Semi-automatic general approach to achieve the practical number of clusters for classification of remote sensing MS satellite images

Serwa, A.; El-Semary, Hossam H.

doi:10.1007/s41324-019-00283-z

Cited by 6 publications

(4 citation statements)

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“…Kappa value (KHAT) and the overall accuracy is included in the result. The overall accuracy using DANNDO is compared with the result from ADIPRS software which was developed previously by Serwa (Serwa & El-Semary, 2020) and it is found identical; the explanation for this result is the similarity of coding of BPNN in both software. No need for any statistical test because the matching case is 100% identical.…”

Section: Resultsmentioning

confidence: 84%

“…Sachdeva et al ( 2016) developed the package -SFERCB -"Segmentation, feature extraction, reduction, and classification analysis by both SVM and ANN for brain tumors. In Some important research that proved the accuracy enhancement in remote sensing classification can be achieved by reducing the fuzzy in the final results such as Nabil et al (2020), Serwa and El-Semary (2020), Serwa (2020.Most recent research concerns a certain application of DANN not a general framework like this research, the reason is that there are some dark areas in DANN must be discovered by a general implementation. To UDC 528.8…”

mentioning

confidence: 95%

“…Chen and Cheng (2004) made design and realize the software of machining techniques manual but since 2004 it can be considered away from the recent updates. Some examples from previously trials to develop a reliable software find insight into the dark area of DANN one must go through it step by step and do all necessary experiments (Serwa & El-Semary, 2020).…”

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Development of Soft Computational Simulator for Optimized Deep Artificial Neural Networks for Geomatics Applications: Remote Sensing Classification as an Application

Serwa

2022

Geodesy and Cartography

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Artificial neural networks (ANN) obtain more importance after the innovation of deep learning (DL) approach. This research is oriented towards development of soft computational simulator for geomatics research using ANN supporting the deep approach. ANN seems to be a black box due to its sensitivity towards initialization, architecture, and behavior. This research gives a spotlight on the dull areas of ANN algorithm by developing a soft computational simulator for it. The applied examples are chosen to cover geomatics data. DANNDO (Deep Artificial Neural Networks Designer and Optimizer) software is developed to achieve the research objective. Multi-layer perceptron (MLP) architecture is applied in this simulator. Geomatics (remote sensing multi- spectral data) is selected to be a testing paradigm to insure the reliability of the developed simulator. The developed simulator proved the high performance of applying both shallow and deep ANN (DANN).

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Section: Resultsmentioning

confidence: 84%

mentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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