Automatic lineament analysis techniques for remotely sensed imagery

Imperial Users onl

An edge detection method using outer totalistic cellular automata

A number of Cellular Automata (CA)-based edge detectors have been developed recently due to the simplicity of the model and the potential for simultaneous removal of different types of noise in the process of detection. This paper introduced a novel edge detector using Outer Totalistic Cellular Automata. Its performance has been compared with other recently developed CA-based edge detectors, in addition to some classic methods, through testing images from a public library. Visual and quantitative measurement of similarity with manually marked correct edges confirmed the superiority of the proposed method over conventional and state-of-the-art CA-based edge detectors

Ballistics Image Processing and Analysis for Firearm Identification

Firearm identification is an intensive and time-consuming process that requires physical interpretation of forensic ballistics evidence. Especially as the level of violent crime involving firearms escalates, the number of firearms to be identified accumulates dramatically. The demand for an automatic firearm identification system arises. This chapter proposes a new, analytic system for automatic firearm identification based on the cartridge and projectile specimens. Not only do we present an approach for capturing and storing the surface image of the spent projectiles at high resolution using line-scan imaging technique for the projectiles database, but we also present a novel and effective FFT-based analysis technique for analyzing and identifying the projectiles

Prevention of Unauthorized Transport of Ore in Opencast Mines Using Automatic Number Plate Recognition

Security in mining is a primary concern, which mainly affects the production cost. An efficiently detecting and deterring theft will maximize the profitability of any mining organization. Many illegal transportation cases were registered in spite of rules imposed by central and state governments under Section 23 (c) of MMDR Act 1957. Use of an automated checkpoint gate based on license plate recognition and biometric fingerprint system for vehicle tracking enhances the security in mines. The method was tested on the number plates with various considerations like clean number plates, clean fingerprints, dusty and faded number plates, dusty fingerprints, and number plates captured by varying distance. By considering all the above conditions the pictures were processed by ANPR and bio-metric fingerprint modules. Vehicle license number plate was captured using a digital camera and the captured RGB image was converted to grayscale image. Thresholding was done to remove unwanted areas from the grayscale image. The characters of the number plate were segmented using Gabor filter. A track-sector matrix was generated by considering the number of pixels in each region and was matched with existing template to identify the character. The fingerprint scans the finger and matches with the template created at the time of fingerprint registration at the machine. The micro-controller accepted the processed output in binary form from ANPR and bio-metric fingerprint system. The micro-controller processed the binary output and the checkpoint gate was closed/open based on the output provided by the microcontroller to motor driver

Edge detection of remote sensing image based on Grünwald-Letnikov fractional difference and Otsu threshold

With the development of remote sensing technology, the resolution of remote sensing images is improving, and the presentation of geomorphic information is becoming more and more abundant, the difficulty of identifying and extracting edge information is also increasing. This paper demonstrates an algorithm to detect the edges of remote sensing images based on Grünwald–Letnikov fractional difference and Otsu threshold. First, a convolution difference mask with two parameters in four directions is constructed by using the definition of the Grünwald–Letnikov fractional derivative. Then, the mask is convolved with the gray image of the remote sensing image, and the edge detection image is obtained by binarization with Otsu threshold. Finally, the influence of two parameters and threshold values on detection results is discussed. Compared with the results of other detectors on the NWPU VHR-10 dataset, it is found that the algorithm not only has good visual effect but also shows good performance in quantitative evaluation indicators (binary graph similarity and edge pixel ratio)

Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Advances in sensor technology and their ever increasing repositories of the collected data are revolutionizing the mechanisms remotely sensed data are collected, stored and processed. This exponential growth of data archives and the increasing user’s demand for real-and near-real time remote sensing data products has pressurized remote sensing service providers to deliver the required services. The remote sensing community has recognized the challenge in processing large and complex satellite datasets to derive customized products. To address this high demand in computational resources, several efforts have been made in the past few years towards incorporation of high-performance computing models in remote sensing data collection, management and analysis. This study adds an impetus to these efforts by introducing the recent advancements in distributed computing technologies, MapReduce programming paradigm, to the area of remote sensing. The MapReduce model which is developed by Google Inc. encapsulates the efforts of distributed computing in a highly simplified single library. This simple but powerful programming model can provide us distributed environment without having deep knowledge of parallel programming. This thesis presents a MapReduce based processing of large satellite images a use case scenario of edge detection methods. Deriving from the conceptual massive remote sensing image processing applications, a prototype of edge detection methods was implemented on MapReduce framework using its open-source implementation, the Apache Hadoop environment. The experiences of the implementation of the MapReduce model of Sobel, Laplacian, and Canny edge detection methods are presented. This thesis also presents the results of the evaluation the effect of parallelization using MapReduce on the quality of the output and the execution time performance tests conducted based on various performance metrics. The MapReduce algorithms were executed on a test environment on heterogeneous cluster that supports the Apache Hadoop open-source software. The successful implementation of the MapReduce algorithms on a distributed environment demonstrates that MapReduce has a great potential for scaling large-scale remotely sensed images processing and perform more complex geospatial problems

Enhanced feature selection algorithm for pneumonia detection

Pneumonia is a type of lung disease that can be detected using X-ray images. The analysis of chest X-ray images is an active research area in medical image analysis and computer-aided radiology. This research aims to improve the accuracy and efficiency of radiologists' work by providing a technique for identifying and categorizing diseases. More attention should be given to applying machine learning approaches to develop a robust chest X-ray image classification method. The typical method for detecting Pneumonia is through chest X-ray images, but analyzing these images can be complex and requires the expertise of a radiographer. This paper demonstrates the feasibility of detecting the disease using chest X-ray images as datasets and a Support Vector Machine combined with a Naive Bayesian classifier, with PCA and GA as feature selection methods. The selected features are essential for training many classifiers. The proposed system achieved an accuracy of 92.26%, using 91% of the principal component. The study's result suggests that using PCA and GA for feature selection in chest X-ray image classification can achieve a good accuracy of 97.44%. Further research is needed to explore the use of other data mining models and care components to improve the accuracy and effectiveness of the system

Vehicle Density Detection Using Hybrid SSD-Yolo-V4 Model

Vehicle traffic congestion is a serious problem in the present day. An intriguing field of study, traffic density estimate is utilized to regulate traffic light systems for more efficient traffic management. The low data resolution and extensive vehicle identification both make this endeavor a more challenging task. This article proposes a vehicle density detection technique using a hybrid deep learning model. The selected data frames from vehicle dataset are denoised using non-adaptive threshold approach. Denoised pictures are segmented using an upgraded Prewitt algorithm and then enhanced using the CLAHE (Contrast Limited Adaptive Histogram Equalization) approach. Then the important features are selected with the use of the Convolutional neural network (CNN) algorithm. The dataset is trained with Deep Q-Learning (DQN) with Improved Spatial Pyramid neural network (I-SPPNET) architecture. Finally, vehicle density identification is done using Improved Single Shot Detector (SSD) with You Only Look Once (YOLOv4) version 4 model and the obtained results proved to be significantly better than CNN, CNN with I-SPPNET and YOVOv3