Anuradha Chetan Phadke

Work place: Dr Vishwanath Karad MIT World Peace University, Pune, India

E-mail: anuradha.phadke@mitpune.edu.in

Website: https://orcid.org 0000-0001-9682-8184

Research Interests:

Biography

Anuradha Chetan Phadke received BE and ME degree in Electronics, from Walchand College of Engineering, Sangli, India in 1993 and 1995 respectively and Ph.D. degree for research work titled “Development of algorithms for diagnosis of breast cancer using digital Mammogram Analysis”, in 2016 from Savitribai Phule Pune University (SPPU). Her expertise is in the domain of Computer Vision. She is presently working as Associate Professor at Department of Electrical and Electronics Engineering, Dr. Vishwanath Karad MIT World Peace University. She has authored two books, granted three patents, published and presented several papers in indexed Journals and conferences. She has worked in collaboration with institutes and industries on various interdisciplinary research projects. She has received recognition from IEEE, as one of the top women contributors to IEEE publications from MITWPU commemorating International Women‟s day on 10th March 2025 and received “Prof. Y. K. Bhushan Most Influential Professor” citation on 13th July 2023 at World education Congress. She was recipient of “Ideal Teacher Award” by Maharashtra Academy of Engineering & Education Research, Pune for Sept. 2011 and was selected as finalists of “Pearson Teaching Awards” under “Innovation in Teaching” category of undergraduate level, held on 13th Dec 2013.

Author Articles
Semantic Segmentation of Multispectral Satellite Images Using Residual Convolutional Networks

By Abhinav Chandra Anuradha Chetan Phadke Vaidehi Deshmukh

DOI: https://doi.org/10.5815/ijigsp.2026.02.02, Pub. Date: 8 Apr. 2026

Satellite imagery is always used to study spatial geographies to find water, residential, farmland, and forest lands; which can be further used for township development and planning, landscape detection etc. Semantic segmentation and image classification are the two crucial procedures in determining the spatial geographies. In order to improve the generalization ability of semantic segmentation algorithms, a combined model of UNet_ResNet is used in this paper. The engineered model is a type of Convolutional Neural Networks using GeoGANs which detects semantic patches in neural networks with smaller sizes and regional characteristics within a certain spatial and pixel scale. However, it faces a semantic segmentation challenge of identifying roadways in metropolitan areas. The model shows an accuracy score from 93% to 97.3% for image classification and segmentation purposes which fares better than the implementation of various existing architectures.

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