Multispectral Image Analysis Using Deep Neural Networks

Multispectral image classification plays a crucial role in remote sensing applications such as land cover mapping, agricultural monitoring, and environmental surveillance. Traditional classification techniques, including the Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM), Decision Tree (DT), and Multi-Layer Perceptron (MLP), often struggle with the complexity and high dimensionality of multispectral data. Recent advances in deep learning have revolutionized the field of remote sensing by enabling the extraction of high-level, abstract features from raw input data. In this paper, we explore the application of Deep Neural Networks (DNNs) for pixel-wise classification in multispectral imagery. DNNs are capable of learning informative and hierarchical representations, which have demonstrated significant success in a wide range of computer vision tasks. We propose and implement a simple DNN architecture consisting of six layers: an input layer (representing reflectance values across spectral bands), a fully connected layer, a batch normalization layer, a ReLU activation layer, another fully connected layer, and a final SoftMax output layer for classification. Each pixel is represented by a vector of spectral reflectance values. We evaluated our model using two Landsat scenes, one from the New Orleans area and the other from the Mississippi River bottomland area. The proposed DNN achieved classification accuracy of 97.44% and 95.74%, respectively, on these datasets, demonstrating the effectiveness of deep learning for multispectral image classification.