TY - GEN
T1 - Classification of Remotely Sensed Data Using Fisher’s Linear Discriminant
AU - Shivakumar, B. R.
AU - Raghudathesh, G. P.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - In place of time-consuming and expensive data collecting on the ground, remote sensing allows for rapid, repeated coverage of enormous areas, with widespread practical applications in fields as diverse as meteorology, disaster reporting, and climate science. Because there are many different LULC classes that can be analyzed, it is important to investigate the classifiers’ classification performance to learn about their strengths and weaknesses. In this study, we use Fisher’s linear discriminant analysis approach to classify two sets of multispectral medium-resolution remote sensor (RS) data and evaluate its performance in recognizing LULC classes, extracting LULC classes, and distinguishing between spectrally overlapping classes. Ten randomly selected pixels from the data are used to illustrate Fisher’s LDA’s pixel assignment approach. The classification analysis shows that Fisher’s LDA is very good at extracting classes that are spectrally dominating, but it is not very effective at extracting classes that are spectrally subservient.
AB - In place of time-consuming and expensive data collecting on the ground, remote sensing allows for rapid, repeated coverage of enormous areas, with widespread practical applications in fields as diverse as meteorology, disaster reporting, and climate science. Because there are many different LULC classes that can be analyzed, it is important to investigate the classifiers’ classification performance to learn about their strengths and weaknesses. In this study, we use Fisher’s linear discriminant analysis approach to classify two sets of multispectral medium-resolution remote sensor (RS) data and evaluate its performance in recognizing LULC classes, extracting LULC classes, and distinguishing between spectrally overlapping classes. Ten randomly selected pixels from the data are used to illustrate Fisher’s LDA’s pixel assignment approach. The classification analysis shows that Fisher’s LDA is very good at extracting classes that are spectrally dominating, but it is not very effective at extracting classes that are spectrally subservient.
UR - https://www.scopus.com/pages/publications/85210520020
UR - https://www.scopus.com/pages/publications/85210520020#tab=citedBy
U2 - 10.1007/978-981-97-4657-6_14
DO - 10.1007/978-981-97-4657-6_14
M3 - Conference contribution
AN - SCOPUS:85210520020
SN - 9789819746569
T3 - Lecture Notes in Electrical Engineering
SP - 177
EP - 194
BT - Recent Advances in Signals and Systems - Select Proceedings of VSPICE 2023
A2 - Ansary, Omid
A2 - Lin, Meng
A2 - Shivakumar, B.R.
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023
Y2 - 19 November 2023 through 20 November 2023
ER -