A Hybrid MLP-SVM Model for Classification using Spatial-Spectral Features on Hyper-Spectral Images
This work provides an incremental improvement in classification accuracy for hyper-spectral image analysis, which is relevant for remote sensing applications.
This paper addresses challenges in hyper-spectral image classification, such as high dimensionality and limited labeled data. The proposed hybrid MLP-SVM model significantly improved classification accuracy on the Indian Pines, U. Pavia, and Salinas datasets, achieving 93.22%, 96.87%, and 93.81% respectively, compared to individual SVM and MLP classifiers.
There are many challenges in the classification of hyper spectral images such as large dimensionality, scarcity of labeled data and spatial variability of spectral signatures. In this proposed method, we make a hybrid classifier (MLP-SVM) using multilayer perceptron (MLP) and support vector machine (SVM) which aimed to improve the various classification parameters such as accuracy, precision, recall, f-score and to predict the region without ground truth. In proposed method, outputs from the last hidden layer of the neural net-ork become the input to the SVM, which finally classifies into various desired classes. In the present study, we worked on Indian Pines, U. Pavia and Salinas dataset with 16, 9, 16 classes and 200, 103 and 204 reflectance bands respectively, which is provided by AVIRIS and ROSIS sensor of NASA Jet propulsion laboratory. The proposed method significantly increases the accuracy on testing dataset to 93.22%, 96.87%, 93.81% as compare to 86.97%, 88.58%, 88.85% and 91.61%, 96.20%, 90.68% based on individual classifiers SVM and MLP on Indian Pines, U. Pavia and Salinas datasets respectively.