Hierarchical spatial features learning with deep CNNs for very high-resolution remote sensing image classification

Abstract

ABSTRACTRecently, very high resolution (VHR) remote sensing (RS) images have become widely available. While a VHR RS image provides more detailed information of the imaged scene, inter-class variation is enhanced as well, which makes it a challenging task to accurately classify the image at pixel level. It has been found that spatial features of a VHR image derived from the spectral measurements can improve the accuracy of image classification. Recent studies have shown that deep convolutional neural networks (CNNs) are powerful to extract high-level spatial features for object recognition and image retrieval. However, few CNNs have been developed for the pixel-wise classification of RS image. Moreover, in order to obtain large-scale spatial features, CNNs can be unmanageably complicated. In this paper, we propose a novel deep CNNs based method for VHR image classification. Firstly, pixel level CNNs are applied to multiscale images generated from the original VHR RS image using the Gaussian pyramid method. The hierarchical multiscale spatial features are then resampled to the size of the raw image. Finally, feature fusion is conducted with the original spectral bands for spectral-spatial based classification, where a support vector machine (SVM) classifier can be employed. Experiments were conducted on the image of VHR aerial image in Vaihingen, Germany. Compared to using spectral features only or with extended morphological profiles (EMPs) for image classification, the proposed method performs better, and the best improvement of classification accuracy is as high as 13%.