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Earth Science Informatics

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01-07-2022 | Research Article

A new framework for object detection using fastcnn- Naïve Bayes classifier for remote sensing image extraction

Authors: K. Kala, N. Padmasini, B. Suresh Chander Kapali, P. G. Kuppusamy

Published in: Earth Science Informatics | Issue 3/2022

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Abstract

In today's research, remote sensing applications play an important role. It might foresee any crisis, provide weather forecasting updates, geographical research, military security, and so forth. Object detection and recognition of remote sensing satellite images, however, is a difficult task. To solve the problem of distinct in remote sensing images, in this work the system, initially, the dataset goes under the phase of testing and training process. After the data is pre-processed with a fuzzy filter, the features are recovered utilizing an enhanced method by constructing effective CNN based Features from the Accelerated Segment Test feature extraction technique (FASTCNN) Convolutional Neural Network. Object detection classification is done via an optimized Naive Bayes (NB) classifier, which detects objects significantly faster and keeps the system running at a higher accuracy rate. The proposed feature extraction strategy outperforms the previous approach in terms of performance.

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Title: A new framework for object detection using fastcnn- Naïve Bayes classifier for remote sensing image extraction
Authors: K. Kala
N. Padmasini
B. Suresh Chander Kapali
P. G. Kuppusamy
Publication date: 01-07-2022
Publisher: Springer Berlin Heidelberg
Published in: Earth Science Informatics / Issue 3/2022
Print ISSN: 1865-0473
Electronic ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-022-00834-3

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