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  Published Paper Details:

  Paper Title

CLASSIFICATION OF DIFFERENT DEEP-LEARNING TECHNIQUES FOR HIGH RESOLUTION SATELLITE IMAGERY OF DAMAGED BUILDING EXTRACTION

  Authors

  Ms. Tanvi Rajnikantbhai Navik,  Dr. Mukta Agarwal

  Keywords

Semantic segmentation, Satellite Imagery, High Resolution Satellite Imagery, Deep Learning, Convolution Neural Networks, Mask R-CNN, Support Vector Machine

  Abstract


With the improved availability of high-resolution satellite imagery, it is possible to detect detailed structures on our planet's surface. Expert knowledge, supervision, and fieldwork are required for traditional mapping approaches. This study examines the potential of machine learning methods such as artificial neural networks (ANN), support vector machines (SVM), and random forest (RF), as well as different deep-learning convolution neural networks (CNNs) for high-resolution satellite imagery using optical data from the satellite and topographic factors. To do multi-label categorization of Amazon satellite images, this uses a Convolutional Neural Network (CNN) model. It starts with a CNN model that obtains an F-score of 0.84. Then, using three deep CNN architectures that have recently performed well in the ImageNet Challenge, demonstrate that a ResNet-50 model can get a 0.91 F-score. The results reveal that without any additional post-processing steps, state-of-the-art methods outperform by 9.8% on the Intersection over Union metric. The DeepGlobe Building Extraction Challenge asks participants to extract all building polygons from satellite images. They use the segmentation algorithm Mask R-CNN and a method that combines Mask R-CNN with building boundary regularization to construct polygons that are accurate and complete. This approach, however, provides better regularized polygons than Mask R-CNN, which is useful in many applications.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2212310

  Paper ID - 228963

  Page Number(s) - c863-c874

  Pubished in - Volume 10 | Issue 12 | December 2022

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Ms. Tanvi Rajnikantbhai Navik,  Dr. Mukta Agarwal,   "CLASSIFICATION OF DIFFERENT DEEP-LEARNING TECHNIQUES FOR HIGH RESOLUTION SATELLITE IMAGERY OF DAMAGED BUILDING EXTRACTION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 12, pp.c863-c874, December 2022, Available at :http://www.ijcrt.org/papers/IJCRT2212310.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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