scholarly journals Performance Analysis in the Segmentation of urban asphalted roads in RGB satellite images using K-Means++ and SegNet

2021 ◽  
Vol 24 (68) ◽  
pp. 89-103
Author(s):  
João Batista Pacheco Junior ◽  
Henrique Mariano Costa do Amaral
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Real Life ◽  
The Other ◽  
Computational Tool ◽  
Average Accuracy ◽  
Proposal A ◽  
Rgb Images ◽  
Associated Data

The design and manual insertion of new terrestrial roads into geographic databases is a frequent activity in geoprocessing and their demand usually occurs as the most up-to-date satellite imagery of the territory is acquired. Continually, new urban and rural occupations emerge, for which specific vector geometries need to be designed to characterize the cartographic inputs and accommodate the relevant associated data. Therefore, it is convenient to develop a computational tool that, with the help of artificial intelligence, automates what is possible in this respect, since manual editing depends on the limits of user agility, and does it in images that are usually easy and free to access. To test the feasibility of this proposal, a database of RGB images containing asphalted urban roads is presented to the K-Means++ algorithm and the SegNet Convolutional Neural Network, and the performance of each was evaluated and compared for accuracy and IoU of road identification. Under the conditions of the experiment, K-Means++ achieved poor and unviable results for use in a real-life application involving tarmac detection in RGB satellite images, with average accuracy ranging from 41.67% to 64.19% and average IoU of 12.30% to 16.16%, depending on the preprocessing strategy used. On the other hand, the SegNet Convolutional Neural Network proved to be appropriate for precision applications not sensitive to discontinuities, achieving an average accuracy of 87.12% and an average IoU of 71.93%.

Classification of EgyptSat-1 Images Using Deep Learning Methods

2020 ◽  
Vol 10 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Hatem Keshk ◽  
Xu-Cheng Yin
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Performance ◽  
Satellite Images ◽  
Satellite Image ◽  
Aerial Photographs ◽  
Average Accuracy

Background: Deep Learning (DL) neural network methods have become a hotspot subject of research in the remote sensing field. Classification of aerial satellite images depends on spectral content, which is a challenging topic in remote sensing. Objective: With the aim to accomplish a high performance and accuracy of Egyptsat-1 satellite image classification, the use of the Convolutional Neural Network (CNN) is raised in this paper because CNN is considered a leading deep learning method. CNN is developed to classify aerial photographs into land cover classes such as urban, vegetation, desert, water bodies, soil, roads, etc. In our work, a comparison between MAXIMUM Likelihood (ML) which represents the traditional supervised classification methods and CNN method is conducted. Conclusion: This research finds that CNN outperforms ML by 9%. The convolutional neural network has better classification result, which reached 92.25% as its average accuracy. Also, the experiments showed that the convolutional neural network is the most satisfactory and effective classification method applied to classify Egyptsat-1 satellite images.

Graphics Forgery Recognition using Deep Convolutional Neural Network in Video for Trustworthiness

2020 ◽  
Vol 8 (4) ◽  
pp. 78-95
Author(s):  
Neeru Jindal ◽  
Harpreet Kaur
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Efficient Method ◽  
Deep Convolutional Neural Network ◽  
The Other ◽  
Compressed Video ◽  
Learning Techniques ◽  
Average Accuracy ◽  
Simulation Results ◽  
Inter Frame

Doctored video generation with easily accessible editing software has proven to be a major problem in maintaining its authenticity. This article is focused on a highly efficient method for the exposure of inter-frame tampering in the videos by means of deep convolutional neural network (DCNN). The proposed algorithm will detect forgery without requiring additional pre-embedded information of the frame. The other significance from pre-existing learning techniques is that the algorithm classifies the forged frames on the basis of the correlation between the frames and the observed abnormalities using DCNN. The decoders used for batch normalization of input improve the training swiftness. Simulation results obtained on REWIND and GRIP video dataset with an average accuracy of 98% shows the superiority of the proposed algorithm as compared to the existing one. The proposed algorithm is capable of detecting the forged content in You Tube compressed video with an accuracy reaching up to 100% for GRIP dataset and 98.99% for REWIND dataset.

scholarly journals Real-Time Ethiopian Currency Recognition for Visually Disabled Peoples Using Convolutional Neural Network

2020 ◽  
Author(s):  
Mekonnen Legess Meharu ◽  
Hussien Seid Worku
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Promising Result ◽  
Low Vision ◽  
Research Work ◽  
Real Life ◽  
Non Governmental Organizations ◽  
Average Accuracy ◽  
Blind Persons

Abstract A survey report made by the Ethiopian Ministry of Health along with several non-governmental organizations in 2006 G.C, there were about 5.3% of the Ethiopian population lives with blindness and low vision problems. This research work aims to develop a Convolutional Neural Network-based model by using pre-trained models to enable vision-impaired peoples to recognize Ethiopian currency banknotes in real-time scenarios. The models attempt to accurately recognize Ethiopian currency banknotes even if the input images come up with partially or highly distorted and folded Birr notes. 8500 (1700 for each class) banknotes data are collected within real-life situations by using 9 blind persons. The models were evaluated with 500 real-time videos of different conditions. The whole training, classification, and detection tasks have been demonstrated by adopting Tensorflow Object Detection API and the pre-trained Faster R-CNN Inception, and SSD MobileNet models. All the codes are implemented using Python. The model tested using numerous Ethiopian currencies at different banknotes status and light conditions. In the case of Faster R-CNN Inception model an average accuracy, precision, recall, and F1-score of 91.8%, 91.8%, 92.8%, and 91.8% are obtained respectively and in the case of SSD MobileNet model an average accuracy, precision, recall, and F1-score of 79.4%, 79.4%, 93.6%, and 84.4% are obtained respectively within a real-time video. Therefore as the first research work, the model has shown good performance in both models but Faster R-CNN provides a promising result with an average accuracy of 91.8%.

scholarly journals Human Activity Recognition using Cell Phone-Based Accelerometer and Convolutional Neural Network

2021 ◽  
Vol 11 (24) ◽  
pp. 12099
Author(s):  
Ashwani Prasad ◽  
Amit Kumar Tyagi ◽  
Maha M. Althobaiti ◽  
Ahmed Almulihi ◽  
Romany F. Mansour ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Research Work ◽  
Real Life ◽  
Human Activity Recognition ◽  
Good Prediction ◽  
Human Beings ◽  
Average Accuracy

Human Activity Recognition (HAR) has become an active field of research in the computer vision community. Recognizing the basic activities of human beings with the help of computers and mobile sensors can be beneficial for numerous real-life applications. The main objective of this paper is to recognize six basic human activities, viz., jogging, sitting, standing, walking and whether a person is going upstairs or downstairs. This paper focuses on predicting the activities using a deep learning technique called Convolutional Neural Network (CNN) and the accelerometer present in smartphones. Furthermore, the methodology proposed in this paper focuses on grouping the data in the form of nodes and dividing the nodes into three major layers of the CNN after which the outcome is predicted in the output layer. This work also supports the evaluation of testing and training of the two-dimensional CNN model. Finally, it was observed that the model was able to give a good prediction of the activities with an average accuracy of 89.67%. Considering that the dataset used in this research work was built with the aid of smartphones, coming up with an efficient model for such datasets and some futuristic ideas pose open challenges in the research community.

scholarly journals CNN Algorithm for Roof Detection and Material Classification in Satellite Images

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1592
Author(s):  
Jonguk Kim ◽  
Hyansu Bae ◽  
Hyunwoo Kang ◽  
Suk Gyu Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Satellite Imagery ◽  
Satellite Images ◽  
External Shocks ◽  
Satellite Imaging ◽  
Material Classification ◽  
Model Algorithm ◽  
Great Damage

This paper suggests an algorithm for extracting the location of a building from satellite imagery and using that information to modify the roof content. The materials are determined by measuring the conditions where the building is located and detecting the position of a building in broad satellite images. Depending on the incomplete roof or material, there is a greater possibility of great damage caused by disaster situations or external shocks. To address these problems, we propose an algorithm to detect roofs and classify materials in satellite images. Satellite imaging locates areas where buildings are likely to exist based on roads. Using images of the detected buildings, we classify the material of the roof using a proposed convolutional neural network (CNN) model algorithm consisting of 43 layers. In this paper, we propose a CNN structure to detect areas with buildings in large images and classify roof materials in the detected areas.

scholarly journals Enhanced Convolutional-Neural-Network Architecture for Crop Classification

2021 ◽  
Vol 11 (9) ◽  
pp. 4292
Author(s):  
Mónica Y. Moreno-Revelo ◽  
Lorena Guachi-Guachi ◽  
Juan Bernardo Gómez-Mendoza ◽  
Javier Revelo-Fuelagán ◽  
Diego H. Peluffo-Ordóñez
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Classification Model ◽  
Classification Error ◽  
Small Scale ◽  
Post Processing ◽  
Average Accuracy ◽  
Processing Step ◽  
Crop Classification

Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.

Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach

2020 ◽  
Vol 230 ◽  
pp. 117451 ◽  
Author(s):  
Tongshu Zheng ◽  
Michael H. Bergin ◽  
Shijia Hu ◽  
Joshua Miller ◽  
David E. Carlson
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Ground Level

A 3-D Lightweight Convolutional Neural Network for Detecting Docking Structures in Cluttered Environments

2021 ◽  
Vol 55 (4) ◽  
pp. 88-98
Author(s):  
Maria Inês Pereira ◽  
Pedro Nuno Leite ◽  
Andry Maykol Pinto
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Research Work ◽  
Point Clouds ◽  
Measurement Unit ◽  
Maritime Industry ◽  
Worst Case ◽  
Cluttered Environments ◽  
Autonomous Surface Vehicles ◽  
Average Accuracy

Abstract The maritime industry has been following the paradigm shift toward the automation of typically intelligent procedures, with research regarding autonomous surface vehicles (ASVs) having seen an upward trend in recent years. However, this type of vehicle cannot be employed on a full scale until a few challenges are solved. For example, the docking process of an ASV is still a demanding task that currently requires human intervention. This research work proposes a volumetric convolutional neural network (vCNN) for the detection of docking structures from 3-D data, developed according to a balance between precision and speed. Another contribution of this article is a set of synthetically generated data regarding the context of docking structures. The dataset is composed of LiDAR point clouds, stereo images, GPS, and Inertial Measurement Unit (IMU) information. Several robustness tests carried out with different levels of Gaussian noise demonstrated an average accuracy of 93.34% and a deviation of 5.46% for the worst case. Furthermore, the system was fine-tuned and evaluated in a real commercial harbor, achieving an accuracy of over 96%. The developed classifier is able to detect different types of structures and works faster than other state-of-the-art methods that establish their performance in real environments.

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