A Comparative Study Between Neural Network and Maximum Likelihood in the Satellite Image Classification

Proceedings of the First International Workshop on Artificial Neural Networks: Data Preparation Techniques and Application Development ◽

10.5220/0001130100010008 ◽

2004 ◽

Keyword(s):

Neural Network ◽

Maximum Likelihood ◽

Comparative Study ◽

Image Classification ◽

Satellite Image ◽

Satellite Image Classification

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Implementation and comparative analysis of rough set, Artificial Neural Network (ANN) and Fuzzy-Rough classifiers for satellite image classification

2009 International Conference on Intelligent Agent & Multi-Agent Systems ◽

10.1109/iama.2009.5228037 ◽

2009 ◽

Cited By ~ 4

Author(s):

Mamta Juneja ◽

Ekta Walia ◽

Parvinder Singh Sandhu ◽

Rajni Mohana

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Comparative Analysis ◽

Image Classification ◽

Rough Set ◽

Satellite Image ◽

Satellite Image Classification ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Performance Enhancement of Satellite Image Classification Using a Convolutional Neural Network

Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017 - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-319-64861-3_63 ◽

2017 ◽

pp. 673-682 ◽

Cited By ~ 1

Author(s):

Noureldin Laban ◽

Bassam Abdellatif ◽

Hala M. Ebied ◽

Howida A. Shedeed ◽

Mohamed F. Tolba

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Classification ◽

Performance Enhancement ◽

Satellite Image ◽

Satellite Image Classification

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A Comparative Study on Machine Learning Methods for On-board Satellite Image Classification

The Proceedings of the Space Engineering Conference ◽

10.1299/jsmesec.2016.25.1b1 ◽

2016 ◽

Vol 2016.25 (0) ◽

pp. 1B1

Author(s):

Kei Ohta ◽

Takehiko Koike ◽

Yoichi Yatsu ◽

Saburo Matunaga

Keyword(s):

Machine Learning ◽

Comparative Study ◽

Image Classification ◽

Satellite Image ◽

Learning Methods ◽

Machine Learning Methods ◽

Satellite Image Classification

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Advanced Satellite Image Classification of Various Resolution Image Using a Novel Approach of Deep Neural Network Classifier

Wireless Personal Communications ◽

10.1007/s11277-018-6024-7 ◽

2018 ◽

Vol 104 (1) ◽

pp. 357-372

Author(s):

S. Jayanthi ◽

C. Vennila

Keyword(s):

Neural Network ◽

Image Classification ◽

Deep Neural Network ◽

Satellite Image ◽

Neural Network Classifier ◽

Resolution Image ◽

Satellite Image Classification ◽

Novel Approach

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Landsat 8 OLI Satellite Image Classification using Convolutional Neural Network

Procedia Computer Science ◽

10.1016/j.procs.2020.03.398 ◽

2020 ◽

Vol 167 ◽

pp. 987-993 ◽

Cited By ~ 1

Author(s):

Amit Kumar Rai ◽

Nirupama Mandal ◽

Akansha Singh ◽

Krishna Kant Singh

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Classification ◽

Satellite Image ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Satellite Image Classification

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Satellite Image Classification Using Artificial Neural Network

International Journal of Research in Advent Technology ◽

10.32622/ijrat.6122018109 ◽

2019 ◽

Vol 7 (1) ◽

pp. 459-462 ◽

Cited By ~ 1

Author(s):

P. Sathya ◽

Dr.V. Baby deepa

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Image Classification ◽

Satellite Image ◽

Satellite Image Classification ◽

Artificial Neural

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Convolutional Neural Network for Satellite Image Classification

Intelligent Information and Database Systems: Recent Developments - Studies in Computational Intelligence ◽

10.1007/978-3-030-14132-5_13 ◽

2019 ◽

pp. 165-178 ◽

Cited By ~ 3

Author(s):

Mohammed Abbas Kadhim ◽

Mohammed Hamzah Abed

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Classification ◽

Satellite Image ◽

Satellite Image Classification

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Satellite Image Classification with Data Augmentation and Convolutional Neural Network

Lecture Notes in Electrical Engineering - Advances in Electrical and Computer Technologies ◽

10.1007/978-981-15-5558-9_9 ◽

2020 ◽

pp. 83-92

Author(s):

Parth R. Dave ◽

Hariom A. Pandya

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Classification ◽

Data Augmentation ◽

Satellite Image ◽

Satellite Image Classification

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Satellite image classification using Genetic Algorithm trained radial basis function neural network, application to the detection of flooded areas

Journal of Visual Communication and Image Representation ◽

10.1016/j.jvcir.2016.11.017 ◽

2017 ◽

Vol 42 ◽

pp. 173-182 ◽

Cited By ~ 21

Author(s):

Akansha Singh ◽

Krishna Kant Singh

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Radial Basis Function ◽

Image Classification ◽

Basis Function ◽

Satellite Image ◽

Satellite Image Classification ◽

Radial Basis ◽

Network Application ◽

Flooded Areas

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Fruit Fly-Based Artificial Neural Network Classifier with Kernel-Based Fuzzy c-Means Clustering for Satellite Image Classification

International Journal of Image and Graphics ◽

10.1142/s0219467820500163 ◽

2020 ◽

Vol 20 (02) ◽

pp. 2050016 ◽

Cited By ~ 1

Author(s):

Sandeep Kumar ◽

L. Suresh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Image Classification ◽

Satellite Image ◽

Fruit Fly ◽

Fuzzy C Means ◽

Satellite Image Classification ◽

Artificial Neural Network Classifier ◽

Fuzzy C Means Clustering ◽

Artificial Neural

Image segmentation and classification are the major challenges to satellite imagery. Also, the identification of unique objects in the satellite image is a significant aspect in the application of remote sensing. Many satellite image classification techniques have been presented earlier. However, the accuracy of the image classification has to be further improved. So that, optimal artificial neural network with kernel-based fuzzy c-means ([Formula: see text]) clustering based satellite image classification is proposed in this paper. Initially, the images are segmented with the help of KFCM algorithm. Then, color features and gray level co-occurrence matrix (GLCM) features to be extracted from the segmented regions. Then, these extracted features are given to the OANN classifier. Based on these features, segmented regions are classified as building, road, shadow, and tree. To enhance the performance of the classifier, the weight values are optimally selected with the help of fruit fly algorithm. Simulation results show that the performance of proposed classifier outperforms that of the existing filters in terms of accuracy.

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