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Chapter 4

Abstract

deep CNNs to other remote sensing tasks.

deep learning, generalization power

1. Introduction

67

Utilization of Deep Convolutional

Chang Luo, Hanqiao Huang, Yong Wang and Shiqiang Wang

Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, for the task of remote scene classification, there are no sufficient images to train a very deep CNN from scratch. Instead, transferring successful pre-trained deep CNNs to remote sensing tasks provides an effective solution. Firstly, from the viewpoint of generalization power, we try to find whether deep CNNs need to be deep when applied for remote scene classification. Then, the pre-trained deep CNNs with fixed parameters are transferred for remote scene classification, which solve the problem of timeconsuming and parameters over-fitting at the same time. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that transferred deep CNNs can achieve state-of-the-art results in unsupervised setting. This chapter also provides baseline for applying

Keywords: convolutional neural network, remote sensing, scene classification,

Remote sensing image processing achieves great advances in recent years, from low-level tasks, such as segmentation, to high-level ones, such as classification. [1–7] However, the task becomes incrementally more difficult as the level of abstraction increases, going from pixels, to objects, and then scenes. Classifying remote scenes according to a set of semantic categories is a very challenging problem, because of high intra-class variability and low interclass distance. [5–9] Therefore, the more representative and higher-level representations are desirable and will certainly play a dominant role in scene-level tasks. The deep convolutional neural network (CNN), which is acknowledged as the most successful and widely used deep learning model, attempts to learn high-level features corresponding to high level of abstraction [10]. Its recent impressive results for classification and detection tasks bring dramatic improvements beyond the state-of-the-art records on a number of benchmarks [11–14]. In theory, considering the subtle differences among categories in remote scene classification, we may attempt to form high-level representations for remote scenes from CNN activations. However, the acquisition of large-scale well-annotated remote sensing datasets is costly, and it is easy to

Neural Networks for Remote

Sensing Scenes Classification

## Chapter 4
