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  Published Paper Details:

  Paper Title

A NOVEL FRAMEWORK FOR IMAGE CLASSIFICATION USING HYBRID CNN-GAN-BASED MASKING

  Authors

  KADAPALA ANJAIAH,  Dr.K. SAGAR

  Keywords

- Satellite Images, Object Recognition, k-Means Clustering, Support Vector Machines,3DCNN, Hyperspectral Images [HSIs], classification, kernel function

  Abstract


Abstract: This research evaluates object identification systems using machine learning techniques for processing satellite photos. It uses transfer learning and multi-object identification deep learning algorithms on remotely sensed satellite data. Five object identification techniques based on YOLO R-CNN architectures were tested, with YOLOv8 showing the fastest detection speed and highest accuracy. Support vector machines were found to be more useful than k-means clustering for object detection on satellite photos. The study also proposes an efficient 3-D CNN model using overlapping 3-D patches and a kernel function, achieving higher accuracy and faster convergence than existing 2-D/3-D CNN methods.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2509088

  Paper ID - 293460

  Page Number(s) - a731-a738

  Pubished in - Volume 13 | Issue 9 | September 2025

  DOI (Digital Object Identifier) -   

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

  E-ISSN Number - 2320-2882

  Cite this article

  KADAPALA ANJAIAH,  Dr.K. SAGAR,   "A NOVEL FRAMEWORK FOR IMAGE CLASSIFICATION USING HYBRID CNN-GAN-BASED MASKING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 9, pp.a731-a738, September 2025, Available at :http://www.ijcrt.org/papers/IJCRT2509088.pdf

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


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