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Biplab Banerjee
Researcher at Indian Institute of Technology Bombay
Publications - 142
Citations - 1204
Biplab Banerjee is an academic researcher from Indian Institute of Technology Bombay. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 12, co-authored 115 publications receiving 533 citations. Previous affiliations of Biplab Banerjee include Central University of Punjab & Jadavpur University.
Papers
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Siamese graph convolutional network for content based remote sensing image retrieval
TL;DR: This paper proposes the SGCN architecture for assessing the similarity between a pair of graphs which can be trained with the contrastive loss function and implements the proposed embeddings for the task of CBIR for RS data on the popular UC-Merced dataset and the PatternNet dataset where improved performance can be observed.
Proceedings ArticleDOI
FusAtNet: Dual Attention based SpectroSpatial Multimodal Fusion Network for Hyperspectral and LiDAR Classification
TL;DR: The proposed FusAtNet framework achieves the state-of-the-art classification performance, including on the largest HSI-LiDAR dataset available, University of Houston (Data Fusion Contest - 2013), opening new avenues in multimodal feature fusion for classification.
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CMIR-NET: A deep learning based model for cross-modal retrieval in remote sensing
TL;DR: A novel deep neural network based architecture is proposed which is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval.
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Graph convolutional network for multi-label VHR remote sensing scene recognition
TL;DR: This paper addresses the problem of multi-label scene classification from Very High Resolution (VHR) satellite remote sensing (RS) images by exploring the deep graph convolutional network (GCN) by model the subsequent supervised learning problem in terms of a novel multi- label deep GCN.
Journal ArticleDOI
A New Self-Training-Based Unsupervised Satellite Image Classification Technique Using Cluster Ensemble Strategy
Biplab Banerjee,Francesca Bovolo,Avik Bhattacharya,Lorenzo Bruzzone,Subhasis Chaudhuri,B. Krishna Mohan +5 more
TL;DR: A cluster-ensemble-based method is proposed here for the initialization of the unsupervised iterative expectation-maximization (EM) algorithm which eventually produces a better approximation of the cluster parameters considering a certain statistical model is followed to fit the data.