TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING-- Part 2

PART 2
TRAFFIC MANAGEMENT USING SATELLITE IMAGES
Go, change the orld
Intelligent Systems 1

• Motivation
• Dataset description
• Methodology
• Role of team members
• Timeline
• Expected outcomes
• References
PRESENTATION CONTENTS Go, change the world
2
Intelligent Systems

Motivation Go, change the world
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Intelligent Systems
The project is mainly intended to aid the traffic department by taking data
of roads and vehicles. This is used for road maintenance, annual surveys.
These can also be taken in order to identify speeding vehicles, heavy
vehicles movement, and in other crime related issues
This is also used in regulating traffic movement by identifying easy routes
by analysis of extracted roads from satellite imagery

long with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents.
Dataset details Go, change the world
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Intelligent Systems
 Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of
aerial videos have been produced. It is unrealistic for humans to screen such big data
and understand their contents.
 we introduce a novel problem of event recognition in unconstrained aerial videos in
the remote sensing community and present a large-scale, human-annotated dataset,
named ERA (Event Recognition in Aerial videos)
 The ERA dataset is designed to have a significant intra-class variation and inter-class
similarity and captures dynamic events in various circumstances and at dramatically
various scales.
 Moreover, to offer a benchmark for this task, we extensively validate existing deep
networks. We expect that the ERA dataset will facilitate further progress in automatic
aerial video comprehension.

dat
Dataset details Go, change the world
• Road images from any type of satellite can be considered as dataset provided it has
0.5m resolution
• The images considered are multispectral image .
• The date base for road extraction can be created based on the color of the roads.
• Dataset should contain satellite road images whose road intensity values are within
a particular range.
• The important and key parameter of this algorithm is the color of the roads in the
database.
• The size of the image used is 512*512.

Methodology for solving the problem Go, change the world
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Intelligent Systems
SASSDCCDTELLITE IMAGESS
SATELLITE IMAGES
GRAY
SCALE
IMAGE
ADJUSTING THE
IMAGES ACC TO
THRESHOLD VALUES
OF ROAD
BINARY
IMAGE
MEDIAN
FILTERING
MORPHOLOGICAL
OPERATIONS
REMOVING
SMALL
OBJECTS
EDGE
DETECTION
OVERLAY THE
EXTRACTED ROAD ON
ORIGINAL IMAGE

E
Methodology for solving the problem Go, change the world
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Intelligent Systems
The code is mainly intended in order to extract roads from satellite
imagery. It is implemented using MATLAB
The code first converts the color satellite images to its grayscale images
then threshold is applied. It is then converted to its binary form. The image
is then median filtered, the connected components are removed from the
filtered image (less than 6 pixels). It is then morphologically filtered to get
the roads, then the unwanted components are removed and edge detection
is carried out to get the exact road images
The code is working based on color of the road, predominantly white
topped/ concrete roads. The case study was taken for a random satellite
image, then an aerodrome to extract roads

Role of Team Members Go, change the world
1. Sohangi Srivatsava : Implementing image processing using MATLAB tool
2. Nanubala Dhruvan :Exploring an algorithm for the topic
3. Naveen Krishna :Finding the datasets available and selecting the correct one
for the project
4. M Srinath : Finding the errors in the code and explaining about future
motivation
5. Namitha Nandikesh : using different techniques in the road detection

Timeline (16-08-2020 to 3-09-2020) Go, change the world
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Intelligent Systems
 14/08/2020 - Phase 1 presentation
 17/08/2020 – Finalisation of the topic, platform and the algorithm
 18/08/2020 – Exploring the papers and different resources
available.
 20/08/2020 – Session on MATLAB tool
 22/08/2020 – Finalise the method to be used
 23/08/2020 – Finalise the code taken once with the desired
outputs.
 25/08/2020 – Phase 2 presentation

CODE Go, change the world
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Intelligent Systems
Clc
clear all;
close all;
addpath
'C:UsersJIOCHDesktop'
I=imread('input3.jpg');
figure;
imshow(I);
title('Original image');
%% coversion to grayscale
J=rgb2gray(I);
%whos
figure,imshow(J);
title('grayscale image');
%[a,b]=size(J);
%K = zeros(a,b);
K=imadjust(J,[0.5 0.9],[]);
%P=find(J >= 127.5);
%S=find(J <= 229.5);
%M=intersect(P,S);
%K(M)= J(M);
figure;
imshow(K);
%% threshold grayscale image and
binarize
level = graythresh(K);
I=imbinarize(K,level);
figure;
imshow(I);
title('Binary image after
thresholding');
%% median filtering
B = medfilt2(I);
figure,imshow(B);
title('median filtered image');
%% removal of unwanted
componets less than 6 pixels
im = bwareaopen(B,1200);
%Binary = imfill(im,'holes');
figure,imshow(im);
title('removing connected
components(pixel <6)');
%% morphological filtering
BW = bwmorph(im,'remove');
figure,imshow(BW);
title('morphological filtering');
%% edge detection algorithm
BW1 = edge(BW,'sobel');
figure,imshow(BW1);
title('edge detection(sobel)');
%% overlaying image
H = vision.AlphaBlender;
J = im2single(J);
BW1 = im2single(BW1);
Y = step(H,J,BW1);
figure,imshow(Y)
title('overlay on grayscale image');
%whos
%% final display
figure,imshow(J);
hold on;
contour(BW1,100);
hold off;

Expected Outcomes Go, change the world
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Intelligent Systems
IMPLEMENTATION OF FINDING THE TAXIWAY ROADS OF CHENNAI INT’LAIRPORT (SOURCE- GOOGLE EARTH IMAGERY)

Expected Outcomes Go, change the world
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Intelligent Systems
IMPLEMENTATION OF FINDING THE TAXIWAY ROADS OF CHENNAI INT’LAIRPORT (SOURCE- GOOGLE EARTH IMAGERY)

References Go, change the world
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Intelligent Systems
1.Road Extraction From Satellite Images, Satya Sai Venkata Raja Yalla , Indian Institute of
Information Technology Sricity, Chittoor, India.
2. D. Chaudhuri, N. K. Kushwaha, and A. Samal, “SemiAutomated Road Detection From High
Resolution Satellite Images by Directional Morphological Enhancement and Segmentation
Techniques” IEEE Journal Of Selected Topics In Applied Earth Observations And Remote
Sensing, Vol. 5, No. 5, October 2012
3. Yuilie A.L.,Coughlan J.M. 2000. Fundamntal Limits of Bayesian Inference:Orders and Phase
Transitions for Road Tracking.IEEE Transitions on Pattern Analysis and Machine 1ntel1igence.
22(2),pp.160-173.
4. Steger C.,Mayer H.,Radig B. .1997.The Role of Grouping for Road Extraction. Automatic
Extraction of Manmade Objects from Aerial and Space Images.Basel.Birkhaeuser
Verlag.pp.245-255.
5. https://github.com/rajayalla98/Road-Extraction-from-Satellite-Images

TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING-- Part 2

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