Abstract

Current scene classification for high-resolution remote sensing images usually uses deep convolutional neural networks (DCNN) to extract extensive features and adopts support vector machine (SVM) as classifier. DCNN can well exploit deep features but ignore valuable shallow features like texture and directional information; and SVM can hardly train a large amount of samples in an efficient way. This paper proposes a fast deep perception network (FDPResnet) that integrates DCNN and Broad Learning System (BLS), a novel effective learning system, to extract both deep and shallow features and encapsulates a designed DPModel to fuse the two kinds of features. FDPResnet first extracts the shallow and the deep scene features of a remote sensing image through a pre-trained model on residual neural network-101 (Resnet101). Then, it inputs the two kinds of features into a designed deep perception module (DPModel) to obtain a new set of feature vectors that can describe both higher-level semantic and lower-level space information of the image. The DPModel is the key module responsible for dimension reduction and feature fusion. Finally, the obtained new feature vector is input into BLS for training and classification, and we can obtain a satisfactory classification result. A series of experiments are conducted on the challenging NWPU-RESISC45 remote sensing image dataset, and the results demonstrate that our approach outperforms some popular state-of-the-art deep learning methods, and present high-accurate scene classification within a shorter running time.

Highlights

  • The ever-advancing remote sensing technology can generate a large number of high-resolution remote sensing images in a fast and effective way

  • We propose a fast deep perception network based on ResNet101 (Abbreviated as FDPResNet) that targets scene classification for remote sensing images

  • We conducted a series of experiments on NWPU-RESISC45 dataset [21], which was created by a research team of the Northwestern Polytechnic University in 2017

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Summary

Introduction

The ever-advancing remote sensing technology can generate a large number of high-resolution remote sensing images in a fast and effective way. This deftness has promoted its applications throughout numerous fields, including natural disaster monitoring, geospatial object detection, traffic supervision, weapon guidance and urban planning [1,2,3]. High-resolution remote sensing images, contain unique characteristics that make classifying scenes in them quite difficult. They usually stretch in different sizes, and contain diversified contents, like multi-directional targets standing against complex backgrounds. How to effectively classify scenes in high-resolution remote sensing images remains a challenging task

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