Optimized Deep Belief Neural Network for Semantic Change Detection in Multi-Temporal Image

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L. Ashok Kumar
M. R. Ebenezar Jebarani
V. Gokula Krishnan


Nowadays, a massive quantity of remote sensing images is utilized from tremendous earth observation platforms. For processing a wide range of remote sensing data to be transferred based on knowledge and information of them. Therefore, the necessity for providing the automated technologies to deal with multi-spectral image is done in terms of change detection. Multi-spectral images are associated with plenty of corrupted data like noise and illumination. In order to deal with such issues several techniques are utilized but they are not effective for sensitive noise and feature correlation may be missed. Several machine learning-based techniques are introduced to change detection but it is not effective for obtaining the relevant features. In other hand, the only limited datasets are available in open-source platform; therefore, the development of new proposed model is becoming difficult. In this work, an optimized deep belief neural network model is introduced based on semantic modification finding for multi-spectral images. Initially, input images with noise destruction and contrast normalization approaches are applied. Then to notice the semantic changes present in the image, the Semantic Change Detection Deep Belief Neural Network (SCD-DBN) is introduced. This research focusing on providing a change map based on balancing noise suppression and managing the edge of regions in an appropriate way. The new change detection method can automatically create features for different images and improve search results for changed regions. The projected technique shows a lower missed finding rate in the Semantic Change Detection dataset and a more ideal rate than other approaches.

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How to Cite
Ashok Kumar, L. ., M. R. E. . Jebarani, and V. . Gokula Krishnan. “Optimized Deep Belief Neural Network for Semantic Change Detection in Multi-Temporal Image”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 2, Mar. 2023, pp. 86-93, doi:10.17762/ijritcc.v11i2.6132.


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