公开(公告)号：US20220138481A1

公开(公告)日：2022-05-05

申请号：US17201710

申请日：2021-03-15

Applicant: Tata Consultancy Services Limited

Inventor： Shailesh Shankar Deshpande ,  Rohit Thakur ,  Balamuralidhar Purushothaman

IPC: G06K9/20 ,  G06N3/04 ,  G06K9/46 ,  G06K9/62 ,  G06K9/32

Abstract: Hyperspectral data associated with hyperspectral images received for any Region of Interest (ROI) is in form of number of pixel vectors. Unlike conventional methods in the art that treat this pixel vector as a time series, the embodiments herein provide a method and system that analyzes the pixel vectors by transforming the pixel vector into two-dimensional spectral shape space and then perform convolution over the image of graph thus formed. Learning from pixel vectors directly may not capture the spectral details efficiently. The intuition is to learn the spectral features as represented by the shape of a spectrum or in other words the features which a spectroscopy expert uses to interpret the spectrum. Method and system disclosed converts the pixel vector into image and provides a DCNN architecture that is built for processing 2D visual representation of the pixel vectors to learn spectral and classify the pixels. Thus, DCNN now learn edges, arcs, arcs segments and the other shape features of the spectrum

公开(公告)号：US11615603B2

公开(公告)日：2023-03-28

申请号：US17201710

申请日：2021-03-15

Applicant: Tata Consultancy Services Limited

Inventor： Shailesh Shankar Deshpande ,  Rohit Thakur ,  Balamuralidhar Purushothaman

IPC: G06V10/00 ,  G06V10/143 ,  G06V10/25 ,  G06V10/40 ,  G06F18/2413 ,  G06F18/2431 ,  G06N3/045 ,  G06V10/58

Abstract: The embodiments herein provide a method and system that analyzes the pixel vectors by transforming the pixel vector into two-dimensional spectral shape space and then perform convolution over the image of graph thus formed. Method and system disclosed converts the pixel vector into image and provides a DCNN architecture that is built for processing 2D visual representation of the pixel vectors to learn spectral and classify the pixels. Thus, DCNN learn edges, arcs, arcs segments and the other shape features of the spectrum. Thus, the method disclosed enables converting a spectral signature to a shape, and then this shape is decomposed using hierarchical features learned at different convolution layers of the disclosed DCNN at different levels.