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A Dual Neural Architecture Combined Squeezenet with Octconv for Lidar Data Classification

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A Dual Neural Architecture Combined Squeezenet with Octconv for Lidar Data Classification sensors Article A Dual Neural Architecture Combined SqueezeNet with OctConv for LiDAR Data Classification Aili Wang 1,*, Minhui Wang 1, Kaiyuan Jiang 1, Mengqing Cao 1 and Yuji Iwahori 2 1 The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang, Harbin University of Science and Technology, Harbin 150080, China; [email protected] (M.W.); [email protected] (K.J.); [email protected] (M.C.) 2 Department of Computer Science, Chubu University, Aichi 487-8501, Japan; [email protected] * Correspondence: [email protected]; Tel.: +86-451-86-392-366 Received: 7 October 2019; Accepted: 9 November 2019; Published: 12 November 2019 Abstract: Light detection and ranging (LiDAR) is a frequently used technique of data acquisition and it is widely used in diverse practical applications. In recent years, deep convolutional neural networks (CNNs) have shown their effectiveness for LiDAR-derived rasterized digital surface models (LiDAR-DSM) data classification. However, many excellent CNNs have too many parameters due to depth and complexity. Meanwhile, traditional CNNs have spatial redundancy because different convolution kernels scan and store information independently. SqueezeNet replaces a part of 3 3 convolution kernels in CNNs with 1 1 convolution kernels, decomposes the original × × one convolution layer into two layers, and encapsulates them into a Fire module. This structure can reduce the parameters of network. Octave Convolution (OctConv) pools some feature maps firstly and stores them separately from the feature maps with the original size. It can reduce spatial redundancy by sharing information between the two groups. In this article, in order to improve the accuracy and efficiency of the network simultaneously, Fire modules of SqueezeNet are used to replace the traditional convolution layers in OctConv to form a new dual neural architecture: OctSqueezeNet. Our experiments, conducted using two well-known LiDAR datasets and several classical state-of-the-art classification methods, revealed that our proposed classification approach based on OctSqueezeNet is able to provide competitive advantages in terms of both classification accuracy and computational amount. Keywords: data classification; light detection and ranging (LiDAR); convolutional neural networks (CNNs); SqueezeNet; octave convolution (OctConv) 1. Introduction Light detection and ranging (LiDAR) technology is an active remote sensing measurement technology which can obtain ground object information by emitting laser to the target [1]. Compared with optical imaging and infrared remote sensing, LiDAR can obtain high-resolution three-dimensional spatial point clouds, elevation models, and other raster-derived data independent of weather conditions through laser beam [2,3]. The data adopted in this article are LiDAR-derived rasterized digital surface models (LiDAR-DSM) which are obtained by rasterizing the point cloud data acquired by the LiDAR system [4]. There is a lot of research on LiDAR-DSM data classification. Priestnall et al. examined methods for extracting surface features from DSM produced by LiDAR [5]. Song et al. modified the crown shape in spectrum by using DSM [6]. Zhou et al. used minimum description length (MDL) and morphology to recognize the buildings [7]. Zhou combined LiDAR height and intensity data to accurately map urban land cover [8]. Sensors 2019, 19, 4927; doi:10.3390/s19224927 www.mdpi.com/journal/sensors Sensors 2019, 19, 4927 2 of 15 LiDAR-DSM mainly includes terrain changes and feature heights of the target area and it is suitable for classification tasks that distinguish targets with different heights. Thus, the accurate classification of DSM plays an important role in distinguishing different land cover categories. The classification task of LiDAR-DSM data is usually based on the classification of pixels, that is, the interpretation process of remote sensing images [9]. For example, Lodha et al. used the support vector machine (SVM) algorithm to classify LiDAR data and obtained higher accuracy and convincing visual results [10]. Sasaki et al. adopted a decision tree analysis to investigate the average height of each land class [11]. Naidoo et al. used an automated Random Forest modelling approach to classify eight common savanna tree species [12]. Khodadadzadeh et al. developed a new efficient strategy for fusion and classification of hyperspectral and LiDAR data to integrate multiple types of features [13]. Ghamisi et al. proposed a joint classification method which is based on extinction profiles (EPs) features and convolutional neural network (CNN) to improve the classification accuracy [14]. Then Ghamisi et al. proposed to extract the spatial and background information of DSM data in an unsupervised manner to obtain higher classification precision [15]. Wang et al. combined morphological profiles (MPs) and CNN to provide more features for LiDAR-DSM classification [16]. He et al. used spatial transformer networks (STN) to identify the best input of CNN for LiDAR classification [17]. Xia et al. used an ensemble classifier to process morphological features which fuses hyperspectral (HS) images and LiDAR-DSM [18]. Ge et al. proposed a new framework for fusion of hyperspectral image and LiDAR data based on the extinction profiles, local binary pattern (LBP), and kernel collaborative representation classification [19]. AlexNet [20] won the ImageNet [21] competition in 2012, making the convolutional neural network (CNN) become the focus again. It is a breakthrough achievement in the history of CNN development. Since then, convolutional neural networks have been widely used in various image processing fields [22–32]. Compared with the traditional machine learning algorithms, the three characteristics of CNN (receptive field, weights sharing, and subsampling) make it learn characteristics from the images directly. Researchers have proposed various CNNs with different structures and obtained good classification results [33–36]. These excellent CNNs are designed to achieve higher classification accuracy by designing deeper and more complex structures. However, they have relatively many structural parameters and may require large storage space and computing resources. SqueezeNet is a lightweight structure with a smaller number of structural parameters and less calculations whose structure and classification accuracy satisfy the application requirements as well [37]. SqueezeNet has only 1 1 and 3 3 convolution kernels and its purpose is not to obtain the best × × classification accuracy but to simplify the complexity of the network and achieve the classification accuracy similar to a public network. Octave Convolution (OctConv) is mainly used to deal with feature mapping of multiple spatial frequencies and reduce spatial redundancy. It is a single, generic, and plug-and-play convolution unit that can replace ordinary convolution directly and it does not need to adjust the network structure. In addition, the features of adjacent pixels in one image have similarities; however, the convolution kernel in traditional CNNs sweeps each location and stores its own feature description independently. It ignores spatial consistency, so that the feature maps have a large amount of redundancy in the spatial dimension. OctConv scales a portion of the feature maps and then communicates with the unscaled portion to reduce spatial redundancy and enhance feature utilization [38]. In this article, we propose a novel dual neural architecture, OctSqueezeNet, which combines SqueezeNet with OctConv, which improves LiDAR-DSM data classification accuracy with less structural parameter memory. In this article, OctSqueezeNet was applied to LiDAR data classification. The main contributions are written as follows: SqueezeNet combined with OctConv was used to LiDAR data classification for the first time, which improved the classification accuracy and reduced the storage space occupied by the network. We replaced the original convolutional layer in OctConv with the Fire modules in SqueezeNet to Sensors 2019, 19, 4927 3 of 15 compensate for the shortcomings of SqueezeNet, which had less extracted feature information due to lots of 1 1 convolution kernel. × 2. SqueezeNet Design Architecture The main objective of SqueezeNet is maintaining competitive accuracy with few parameters. To achieve this goal, three main strategies were adopted. Firstly, the 3 3 filter was replaced by the 1 1 × × filter for which has fewer parameters. Secondly, we reduced the number of input channels to the 3 3 × filters. Finally, we performed subsampled operations in the later stages of the network to make the convolution layer with large activation. SqueezeNet drew on the idea of the Inception module [23] to design a Fire module with a squeeze layer and an expand layer. The structure of Fire module is shown in Figure1. In order to reduce the number of channels for input elements, the squeeze layer used a 1 × 1 convolution kernel to compress the input elements. The expansion layer used the 1 1 and 3 3 Sensors 2019, 19, x FOR PEER REVIEW × 3 of 15 × convolution kernels for multi-scale learning and concatenating. Sensors 2019, 19, x FOR PEER REVIEW 3 of 15 1×1 squeeze 1×1 squeeze expend expend 3×33×3 1×1 1×1 softmaxsoftmax FigureFigure 1. 1.Structure Structure of of Fire Fire module. module. Figure 1. Structure of Fire module. The operationThe main process objective of of the SqueezeNet Fire module is maintaining is shown competitive in Figure 2accuracy. The size with of few the parameters.
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