Machine learning (ML) techniques are used often to classify pixels in multispectral images. Recently, there is growing interest in using Convolution Neural Networks (CNNs) for classifying multispectral images. CNNs are preferred because of high performance, advances in hardware such as graphical processing units (GPUs), and availability of several CNN architectures. In CNN, units in the first hidden layer view only a small image window and learn low level features. Deeper layers learn more expressive features by combining low level features. In this paper, we propose a novel approach to classify pixels in a multispectral image using deep convolution neural networks (DCNNs). In our approach, each feature vector is mapped to an image. We used the proposed framework to classify two Landsat scenes that are obtained from New Orleans and Juneau, Alaska areas. The suggested approach is compared with the commonly used classifiers such as the Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The proposed approach has shown the state-of-the-art results.
This article is published by (IJACSA) International Journal of Advanced Computer Science Applications with a Creative Commons BY 4.0 License: http://creativecommons.org/licenses/by/4.0/
The Science and Information Organization
Date of publication
Kulkarni, Arun D., "Multispectral Image Analysis using Convolution Neural Networks" (2023). Computer Science Faculty Publications and Presentations. Paper 24.