Implementation of deep convolutional neural network for classification of multiscaled and multiangled remote sensing scene

Abstract

With the evolution of convolutional neural networks, extraction of deep features for accurate classification of Remote Sensed (RS) images have gained lot of momentum. However, due to variation in the scale of high resolution remote sensed images, accurate classification still remains a challenging task. Moreover, along with the scale, variation in the angle also decreases the accuracy of extraction of deep features using convolutional neural network. In this paper, a Multiscale and Multiangle convolutional neural network (MSMA-CNN) is proposed which extracts deep features of the RS images by employing several convolutional, pooling and fully connected layers which are discriminant, nonlinear and invariant. In MSMA-CNN, along with the spatial features, spectral features are also considered for classification of remote sensing scenes thus, making the entire system robust. The RS images are scaled at different levels using Gaussian Pyramid Decomposition and rotated at different angles and further features are derived using maximally stable extremal regions (MSER) at spectral and spatial level which are further concatenated and fed to the MSMA-CNN. A regularization parameter is added to get the results for test images as close as the trained images. A hybrid MSMA-CNN structure is designed by altering various parameters of the CNN structure to get improved optimized performance. To demonstrate the effectiveness of the proposed method, we compared the results on six challenging high-resolution remote sensing datasets and achieve a classification accuracy of 92.25% which shows significant improvement compared to the other state-of-the-art scene classification methods in terms classificational accuracy and computational cost.