A review on classification of satellite image using Artificial Neural Network (ANN)

Abstract

Artificial Neural Networks (ANNs) have been useful for decades to the development of image classification algorithms applied to several different fields. Image classification is the major component of the remote sensing to extract some of the important spatially variable parameters, such as land cover and land use (LCLU). The aim of this study is to investigate the capability of Artificial Neural Network system (ANNs) for classifying the satellite images using different algorithm which are back-propagation algorithm and K-means algorithm with different approaches. ANN's classifier is compared with two classification techniques of conventional classifier which are Maximum Likelihood (ML) and unsupervised (ISODATA). Neural network classification is based on the training data set and it the proper classification. ML and ISODATA classifiers are broadly used in many remote sensing applications. Overall classification accuracy and Kappa Coefficient were calculated to get the comparison of the performance the image classification. The optimal performance would be identified by validating the classification results with ground truth data. The accurate classification can produce the correct LU/LC map that can be used fir variety.

Determining Subarctic Peatland Vegetation Using an Unmanned Aerial System (UAS)

“…A confusion Data was extracted for the randomly selected plots for manual interpretation into one of the eight cover classes (Figures 3-5) and all pixels were extracted within a polygon. Using the data extracted from the imagery and the manually interpreted cover class, we developed a Bayesian artificial neural network [37]. Because training sample locations were randomly selected, no spatial component was included in the analysis.…”

“…Machine learning algorithms include decision trees, random forests, boosted trees, vector machines, and artificial neural networks (ANNs) [33][34][35]. ANNs use a supervised classification to train and validate data, with intermediate nodes that develop a model [36,37] and have been used in remote sensing [38,39].…”

Techniques for Machine Learning based Spatial Data Analysis: Research Directions

“…A satellite image using ANN [1] used the capability of ANN's characterizing the satellite pictures utilizing distinctive algorithm which is back-propagation algorithm and K-mean algorithm with different methodologies. ANN's classifier is compared with maximum likelihood(ML) and unsupervised(ISODATA) conventional classifier.…”

“…(1) Regression algorithm (2) Instance-based algorithms (3) Regularization algorithms (4) Decision Tree algorithms (5) Bayesian algorithms (6) Clustering algorithms (7) Association Rule Learning algorithms (8) Artificial Neural Network based algorithms (9) Deep Learning algorithms (10) Dimensionality Reduction algorithms (11) Ensemble algorithms A satellite image using ANN [1] used it's capability to characterize the satellite pictures by utilizing distinctive algorithm which are back-propogation algorithm and K-mean mechanism with different methodologies. ANN's classifier is correlated with Maximum Likelihood (ML) along with unsupervised (ISODATA) conventional classifier.…”

Timescape Image Panorama Registration Techniques

“…To optimize the feature detection and extraction process, the researchers have worked to support their efforts using nontraditional techniques. Machine learning techniques like artificial neural networks (ANN) has obtained popularity in image subset selection [50]. To optimize the feature selection process for a robust image classification, Al-Sahaf et.…”