Brain tumor detection with the mri image and 54900 image Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft .This repo is of segmentation and morphological operations which are the basic concepts of image processing. Detection and extraction of tumor from MRI scan images of the brain is done using python.
Brain tumor detection with the mri image and 54900 image Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft .This repo is of segmentation and morphological operations which are the basic concepts of image processing. Detection and extraction of tumor from MRI scan images of the brain is done using python.
Presented by Adrien Depeursinge, PhD, at MICCAI 2015 Tutorial on Biomedical Texture Analysis (BTA), Munich, Oct 5 2015.
Texture-based imaging biomarkers complement focal, invasive biopsy based biomarkers by providing information on tissue structure over broad regions, non-invasively, and repeatedly across multiple time points. Texture has been used to predict patient survival, tissue function, disease subtypes and genomics (imagenomics and radiogenomics). Nevertheless, several challenges remain, such as: the lack of an appropriate framework for multi-scale, multi-spectral analysis in 2D and 3D; localization uncertainty of texture operators; validation; and, translation to routine clinical applications.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
This slide gives you the basic understanding of digital image compression.
Please Note: This is a class teaching PPT, more and detail topics were covered in the classroom.
Texture based feature extraction and object trackingPriyanka Goswami
The project involved developing and implementing different texture analysis based extraction techniques like Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Ternary Pattern (LTP) in MATLAB and carrying out a comparative study by analyzing the effectiveness of each technique using a standard set of images (Yale data set). The most optimum technique is then applied to identify cloud patterns and track their motion (in pixel position changes) in time series images (acquired from weather satellites like GOES) using the Chi-Square Difference method.
Digital image processing focuses on two major tasks
-Improvement of pictorial information for human interpretation
-Processing of image data for storage, transmission and representation for autonomous machine perception
Image Segmentation
Types of Image Segmentation
Semantic Segmentation
Instance Segmentation
Types of Image Segmentation Techniques based on the image properties:
Threshold Method.
Edge Based Segmentation.
Region-Based Segmentation.
Clustering Based Segmentation.
Watershed Based Method.
Artificial Neural Network Based Segmentation.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
A clear visualization of RGB and CMY color model. How they work and what are their color elements.At the end, you also find the equation of calculating and converting them.
Lec12: Shape Models and Medical Image SegmentationUlaş Bağcı
ShapeModeling – M-reps
– Active Shape Models (ASM)
– Oriented Active Shape Models (OASM)
– Application in anatomy recognition and segmentation – Comparison of ASM and OASM
ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Data Processing Using THEOS Satellite Imagery for Disaster Monitoring (Case S...NopphawanTamkuan
This content shows the specification of THEOS/Thaichote (Thai satellite), information of flood in Vietnam, comparison of pre-disaster image (Landsat-8) and post-disaster image (THEOS) by different methods such as color composite, thresholding, and segmentation for flooded areas classification.
Presented by Adrien Depeursinge, PhD, at MICCAI 2015 Tutorial on Biomedical Texture Analysis (BTA), Munich, Oct 5 2015.
Texture-based imaging biomarkers complement focal, invasive biopsy based biomarkers by providing information on tissue structure over broad regions, non-invasively, and repeatedly across multiple time points. Texture has been used to predict patient survival, tissue function, disease subtypes and genomics (imagenomics and radiogenomics). Nevertheless, several challenges remain, such as: the lack of an appropriate framework for multi-scale, multi-spectral analysis in 2D and 3D; localization uncertainty of texture operators; validation; and, translation to routine clinical applications.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
This slide gives you the basic understanding of digital image compression.
Please Note: This is a class teaching PPT, more and detail topics were covered in the classroom.
Texture based feature extraction and object trackingPriyanka Goswami
The project involved developing and implementing different texture analysis based extraction techniques like Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Ternary Pattern (LTP) in MATLAB and carrying out a comparative study by analyzing the effectiveness of each technique using a standard set of images (Yale data set). The most optimum technique is then applied to identify cloud patterns and track their motion (in pixel position changes) in time series images (acquired from weather satellites like GOES) using the Chi-Square Difference method.
Digital image processing focuses on two major tasks
-Improvement of pictorial information for human interpretation
-Processing of image data for storage, transmission and representation for autonomous machine perception
Image Segmentation
Types of Image Segmentation
Semantic Segmentation
Instance Segmentation
Types of Image Segmentation Techniques based on the image properties:
Threshold Method.
Edge Based Segmentation.
Region-Based Segmentation.
Clustering Based Segmentation.
Watershed Based Method.
Artificial Neural Network Based Segmentation.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
A clear visualization of RGB and CMY color model. How they work and what are their color elements.At the end, you also find the equation of calculating and converting them.
Lec12: Shape Models and Medical Image SegmentationUlaş Bağcı
ShapeModeling – M-reps
– Active Shape Models (ASM)
– Oriented Active Shape Models (OASM)
– Application in anatomy recognition and segmentation – Comparison of ASM and OASM
ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Data Processing Using THEOS Satellite Imagery for Disaster Monitoring (Case S...NopphawanTamkuan
This content shows the specification of THEOS/Thaichote (Thai satellite), information of flood in Vietnam, comparison of pre-disaster image (Landsat-8) and post-disaster image (THEOS) by different methods such as color composite, thresholding, and segmentation for flooded areas classification.
Do Fractional Norms and Quasinorms Help to Overcome the Curse of Dimensiona...Alexander Gorban
A talk given at IJCNN2019, Budapest
Curse of dimensionality (Bellman, 1957);
Blessing of dimensionality (Kainen, 1997).
For a random sample in high-dimensional space:
(i) Concentration of distances: Distances between almost all pairs of points are almost equal;
(ii) Quasiorthogonality: Vectors of the sample are almost orthogonal (after centralization);
(iii) Stochastic separation: Almost every point is linearly separable from the set of all other points --
With high probability,
for a wide class of distributions
and even for exponentially large samples.
Do fractional norms can compensate curse of dimensionality??? (Hypothesis of C.C. Aggarwal, 2001)
Analysis of dozens of datasets gives the answer:
NO! Fractional quasinorms do not help to overcome the curse of dimensionality in classification problem.
We propose two novel Tensor Voting (TV) based supervised binary classification algorithms for N-Dimensional (N-D) data points. (a) The first one finds an approximation to a separating
hyper-surface that best separates the given two classes in N-D: this is done by finding a set of candidate decision-surface points (using the training data) and then modeling the decision
surface by local planes using N-D TV; test points are then classified based on local plane equations. (b) The second algorithm defines a class similarity measure for a given test point t, which is the maximum over all inner products of the vector from t (to training point p) and the tangent at p (computed with TV): t is then assigned the class with the best similarity measure. Our approach is fast, local in nature and is equally valid for different kinds of decisions: we performed several experiments on real and synthetic data to validate our approach, and compared our approaches with standard classifiers such as kNN and Decision Trees.
Lower back pain can be caused by a variety of problems with any parts of the complex, interconnected network of spinal muscles, nerves, bones, discs or tendons in the lumbar spine. This solution is about to identify a person is abnormal or normal using collected physical spine details.
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...MLconf
Graph Representation Learning with Deep Embedding Approach:
Graphs are commonly used data structure for representing the real-world relationships, e.g., molecular structure, knowledge graphs, social and communication networks. The effective encoding of graphical information is essential to the success of such applications. In this talk I’ll first describe a general deep learning framework, namely structure2vec, for end to end graph feature representation learning. Then I’ll present the direct application of this model on graph problems on different scales, including community detection and molecule graph classification/regression. We then extend the embedding idea to temporal evolving user-product interaction graph for recommendation. Finally I’ll present our latest work on leveraging the reinforcement learning technique for graph combinatorial optimization, including vertex cover problem for social influence maximization and traveling salesman problem for scheduling management.
Validation Study of Dimensionality Reduction Impact on Breast Cancer Classifi...ijcsit
A fundamental problem in machine learning is identifying the most representative subset of features from
which we can construct a predictive model for a classification task. This paper aims to present a validation
study of dimensionality reduction effect on the classification accuracy of mammographic images. The
studied dimensionality reduction methods were: locality-preserving projection (LPP), locally linear
embedding (LLE), Isometric Mapping (ISOMAP) and spectral regression (SR). We have achieved high
rates of classifications. In some combinations the classification rate was 100%. But in most of the cases the
classification rate is about 95%. It was also found that the classification rate increases with the size of the
reduced space and the optimal value of space dimension is 60. We proceeded to validate the obtained
results by measuring some validation indices such as: Xie-Beni index, Dun index and Alternative Dun
index. The measurement of these indices confirms that the optimal value of reduced space dimension is
d=60.
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Pinaki Ranjan Sarkar
Recent advancement in sensor technology allows very high spatial resolution along with multiple spectral bands. There are many studies, which highlight that Object Based Image Analysis(OBIA) is more accurate than pixel-based classification for high resolution(< 2m) imagery. Image segmentation is a crucial step for OBIA and it is a very formidable task to estimate optimal parameters for segmentation as it does not have any unique solution. In this paper, we have studied different segmentation algorithms (both mono-scale and multi-scale) for different terrain categories and showed how the segmented output depends on upon various parameters. Later, we have introduced a novel method to estimate optimal segmentation parameters. The main objectives of this study are to highlight the effectiveness of presently available segmentation techniques on very high-resolution satellite data and to automate segmentation process. Pre-estimation of segmentation parameter is more practical and efficient in OBIA. Assessment of segmentation algorithms and estimation of segmentation parameters are examined based on the very high-resolution multi-spectral WorldView-3(0.3m, PAN sharpened) data.
Abstract— Rekonstruksi model tiga dimensi (3D) dapat digunakan untuk tujuan navigasi, dan aplikasi virtual reality. Namun, saat ini model 3D juga digunakan sebagai upaya untuk mitigasi bencana seperti perencanaan evakuasi kebakaran dan gempa bumi. Penelitian ini bertujuan untuk membentuk 3D model bangunan menggunakan gambar panorama 720 derajat. Penilaian akurasi menggunakan akurasi aerial triangulasi, akurasi digitasi sudut dan juga mengambil data terrestrial laser scanning (TLS) untuk membandingkan dan mengukur ground control points (GCPs) menggunakan total station untuk analisa akurasi. Kamera Spherical Garmin VIRB 360 digunakan untuk mengambil video pada 30 fps dengan ukuran gambar 3840 x 2178. Video yang sudah didapatkan akan di ekstrak ke dalam bentuk gambar statis yang berurutan dengan interval 1.23 detik. Gambar panorama yang sudah terbentuk diolah menggunakan Agisoft Photoscan Pro untuk pemodelan 3D. Penilaian akurasi posisi menggunakan GCPs didalam Photoscan Pro. Hasil dense point cloud akan di bandingkan dengan data TLS didalam software CloudCompare. Hasil penelitian yang pertama adalah akurasi posisi 3D (RMSE) setelah SfM adalah 18.9 cm, selain itu perbedaan jarak 3D antara dense point cloud yang dihasilkan dengan data TLS adalah 3.47 cm. Model rekonstruksi bangunan didapatkan menggunakan point cloud dengan memproses didalam Autodesk Revit sehingga dapat digunakan sebagai upaya untuk perencanaan mitigasi bencana.
Kata Kunci—3D Model Rekonstruksi, Gambar Panorama, Fotogrammetri Jarak Dekat.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Satellite Image Classification using Decision Tree, SVM and k-Nearest Neighbor
1. SATELLITE IMAGE
CLASSIFICATION
USING K-NN, SVM,
AND DECISION TREE
1. I Gede B. P. (P66077042)
2. Umroh Dian S. (P66077050)
3. Iva N. (P66067021)
4. M. Irsyadi F. (P66067055)
9. 9
K-NN
• K-Nearest-Neighbor algorithm is a method for
classifying objects based on closest training label in the
feature space.
• K value which gives the minimum error rate may be
selected for K-Nearest Neighbor classification.
• Distance function for K-Nearest-Neighbor is Euclidean
distance. It uses distance based comparison to assign
equal weight to each attribute. They can suffer from
poor accuracy when noisy and irrelevant attributes are
given.
• It is classifying the pattern by comparing a given test
pattern with training pattern that are similar to it. It is
widely used in pattern recognition.
(K-Nearest-Neighbor)
Sounds
Claws
?
K=3
10. 10
SVM
• SVM classification uses different planes in space to
divide data points.
• It gains flexibility in the choice of threshold and handles
more input data very efficiently.
• Its performance and accuracy depend upon the
selection of hyper plane and kernel parameter.
• The goal of SVM Classification is to produce a model,
based on the training data, which will be able to predict
class labels of the test data accurately
(Support Vector Machine)
CupcakeMuffin
Cupcakes are topped with
creamy, delicious frosting.
Muffins may have a sugared
top or a very thin glaze.
VS
10
14. 14
DT
• Decision tree consist of mainly three parts: Partitioning
the nodes, find the terminal nodes and allocate class
label to terminal nodes.
• It is based on hierarchical rule. It handles high
dimensional data and representation of knowledge in
tree form which is easy to humans for understanding
purpose.
• When decision tree built, many of branches reflects
noise in the training pattern so, tree pruning attempts to
identify and remove such branches and improve the
accuracy of classification.
(Decision Tree)
16. 16
Data
High Resolution
Low Resolution Landsat 8
North Taiwan ; 2018
Image size: 4300 x 4300 pixels
Pleiades
Colorado, USA ; 2012
Image size: 1300 x 1300 pixels
ENVI 5.3
Processing
17. High Resolution
Training and Testing Sample
Datasets
• The red color = building region
• The green color = vegetation region
• The blue color = road region
• The yellow color = concrete region
Land Cover Training Area (Segment)
Vegetation 1726
Building 486
Road 72
Concrete 420
17
19. 19
High Resolution
Ground Truth
• Digitation
• Manual Interpretation
• Divided into 4 classes:
1.Building
2.Road
3.Vegetation
4.Concrete
20. Low Resolution
Training and Testing Sample
Datasets
• The red color = urban region
• The green color = vegetation region
• The blue color = water region
Land Cover Training Area (Segment)
Vegetation 11275
Water 98
Urban 10964
20
30. 30
Accuracy Assessment and Comparisons
Overall Accuracy (%) SVM DT KNN
High Resolution 78.60 68.41 76.26
Low Resolution 83.30 59.08 82.34
In high resolution, the classification accuracy of SVM and DT were
significantly different. However, the classification accuracy of SVM
and KNN were not significantly different.
SVM always showed the most accurate results, followed by
Decision Tree and KNN.
31. 31
0
10
20
30
40
50
60
70
80
90
0 1000 2000 3000
OverallAccuracy(%)
Training Sample
Comparing the Performance of
Training Sample
SVM
KNN
Training Sample Comparison
SVM has flexibility in choice of threshold than both method.
SVM K-NN
Training Data Overall Accuracy (%) Difference (%)
2704 78.6
2.24
1354 76.36
3.23
238 73.13
0.03157 73.16
Training Data Overall Accuracy (%) Difference (%)
2704 76.26
2.78
1354 73.48
7.82
238 65.66
11.56157 54.1
32. 32
The Impact of Distance in Training Sample
Land Cover Training Area (Segment)
Vegetation 1726
Building 486
Road 72
Concrete 420
Land Cover Training Area (Segment)
Vegetation 77
Building 38
Road 4
Concrete 38
Classifier Method Overall Accuracy (%)
SVM 78.6
KNN 76.26
Classifier Method Overall Accuracy (%)
SVM 73.16
KNN 54.1
The distance of training sample have significant impact on the classification result.
The closest distance will produce a better result, especially on the KNN method.
34. 34
Conclusion
• The SVM method has best accuracy compared to the Decision
Tree and k-Nearest Neighbor methods.
• The value of Kappa coefficient in the SVM method has a high
value compared to both methods.
• The sample size of training samples has more impact on the
classification accuracy for KNN and DT than for SVM. In
addition, SVM has flexibility in choice of threshold than both
method.
• The distance in training sample affect on the classification
result. Especially in the KNN method, we can see the big
difference of overall accuracy based on the number of used
training data.
Example of k-NN classification. The test sample (green circle) should be classified either to the first class of blue squares or to the second class of red triangles. If k = 3 (solid line circle) it is assigned to the second class because there are 2 triangles and only 1 square inside the inner circle. If k = 5 (dashed line circle) it is assigned to the first class (3 squares vs. 2 triangles inside the outer circle).