Sensing Image Analysis with SMACC and P...

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2nd International conference on Recent Trends in image processing and pattern recognition, RTIP2R 2018

作者： Nalawade, Dhananjay B. Solankar, Mahesh M. Surase, Rupali R. Varpe, Amarsinh B. Vibhute, Amol D. Dhumal, Rajesh K. Kale, Karbhari Geospatial Technology Research Laboratory Dr. Babasaheb Ambedkar Marathwada University AurangabadMaharashtra India Department of Computer Science and IT Dr. Babasaheb Ambedkar Marathwada University AurangabadMaharashtra India School of Computational Sciences Solapur University SolapurMS413255 India

ISBN: (纸本)9789811391804

The hyperspectral data endmember extraction plays a prominent role. In recent years, many researchers put their efforts to develop a new approach for endmember extraction from hyperspectral data. The endmember extraction algorithms especially used to discover the purest type of each spectrally distinct component on a scene. Endmember extraction process can be influenced by type of data, number of endmembers, number of pixels being processed, and number of spectral bands in data, used algorithms and also by the type of noise present in the data. The identified endmembers can be used in the further processing of identification and classification. Comparison of endmember extraction algorithms is a challenging task due to absence of unified criteria and unavailability of a standardized dataset to validate any new algorithm. Previously comparison of endmember extraction algorithm has been carried out on Landsat 4-5 data (Multispectral), Hyperion and the airborne visible/infrared imaging spectrometer (AVIRIS) Cuprite Hyperspectral dataset. In this paper, we have analyzed two widely used methods of endmember extraction the Sequential Maximum Angle Convex Cone (SMACC) and Pixel Purity Index (PPI) on AVIRIS-Next Generation (NG) hyperspectral dataset of Jhagadiya, Gujarat, Jasper Ridge and Hyperion dataset. From the experimental results, it is concluded that SMACC performs better than PPI. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Pixels

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Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in CNNs 40th

Inference, Learning and Attention Mechanisms that Exploit an...

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40th German conference on pattern recognition, GCPR 2018

作者： Hackel, Timo Usvyatsov, Mikhail Galliani, Silvano Wegner, Jan Dirk Schindler, Konrad Photogrammetry and Remote Sensing ETH Zürich Zürich Switzerland

ISBN: (纸本)9783030129385

While CNNs naturally lend themselves to densely sampled data, and sophisticated implementations are available, they lack the ability to efficiently process sparse data. In this work we introduce a suite of tools that exploit sparsity in both the feature maps and the filter weights, and thereby allow for significantly lower memory footprints and computation times than the conventional dense framework, when processing data with a high degree of sparsity. Our scheme provides (i) an efficient GPU implementation of a convolution layer based on direct, sparse convolution;(ii) a filter step within the convolution layer, which we call attention, that prevents fill-in, i.e., the tendency of convolution to rapidly decrease sparsity, and guarantees an upper bound on the computational resources;and (iii) an adaptation of back-propagation that makes it possible to combine our approach with standard learning frameworks, while still exploiting sparsity in the data and the model. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

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image Analysis by Structural Dissimilarity Estimation

Image Analysis by Structural Dissimilarity Estimation

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Adib Akl Charles Yaacoub School of Engineering Holy Spirit University of Kaslik (USEK) Jounieh Lebanon

image analysis using dissimilarity estimation methods is a crucial process in several image processing applications. For instance, it is used in frame reconstruction, pattern recognition and image inpainting. Dissimilarity estimation methods based on intensity information lead to impressive results with different image types, but failure cases still exist especially when dealing with local structural variations. In this paper, a metric for dissimilarity estimation, based on structural information, is proposed. Results show that using the proposed metric, local dissimilarities can be better estimated comparing to traditional intensity-based metrics, thus helping the analysis of images in remote sensing images.

关键词： Tensors Measurement Estimation remote sensing image analysis image edge detection

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A remote sensing image Registration Method Based on Multi-features

A Remote Sensing Image Registration Method Based on Multi-fe...

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International conference on image, Vision and Computing (ICIVC)

作者： Kun Xie Jinlong Chen Minghao Yang Guangxi Key Laboratory of Trusted Software of Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Cryptography and Information Security of Guilin University of Electronic Technology Guilin China Key Laboratory of Pattern Recognition Chinese academy of sciences Beijing China

ISBN: (纸本)9781728123264

remote sensing image registration technology has important significance in the field of image processing. The registration technology of SAR images has been a hot research in this field, and it is also a challenge. Aiming at this difficulty, this paper proposes a fusion feature algorithm, which combines gradient, grayscale, texture, geometry and other features in heterogeneous remote sensing images for comprehensive matching. Compared with the traditional single feature, the improved detector has good performance and registration accuracy in SAR image and visible light image registration.

关键词： remote sensing image registration Fusion features SAR images Visible light images remote sensing images Radar polarimetry Registration image registration registration accuracy images Rescue Synthetic aperture radar Specific absorption rate SAFETY ANALYSIS REPORTS Fields

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image Segmentation Algorithm for feature recognition in Satellite imageries – A case Study

Image Segmentation Algorithm for feature recognition in Sate...

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International conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)

作者： Sheena A D C. Udhaya Kumar Department of Civil Engineering Anna University Chennai India

ISBN: (纸本)9781538694831

remote sensing and satellite information's are very useful for the geospatial analysis of surface and subsurface features. In the modern day-to-day scenario, the landforms and its features to be monitored and managed is a very challenging one and remote sensing plays a crucial role in the society. Analysis of Satellite imageries using geospatial techniques is playing a vibrant role in the earth science system. Earth contains rivers, streams, tanks, water bodies as natural formations and delineating the features itself with the image segmentation algorithm is an elegant task over remote sensing imagery. Feature extraction and image segmentation are extensively used in the field of medicine for X-ray and scan images, satellite imaging, pattern identification, features and face interpretation and various applications of engineering.

关键词： feature extraction geographic information systems geophysical image processing hydrological techniques image segmentation remote sensing Feature extraction hydrological techniques remote sensing image segmentation image segmentation algorithm Satellite imagery geographic information systems Case studies geospatial analysis

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International conference on Intelligent Computing, Communication and Devices, ICCD 2017

International Conference on Intelligent Computing, Communica...

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International conference on Intelligent Computing, Communication and Devices, ICCD 2017

ISBN: (纸本)9789811089435

The proceedings contain 147 papers. The special focus in this conference is on . The topics include: An indoor personnel positioning method based on spectrum;research on fault diagnosis based on test resource virtualization;solving airport gate assignment problem using an improved genetic algorithm with dynamic topology;sound detection and alarm system of unmanned aerial vehicle;Landscape pattern recognition on water quality protection in an urbanizing delta using remote sensing and GIS techniques;map building for a kind of robot’s structural environment;a study on hair density analysis for androgenic hair pattern of low-resolution leg images;an efficient algorithm based on resource regulatory network to predict potential safety hazards;The software quality prediction model based on DBN;Research on DOS attack effect evaluation technology;research and optimization of data sparsity in collaborative filtering algorithms;analyses on requirements of information and communication standard for global energy interconnection;a simplified prediction method of IoT service response time;real-time and distributed anomalies detection architecture and implementation with structured streaming;a distributed complex event processing system based on publish/subscribe;an event-driven multi-process collaborative interaction platform for internet of things;the impact of I-O structure on China’s macroeconomy;coordinated production and distribution scheduling in flowshop with limited waiting times;remove the confusion and speed up the construction of battlefield cyber warfare force;study on online learning intervention based on theory of constraints;LHR: Using LDA helps ranking;a robust image segmentation approach using fuzzy C-means clustering with local coefficient of variation.

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Research on False Alarm Removal Method for Aircraft Target Detection under Small Sample Conditions 4

Research on False Alarm Removal Method for Aircraft Target D...

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4th IEEE International conference on image, Vision and Computing, ICIVC 2019

作者： Zeng, Qinghao Chen, Jinlong Yang, Minghao University of Electronic Technology Guangxi Key Laboratory of Trusted Software of Guilin Guilin541004 China University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security of Guilin Guilin541004 China Chinese Academy of Sciences Key Laboratory of Pattern Recognition Institute of Automation Beijing100190 China

ISBN: (纸本)9781728123257

Aiming at the aircraft target in visible light remote sensing image, this paper proposes a false alarm removal method for target detection under small sample training conditions. First, use data enhancement methods for small sample data to increase the number of samples and improve the generalization ability of features. Then, according to the clear outline of the aircraft target and the obvious characteristics of the edge features, the Haar feature plus AdaBoost algorithm is used to preliminary detect the aircraft target. Finally, the convolutional neural network is used to extract the target features, and the preliminary detection results are classified to remove the false alarm targets and improve the accuracy of the detection. The experimental results show that the detection effect is good in aircraft target detection. © 2019 IEEE.

关键词： Errors

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Research on a Method of Airport Runway Detection Algorithm 4

Research on a Method of Airport Runway Detection Algorithm

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4th IEEE International conference on image, Vision and Computing, ICIVC 2019

作者： Xie, Kun Chen, Jinlong Yang, Minghao University of Electronic Technology Guangxi Key Laboratory of Trusted Software of Guilin Guilin541004 China University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security of Guilin Guilin541004 China Chinese Academy of Sciences Key Laboratory of Pattern Recognition Institute of Automation Beijing100190 China

ISBN: (纸本)9781728123257

In view of the long and narrow characteristics of the airport runway function area, this paper proposes a method combining the Haar -like feature and the spherical neural network to detect the airport runway in remote sensing images. The algorithm first uses Haar-like to extract the target features and trains the strong classifier, and then further uses a spherical neural network model to eliminate false alarms. The experimental results show that the algorithm achieves good accuracy in detecting airport runways. © 2019 IEEE.

关键词： Signal detection

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