Bias variance Toeplitz Matrix based Shift Invariance classifier for building detection from satellite images

Abstract

Buildings are a predominant category in man made objects of satellite images. As far as issues like building planning and shape of the building in satellite images plays a paramount aspect. Also, with the contemporary and swift deployment of technology, images are becoming more precise. Moreover, when the image correctness, the distinction in the composition and its features is increasingly obvious hence building feature identification with the aid of artificial selection has become a complicated task. Also, as the building edges are frequently adjoining to other features, there results in misclassification between final building extraction results and original buildings. Hence, novelty in building detection is of considerable importance to enhance the building detection with precise results with minimum classification (i.e., false positive rate). In this work an ideal classification method called, Toeplitz Matrix Convolutional Neural Network-based Shift Invariance (TMCNN-SI) for precise building detection from satellite images is proposed. The TMCNN-SI method is divided into three layers, namely, input layer, three hidden layers and output layer. The input layer by employing the Bias Variance Decomposition-based Generalization function obtains the feature point selection for further processing. In the first hidden layer, a Convolutional Kernel function to the convolved features is applied to generate feature map. In the second hidden layer, Toeplitz Matrix-based Shift Invariance function is applied with the objective of reducing the dimensionality involved in the generated feature map. In the third hidden layer, a Log Fusion function is applied for binary classification with minimum loss. Finally, the resultant target class is obtained in the output layer. The outline of the proposed TMCNN-SI method is studied in terms of classification time, classification accuracy and false positive rate. The radical results show that the TMCNN-SI approach bestows efficiently when considered with respect to classification.