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Wild Animal Detection using a Machine Learning Approach and Alerting using LoRa Communication

Wild Animal Detection using a Machine Learning Approach and ...

引用

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

作者： Gnaneshwar Bandari Prof. L Nirmala Devi P Srividya ECE Dept. ME - Systems and Signal Processing University College of Engineering Osmania University Hyderabad India ECE Department University College of Engineering Osmania University Hyderabad ECE Department University College of Engineering Osmania University Hyderabad India

It is crucial to effectively and consistently monitor wild animals in the vicinity of the forest boundaries. In this paper, an algorithm is demonstrated to identify wild animals in order to protect them. Since there are so many different kinds of animals, manually recognizing them might be challenging. To better effectively monitor animals, this system categorizes them based on their images. Animal monitoring, theft prevention, and animal-vehicle accident prevention can all be aided by animal detection and classification. Applying efficient deep learning techniques can help with this. When an animal is detected, an alert will be sent to another device through LoRa communication because standard communications like WiFi and GSM technologies may not be available in the remote sensing area. Computer vision using Python is utilized for image processing. Raspberry Pi development board is used where the model is deployed and live streaming is processed to detect a wild animal which will differentiate between Humans and Domestic animals. Once, a wild animal is detected an alert is sent through LoRa communication to an end-user who is monitoring the wildlife.

关键词： GSM Animals image processing Wildlife Forestry Streaming media Monitoring

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Drone-Based Car Counting via Density Map Learning

Drone-Based Car Counting via Density Map Learning

引用

IEEE International conference on Visual Communications and image processing (VCIP)

作者： Huang, Jingxian Ding, Guanchen Guo, Yujia Yang, Daiqin Wang, Sihan Wang, Tao Zhang, Yunfei Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Tencent Inc Shenzhen 518052 Peoples R China

ISBN: (纸本)9781728180687

Car counting on drone-based images is a challenging task in computer vision. Most advanced methods for counting are based on density maps. Usually, density maps are first generated by convolving ground truth point maps with a Gaussian kernel for later model learning (generation). Then, the counting network learns to predict density maps from input images (estimation). Most studies focus on the estimation problem while overlooking the generation problem. In this paper, a training framework is proposed to generate density maps by learning and train generation and estimation subnetworks jointly. Experiments demonstrate that our method outperforms other density map-based methods and shows the best performance on drone-based car counting.

关键词： Car counting density map learning drone-based image

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A DWT-SVDD Anomaly Detection Algorithm Using Hyperspectral remote sensing in Marine Search and Rescue

A DWT-SVDD Anomaly Detection Algorithm Using Hyperspectral R...

引用

IEEE International conference on signal and image processing (ICSIP)

作者： Can Cui Jin Xu Civil Aviation Institute Shenyang Aerospace University Shenyang China Maritime Institute Guangdong Ocean University Zhanjiang China

ISBN: (纸本)9781665446006

In recent years, air crashes have occurred frequently, which caused huge losses to life and property. Timely and effective anomaly detection is the premise in marine rescue and rescue (MSAR). The paper took advantages of hyperspectral remote sensing with multidimension bands’ feature, to realize air crash debris anomaly detection, from pixel to subpixel. A DWT-SVDD algorithm was proposed. First a wavelet based feature extraction was carried out, to mine multi-level features of images. And then a support vector data description was proposed to detect the anomalies. In this process, it is necessary to add anomalies manually so as to optimize parameters by particle swarm optimization. The contribution of this paper was to propose an anomaly detection algorithm based on wavelet packet decomposition and support vector description. In the selection of training samples, the background residual data was used to extract automatically.

关键词： Training Support vector machines Feature extraction Computer crashes Wavelet packets Discrete wavelet transforms Kernel

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A Novel Deep Transfer Learning Method for Airborne remote sensing Semantic Segmentation Based on Fully Convolutional Network 4

A Novel Deep Transfer Learning Method for Airborne Remote Se...

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4th International conference on Imaging, signal processing and Communications, ICISPC 2020

作者： Liu, Yanjuan Kong, Yingying Zhang, Bowen Peng, Xiangyang Leung, Henry Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing China Ece Nanjing Research Institute of Electronics Engineering Nanjing China

ISBN: (纸本)9781728185484

Semantic segmentation for airborne remote sensing images is a critical process in the workflow of object-based image analysis. The success of deep neural networks for semantic segmentation heavily rely on large-scale and well-labeled datasets, which are hard to collect in practice. In this paper, we consider transfer learning for semantic segmentation that can address the cross-domain learning problem by extracting useful information from data of related domain and transferring them to target domain. Considering the high similarity between Unmanned Aerial Vehicles (UAV) images and airborne remote sensing images, we propose a deep novel transfer learning method, which transfers a semantically segment model used UAV images to airborne remote sensing images. The experimental results show that the method proposed achieves higher Global Accuracy (GA) with less running time compared other transfer algorithm. © 2020 IEEE.

关键词： Semantic Segmentation

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SINGLE image SUPER-RESOLUTION USING DEPTH MAP AS CONSTRAINT

SINGLE IMAGE SUPER-RESOLUTION USING DEPTH MAP AS CONSTRAINT

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IEEE International conference on image processing (ICIP)

作者： Shi, Haitao Jiang, Jiaqin Yao, Jian Xu, Zheyuan Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Hubei Peoples R China Shenzhen Jimuyida Technol Co Ltd Shenzhen Peoples R China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163956

Single image super-resolution based on the deep neural network has achieved great performance recently, but generating photo-realistic images remains a challenging problem. To tackle this issue, we propose a method that uses depth maps as a constraint to get better visual quality. Specifically, we propose a self-adaptive feature transform (AFT) layer, which can perform affine transformation on the feature map based on the depth map to constrain the plausible solution space of the SR image. Furthermore, we propose a hierarchical residual multi-scale fusion block to improve the representational ability of the network. Experimental results on benchmark datasets demonstrate that our method is superior to other perceptually-oriented SISR methods in terms of visual quality and also achieves state-of-the-art performance on quantitative metrics.

关键词： super-resolution depth map feature transform weight map

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NO-REFERENCE VIDEO QUALITY ASSESSMENT BASED ON SIMILARITY MAP ESTIMATION

NO-REFERENCE VIDEO QUALITY ASSESSMENT BASED ON SIMILARITY MA...

引用

IEEE International conference on image processing (ICIP)

作者： Wu, Wei Liu, Zizheng Chen, Zhenzhong Liu, Shan Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Tencent Media Lab Palo Alto CA USA

ISBN: (纸本)9781728163956

One of the key challenges in no-reference video quality assessment (NR-VQA) is the absence of the reference video to measure the similarity or difference between the distorted video and the original one. In this paper, an encoder-decoder model is proposed to predict pixel-by-pixel similarity maps from the distorted video. The model takes multiple frames as input since correlated pixels of adjacent frames can be exploited to recover the similarity map of the middle frame of the distorted video clip. In addition, to further exploit the temporal perception mechanism of the human visual system (HVS), which is relevant to the perceptual video distortion measurement, visual persistence and temporal memory effects are considered in the spatio-temporal pooling network design. Experimental results demonstrate that our proposed method outperforms state-of-the-art NR-VQA metrics.

关键词： video quality assessment encoder-decoder similarity map spatio-temporal pooling

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Next updates of atmospheric correction processor Sen2Cor 26

Next updates of atmospheric correction processor Sen2Cor

引用

conference on image and signal processing for remote sensing XXVI

作者： Pflug, Bringfried Louis, Jerome Debaecker, Vincent Mueller-Wilm, Uwe Quang, Carine Gascon, Ferran Boccia, Valentina German Aerosp Ctr Remote Sensing Technol Inst Rutherfordstr 2 D-12489 Berlin Germany Telespazio France 26 Av JF Champollion F-31023 Toulouse France TPZV D Telespazio Vega Deutschland Darmstadt Germany CS Grp Toulouse France European Space Agcy ESRIN Frascati Italy

ISBN: (纸本)9781510638808

The Sentinel-2 mission is dedicated to land monitoring, emergency management and security. It serves for monitoring of land-cover change and biophysical variables related to agriculture and forestry. The mission is also used to monitor coastal and inland waters and is useful for risk and disaster mapping. The Sentinel-2 mission is fully operating since June 2017 with a constellation of two polar orbiting satellite units. Both Sentinel-2A and Sentinel-2B are equipped with an optical imaging sensor MSI (Multi-Spectral Instrument) which acquires optical data products with spatial resolution up to 10 m. Accurate atmospheric correction of satellite observations is a precondition for the development and delivery of high quality applications. Therefore the atmospheric correction processor Sen2Cor was developed with the objective of delivering land surface reflectance products. Sen2Cor is designed to process monotemporal single tile Level-1C products, providing Level-2A surface (Bottom-of-Atmosphere) reflectance product together with Aerosol Optical Thickness (AOT), Water Vapour (WV) estimation maps and a Scene Classification (SCL) map for further processing. The paper will give an overview of the Level-2A product content and up-to-date information about the data quality of the Level-2A products generated with Sen2Cor 2.8 in terms of Cloud Screening and Atmospheric Correction. In addition the paper gives an outlook on the next updates of Sen2Cor and their impact on Level-2A Data Quality.

关键词： atmospheric correction cloud masking Sentinel-2 Sen2Cor performance assessment data quality Level-2A product

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Human-computer Interactive System Development for Cloud Removal from Single images 7

Human-computer Interactive System Development for Cloud Remo...

引用

conference on Optical Metrology and Inspection for Industrial Applications VII

作者： Wu, Feng Zhu, Xifang Xiang, Ruxi Xu, Qingquan Xu, Yang Sun, Qingcheng Changzhou Inst Technol Sch Elect & Informat Engn 666 Liaohe Rd Changzhou 213032 Jiangsu Peoples R China

ISBN: (数字)9781510639201

ISBN: (纸本)9781510639201

remote sensing imaging is disturbed by clouds frequently and produces images with low contrast and poor resolution. Algorithms removing clouds from single remote sensing images are expected to improve their qualities and aroused worldwide interests. Compared with multi-image methods, cloud removal algorithms from single images reduce the demands for weather and improve the image collecting flexibility. In this paper, a human-computer interactive system for cloud removal from single images was developed according to the proposed cloud removal algorithm. Firstly, the distributions of clouds and sceneries in the contaminated remote sensing images were discussed. Features of the dual tree complex wavelet transform (DTCWT) to split image frequencies were investigated after the principle of DTCWT was analyzed. By combining the frequency features of DTCWT sub-bands and the frequency distributions of the remote sensing images, the cloud removal algorithm based on DTCWT was proposed, and its procedures were described completed. Then, a human-computer interactive system for cloud removal was developed in order to improve the cloud removal processing conveniences and achieve the best performance. The development schemes were introduced. The system structures were illustrated. The design methods of the camera driver and video signal output program were described. Communication streams between modules and human-computer interaction interfaces were designed further according to the cloud removal algorithm based on DTCWT. The system has a very good flexibility which permits the input of most parameters from users and is expect to achieve a best performance. Finally, the device of the system was completed and tested. image acquisition and cloud removal processing experiments were implemented by applying the human-computer interactive system. Cloud removal effects by adjusting parameters through the human-computer interaction interfaces were compared and evaluated. It proved that th

关键词： cloud removal dual tree complex wavelet transform human-computer interaction remote sensing image

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引用

IEEE International conference on Acoustics, Speech and signal processing

作者： Zhinan Cai Zhiyu Jiang Yuan Yuan School of Computer Science and School of Artificial Intelligence Optics and Electronics (iOPEN) Northwestern Polytechnical University Xi’an P. R. China

Change detection for remote sensing images is widely applied for urban change detection, disaster assessment and other fields. However, most of the existing CNN-based change detection methods still suffer from the problem of inadequate pseudo-changes suppression and insufficient feature representation. In this work, an unsupervised change detection method based on Task-related Self-supervised Learning Change Detection network with smooth mechanism(TSLCD) is proposed to eliminate it. The main contributions include: (1) the task-related self-supervised learning module is introduced to extract spatial features more effectively. (2) a hard-sample-mining loss function is applied to pay more attention to the hard-to-classify samples. (3) a smooth mechanism is utilized to remove some of pseudo-changes and noise. Experiments on four remote sensing change detection datasets reveal that the proposed TSLCD method achieves the state-of-the-art for change detection task.

关键词： conferences signal processing Feature extraction Acoustics Sensors Task analysis Speech processing

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DOVE: Decomposition Oriented Video super-rEsolution

DOVE: Decomposition Oriented Video super-rEsolution

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IEEE International conference on Visual Communications and image processing (VCIP)

作者： Wang, Huairui Sun, Wanjie Chen, Zhenzhong Yang, Daiqin Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China

ISBN: (纸本)9781728180687

Video super-resolution (VSR) has attracted a lot of attention that converts a low resolution (LR) video into a high resolution (HR) one. The original LR video is typically produced either by the downscaling processing or low-resolution sensor. Considering that the resolution degradation or limitation makes different impacts on different low-frequency (LF) and high-frequency (HF) components of the LR video signal, we propose a Decomposition Oriented Video super-rEsolution (DOVE) method in this paper. More specifically, a three-stream VSR network is designed in which the proposed LF and HF stream is responsible for modeling LF and HF components in the feature space. Moreover, a multi-frame refinement stream takes features of coarsely aligned frames as input and generates finely aligned counterparts progressively to guide the learning of LF and HF streams at the intermediate feature level. Furthermore, non-local channel attention is devised to capture long-range dependencies on a global scale both in the channel domain. Experimental results indicate that separating the learning of LF and HF components helps better estimate the HR frame from LR frames and superior VSR performance is achieved when compared with that of recent state-of-the-art methods.

关键词： video super-resolution feature decomposition deformable convolution non-local attention

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