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Abstract
ABSTRACTWith the development of earth observation technologies, the acquired remote sensing images are increasing dramatically, and a new era of big data in remote sensing is coming. How to effectively mine these massive volumes of remote sensing data are new challenges. Deep learning provides a new approach for analyzing these remote sensing data. As one of the deep learning models, convolutional neural networks (CNNs) can directly extract features from massive amounts of imagery data and is good at exploiting semantic features of imagery data. CNNs have achieved remarkable success in computer vision. In recent years, quite a few researchers have studied remote sensing image classification using CNNs, and CNNs can be applied to realize rapid, economical and accurate analysis and feature extraction from remote sensing data. This paper aims to provide a survey of the current state-of-the-art application of CNN-based deep learning in remote sensing image classification. We first briefly introduce the principles and characteristics of CNNs. We then survey developments and structural improvements on CNN models that make CNNs more suitable for remote sensing image classification, available datasets for remote sensing image classification, and data augmentation techniques. Then, three typical CNN application cases in remote sensing image classification: scene classification, object detection and object segmentation are presented. We also discuss the problems and challenges of CNN-based remote sensing image classification, and propose corresponding measures and suggestions. We hope that the survey can facilitate the advancement of remote sensing image classification research and help remote-sensing scientists to tackle classification tasks with the state-of-art deep learning algorithms and techniques.
Highlights
With the development of earth observation technologies, an integrated space-airground global observation has been gradually established
The above studies shown that more index, texture and spectrum information in multisource data has been used in convolutional neural networks (CNNs)-based remote sensing (RS) image classification, and the result of these experiments proved that this approach could improve accuracy of extracting geographic objects in RS images
Bounding boxes that may contain target objects are first generated, and whether there are target objects is determined for each bounding box. To determine whether they rely on an external method for candidate region proposals, region proposal CNNs can further be classified into Region-based CNNs (R-CNNs) and Faster R-CNNs (Ren, Girshick, Girshick, & Sun, 2017)
Summary
With high-resolution satellite remote sensing systems as the principal parts. As a result, our comprehensive abilities to observe the earth have reached unprecedented levels (Li, Zhang, & Xia, 2014). In 2012, AlexNet (Krizhevsky, Sutskever, & Hinton, 2012), a deep learning model of convolutional neural network (CNN), achieved remarkable accuracy in the computer vision field and won the ImageNet Challenge, a top-level competition in the image classification field. This CNN model is developed from ordinary neural networks, and directly extracts features from massive amounts of imagery data and abstracts the features layer by layer. CNNs, as one type of deep leaning networks, have the following advantages over shallow structure models: (1) CNNs directly apply a convolution operation to the pixels of an image to extract abstract data features. In order to better understand CNN-based image classification, this section will briefly introduce the structure of CNNs and its training method, followed by several popular CNN models in the computer vision field
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