scholarly journals Satellite image classification and quality parame-ters using ML classifier

2018 ◽  
Vol 7 (1.8) ◽  
pp. 6
Author(s):  
K. Radhika ◽  
S. Varadarajan
Keyword(s):  
Maximum Likelihood ◽  
Satellite Images ◽  
Performance Metrics ◽  
Satellite Image ◽  
Feature Space ◽  
Support Vector ◽  
Vector Machines ◽  
Supervised Classifiers ◽  
Multispectral Satellite Images

Remote sensing images are an important source of information regarding the Earth surface. For many applications like geology, urban planning, forest and land cover/land use, the underlying information from such images is needed. Extraction of this information is usually achieved through a classification process which is one of the most powerful tools in digital image processing. Good classifier is required to extract the information in satellite images. Latest methods used for classification of pixels in multispectral satellite images are supervised classifiers such as Support Vector Machines (SVM), k-Nearest Number (K-NN) and Maximum Likelihood (ML) classifier. SVM may be one-class SVM or multi-class SVM. K-NN is simple technique in high-dimensional feature space. In ML classifier, classification is based on the maximum likelihood of the pixel. The performance metrics for these classifiers are calculated and compared. Totally 200 points have been considered for validation purpose.

Using Remote Monitoring and Machine Learning to Classify Slam Events of Wave Piercing Catamarans

2021 ◽  
Vol 163 (A3) ◽  
Author(s):  
B Shabani ◽  
J Ali-Lavroff ◽  
D S Holloway ◽  
S Penev ◽  
D Dessi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Probability Distributions ◽  
Feature Space ◽  
Support Vector ◽  
Acceleration Threshold ◽  
Vector Machines ◽  
Structural Responses ◽  
As Stress

An onboard monitoring system can measure features such as stress cycles counts and provide warnings due to slamming. Considering current technology trends there is the opportunity of incorporating machine learning methods into monitoring systems. A hull monitoring system has been developed and installed on a 111 m wave piercing catamaran (Hull 091) to remotely monitor the ship kinematics and hull structural responses. Parallel to that, an existing dataset of a similar vessel (Hull 061) was analysed using unsupervised and supervised learning models; these were found to be beneficial for the classification of bow entry events according to key kinematic parameters. A comparison of different algorithms including linear support vector machines, naïve Bayes and decision tree for the bow entry classification were conducted. In addition, using empirical probability distributions, the likelihood of wet-deck slamming was estimated given a vertical bow acceleration threshold of 1  in head seas, clustering the feature space with the approximate probabilities of 0.001, 0.030 and 0.25.

scholarly journals Data Fusion of Scanned Black and White Aerial Photographs with Multispectral Satellite Images

Sci ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 10
Author(s):  
Dimitris Kaimaris ◽  
Petros Patias ◽  
Giorgos Mallinis ◽  
Charalampos Georgiadis
Keyword(s):  
Data Fusion ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Image Acquisition ◽  
Satellite Image ◽  
Aerial Photographs ◽  
Black And White ◽  
Multispectral Satellite Images ◽  
Number Of Publications

Αbstract: To date, countless satellite image fusions have been made, mainly with panchromatic spatial resolution to a multispectral image ratio of 1/4, fewer fusions with lower ratios, and relatively recently fusions with much higher spatial resolution ratios have been published. Apart from this, there is a small number of publications studying the fusion of aerial photographs with satellite images, with the year of image acquisition varying and the dates of acquisition not mentioned. In addition, in these publications, either no quantitative controls are performed on the composite images produced, or the aerial photographs are recent and colorful and only the RGB bands of the satellite images are used for data fusion purposes. The objective of this paper is the study of the addition of multispectral information from satellite images to black and white aerial photographs of the 80s decade (1980–1990) with small difference (just a few days) in their image acquisition date, the same year and season. Quantitative tests are performed in two case studies and the results are encouraging, as the accuracy of the classification of the features and objects of the Earth’s surface is improved and the automatic digital extraction of their form and shape from the archived aerial photographs is now allowed. This opens up a new field of use for the black and white aerial photographs and archived multispectral satellite images of the same period in a variety of applications, such as the temporal changes of cities, forests and archaeological sites.

scholarly journals Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images

2021 ◽  
Vol 15 (4) ◽  
pp. 101-116
Author(s):  
Lamyaa Gamal El-deen Taha ◽  
Rania Elsayed Ibrahim
Keyword(s):  
Support Vector Machines ◽  
Maximum Likelihood ◽  
High Resolution ◽  
Random Forest ◽  
Satellite Images ◽  
Support Vector ◽  
Morphological Operations ◽  
Vector Machines ◽  
High Resolution Images ◽  
Very High

The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery.The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.

Hyperparameter tuning for multi-label classification of feedbacks in online courses

2021 ◽  
pp. 1-9
Author(s):  
Dorian Ruiz Alonso ◽  
Claudia Zepeda Cortés ◽  
Hilda Castillo Zacatelco ◽  
José Luis Carballido Carranza
Keyword(s):  
Search Strategy ◽  
Performance Metrics ◽  
Online Courses ◽  
Support Vector ◽  
Grid Search ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Feedback Model ◽  
Vector Machines

In this work, we propose the extension of a methodology for the multi-label classification of feedback according to the Hattie and Timperley feedback model, incorporating a hyperparameter tuning stage. It is analyzed whether the incorporation of the hyperparameter tuning stage prior to the execution of the algorithms support vector machines, random forest and multi-label k-nearest neighbors, improves the performance metrics of multi-label classifiers that automatically locate the feedback generated by a teacher to the activities sent by students in online courses on the Blackboard platform at the task, process, regulation, praise and other levels proposed in the feedback model by Hattie and Timperley. The grid search strategy is used to refine the hyperparameters of each algorithm. The results show that the adjustment of the hyperparameters improves the performance metrics for the data set used.

Comparison of Accuracy Measures for RS Image Classification using SVM and ANN Classifiers

2017 ◽  
Vol 7 (3) ◽  
pp. 1180
Author(s):  
S.V.S. Prasad ◽  
T. Satya Savithri ◽  
Iyyanki V. Murali Krishna
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Satellite Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Landsat 8 ◽  
Hybrid Technique ◽  
Multispectral Satellite Images ◽  
Artificial Neural Network Ann

<p>The accurate land use land cover (LULC) classifications from satellite imagery are prominent for land use planning, climatic change detection and eco-environment monitoring. This paper investigates the accuracy and reliability of Support Vector Machine (SVM) classifier for classifying multi-spectral image of Hyderabad and its surroundings area and also compare its performance with Artificial Neural Network (ANN) classifier. In this paper, a hybrid technique which we refer to as Fuzzy Incorporated Hierarchical clustering has been proposed for clustering the multispectral satellite images into LULC sectors. The experimental results show that overall accuracies of LULC classification of the Hyderabad and its surroundings area are approximately 93.159% for SVM and 89.925% for ANN. The corresponding kappa coefficient values are 0.893 and 0.843. The classified results show that the SVM yields a very promising performance than the ANN in LULC classification of high resolution Landsat-8 satellite images.</p>

Data Fusion of Scanned Black and White Aerial Photographs with Multispectral Satellite Images

Sci ◽  
2019 ◽  
Vol 1 (2) ◽  
pp. 36
Author(s):  
Dimitris Kaimaris ◽  
Petros Patias ◽  
Giorgos Mallinis ◽  
Charalampos Georgiadis
Keyword(s):  
Data Fusion ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Image Acquisition ◽  
Satellite Image ◽  
Aerial Photographs ◽  
Black And White ◽  
Multispectral Satellite Images ◽  
Number Of Publications

To date, countless satellite image fusions have been made, mainly with panchromatic spatial resolution to a multispectral image ratio of 1/4, fewer fusions with lower ratios, and relatively recently fusions with much higher spatial resolution ratios have been published. Apart from this, there is a small number of publications studying the fusion of aerial photographs with satellite images, with the year of image acquisition varying and the dates of acquisition not mentioned. In addition, in these publications, either no quantitative controls are performed on the composite images produced, or the aerial photographs are recent and colorful and only the RGB bands of the satellite images are used for data fusion purposes. The objective of this paper is the study of the addition of multispectral information from satellite images to black and white aerial photographs of the 2nd half of the 20th century (1950–1999) with small difference (just a few days) in their image acquisition date, the same year and season. Quantitative tests are performed in two case studies and the results are encouraging, as the accuracy of the classification of the features and objects of the Earth’s surface is improved and the automatic digital extraction of their form and shape from the archived aerial photographs is now allowed. This opens up a new field of use for the black and white aerial photographs and archived multispectral satellite images of the same period in a variety of applications, such as the temporal changes of cities, forests and archaeological sites.

scholarly journals Comparison of maximum likelihood, support vector machines, and random forest techniques in satellite images classification

Tecnura ◽  
2019 ◽  
Vol 23 (59) ◽  
pp. 13-26 ◽  
Author(s):  
José Antonio Valero Medina ◽  
Beatriz Elena Alzate Atehortúa
Keyword(s):  
Support Vector Machines ◽  
Maximum Likelihood ◽  
Random Forest ◽  
Decision Trees ◽  
Satellite Image ◽  
Likelihood Estimation ◽  
Assessment Process ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Vector Machines

Context: Nowadays, the images of the Earth surface and the algorithms for their classification are widely available. In particular, the algorithms are promising in the differentiating of cotton crops stages, but it is necessary to establish the capabilities of the different algorithms in order to identify their advantages, and disadvantages. Method: This paper describes the assessment process in which the Support Vector Machines (SVM) and random-forest technique (decision trees) are compared with the maximum likelihood estimation when differentiating the stages of cotton crops. A RapidEye satellite image of a geographic area in the municipality of San Pelayo, Cordoba (Colombia), is used for the study. Using a set of sampling polygons, a random sample of 6000 pixels was taken (2000 training and 4000 for validating the classifications.) Confusion matrices, and R (data processing and analysis software) were used during the validation process Results: The maximun likelihood estimation presented a correct classification percentage of 68.95%. SVM correctly classified 81.325% of the cases and the decision trees correctly classified 78.925%. The confidence test for the classifications showed non-overlapping intervals, and SVM obtained the highest values. Conclusions: It was possible to confirm the superiority of the technique based on support vector machines for the proposed verification zones. However, this technique requires a number of classes that comprehensively represent the variations of the image (in order to guarantee a minimum number of support vectors) to avoid confusion in the classification of non-sampled areas. This was less evident in the other two classification techniques analysed.

scholarly journals Data Fusion of Scanned Black and White Aerial Photographs with Multispectral Satellite Images

Sci ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 29
Author(s):  
Dimitris Kaimaris ◽  
Petros Patias ◽  
Giorgos Mallinis ◽  
Charalampos Georgiadis
Keyword(s):  
Data Fusion ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Image Acquisition ◽  
Satellite Image ◽  
Aerial Photographs ◽  
Black And White ◽  
Multispectral Satellite Images ◽  
Number Of Publications

To date, countless satellite image fusions have been made, mainly with panchromatic spatial resolution to a multispectral image ratio of 1/4, fewer fusions with lower ratios, and relatively recently fusions with much higher spatial resolution ratios have been published. Apart from this, there is a small number of publications studying the fusion of aerial photographs with satellite images, with the year of image acquisition varying and the dates of acquisition not mentioned. In addition, in these publications, either no quantitative controls are performed on the composite images produced, or the aerial photographs are recent and colorful and only the RGB bands of the satellite images are used for data fusion purposes. The objective of this paper is the study of the addition of multispectral information from satellite images to black and white aerial photographs of the 80s decade (1980–1990) with small difference (just a few days) in their image acquisition date, the same year and season. Quantitative tests are performed in two case studies and the results are encouraging, as the accuracy of the classification of the features and objects of the Earth’s surface is improved and the automatic digital extraction of their form and shape from the archived aerial photographs is now allowed. This opens up a new field of use for the black and white aerial photographs and archived multispectral satellite images of the same period in a variety of applications, such as the temporal changes of cities, forests and archaeological sites.

scholarly journals Classification of hyperspectral endocrine tissue images using support vector machines

2020 ◽  
Vol 16 (5) ◽  
pp. 1-10
Author(s):  
Marianne Maktabi ◽  
Hannes Köhler ◽  
Magarita Ivanova ◽  
Thomas Neumuth ◽  
Nada Rayes ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Endocrine Tissue ◽  
Vector Machines

scholarly journals Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

2019 ◽  
Vol 12 (1) ◽  
pp. 96 ◽  
Author(s):  
James Brinkhoff ◽  
Justin Vardanega ◽  
Andrew J. Robson
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Image ◽  
Resource Planning ◽  
Support Vector ◽  
Perennial Crop ◽  
Perennial Crops ◽  
Object Based ◽  
Rbf Kernel

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

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