Semantic segmentation of remote sensing ship image via a convolutional neural networks model

IET Image ProcessingVolume 13, Issue 6 p. 1016-1022 Research ArticleFree Access Semantic segmentation of remote sensing ship image via a convolutional neural networks model Wenxiu Wang, Wenxiu Wang Department 9, CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai, 200083 People's Republic of China Department 9, Shanghai Institute of Technical Physics of CAS, Shanghai, 200083 People's Republic of China Department 9, University of Chinese Academy of Sciences, Beijing, 100049 People's Republic of ChinaSearch for more papers by this authorYutian Fu, Yutian Fu Department 9, CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai, 200083 People's Republic of China Department 9, Shanghai Institute of Technical Physics of CAS, Shanghai, 200083 People's Republic of ChinaSearch for more papers by this authorFeng Dong, Corresponding Author Feng Dong dongfeng@mail.sitp.ac.cn Department 9, CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai, 200083 People's Republic of China Department 9, Shanghai Institute of Technical Physics of CAS, Shanghai, 200083 People's Republic of ChinaSearch for more papers by this authorFeng Li, Feng Li Department 9, CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai, 200083 People's Republic of China Department 9, Shanghai Institute of Technical Physics of CAS, Shanghai, 200083 People's Republic of China Department 9, University of Chinese Academy of Sciences, Beijing, 100049 People's Republic of ChinaSearch for more papers by this author Wenxiu Wang, Wenxiu Wang Department 9, CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai, 200083 People's Republic of China Department 9, Shanghai Institute of Technical Physics of CAS, Shanghai, 200083 People's Republic of China Department 9, University of Chinese Academy of Sciences, Beijing, 100049 People's Republic of ChinaSearch for more papers by this authorYutian Fu, Yutian Fu Department 9, CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai, 200083 People's Republic of China Department 9, Shanghai Institute of Technical Physics of CAS, Shanghai, 200083 People's Republic of ChinaSearch for more papers by this authorFeng Dong, Corresponding Author Feng Dong dongfeng@mail.sitp.ac.cn Department 9, CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai, 200083 People's Republic of China Department 9, Shanghai Institute of Technical Physics of CAS, Shanghai, 200083 People's Republic of ChinaSearch for more papers by this authorFeng Li, Feng Li Department 9, CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai, 200083 People's Republic of China Department 9, Shanghai Institute of Technical Physics of CAS, Shanghai, 200083 People's Republic of China Department 9, University of Chinese Academy of Sciences, Beijing, 100049 People's Republic of ChinaSearch for more papers by this author First published: 02 April 2019 https://doi.org/10.1049/iet-ipr.2018.5914Citations: 9AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Semantic segmentation of remote sensing ship targets is one of the most challenging works in image processing, especially for small and multi-scale ship target detection. To solve these problems, an efficient method based on convolutional neural networks (CNN) to detect ship targets is proposed. This method introduces the attention model to the network to enhance the characteristics of small targets and combines atrous convolution with traditional CNN to increase the receptive field. To preserve the information lost by pooling, the proposed method uses the passthrough layer method to retain more features and concatenate the high- and low-resolution features. To verify the effectiveness of the method proposed in this study, the performance was evaluated by using precision, recall, F1-Score, mean intersection-over-union (IoU), and pixel accuracy measurements. These performances are all higher than the traditional semantic segmentation network SegNet. Mean IoU increases to 0.783 and pixel accuracy increases to 0.935. This method can conclusively identify ship targets in remote sensing images and has a certain reference value for remote sensing target detection. 1 Introduction In recent years, with the rapid development of earth observation technology, a large number of high spatial resolution optical remote sensing imaging and processing methods have emerged [1]. Aerospace aerial remote sensing imaging is a significant data source for the detection and identification of ground objects, with broad coverage, a long detection range, and high efficiency of execution [2]. Determining how to extract useful information from massive remote sensing image data accurately and timely to automatically find targets of interest is a vital issue for the application of remote sensing image target detection [3]. As carriers of maritime transport are an important military objective, ships have always been targets with had significant meaning and value [4] in our society, whether in the military or civil industry. In the military field, high-efficiency ship detection algorithms provide a powerful technical means for the supervision of ships. If we knew the real-time position of a target ship, then we would know the layout and dynamics of the enemy fleet and could select the appropriate war strategy. In civilian application, real-time monitoring of ships location in a target sea area allows better management of the sea area [5-7]. For example, effective monitoring of remote sensing ship targets could reduce various ship collision disasters if applied to improve intruder detection and for search and rescue operations in marine accidents. It can also be used to supervise fisheries and manage marine transportation to ensure the safety of coasts and seas [8, 9]. The use of remote sensing for the detection of ships has gradually become the general focus of the current target detection field and its researchers. The use of statistical greyscale image properties is a classic ship detection method [10]. Early methods of ship detection mostly focused on image pre-processing, which is closely dependent on the Otsu threshold method, the maximum entropy threshold method, and the histogram threshold method. These are highly reliant on the state of the sea surface and require images with calm, homogeneous, and low overall grey values of the sea surface [11]. The rapid development of remote sensing technology has enabled real-time target detection using remote sensing [12]. A wide variety of feature extraction algorithms have been continuously applied, using properties such as shape features, texture features, corner features, and so on. Zhu et al proposed a method based on edge information [13] that mainly utilises the features of high-resolution optical satellite remote sensing images and obtains the target candidate area based on target texture information and shape analysis. Zhang et al proposed S-CNN to combine the pointed shape of the bow with the linear shape of the hull, which improved the target recognition results of remote sensing for target ships [14]. However, in the context of this article, a sporting vessel will accompany the wake trail increasing the difficulty in identifying with such features. Currently, deep neural networks are widely used in image processing [15], and many methods based on CNN have achieved excellent results. Tang et al. used compressed domain processing to extract the candidate regions of the ship target, followed by the uses of a deep neural network to perform feature representation, classification, and recognition of the candidate regions [16]. Yang et al. proposed a framework called rotation dense feature pyramid networks, which can detect ships effectively in different scenes, including ocean and port scenes [8]. Multi-scale image pyramids are computationally intensive in the establishment and operation of scale space, and the feature map of the last layer in these algorithm networks is too small, causing detection and regression to fail. Therefore, their detection capability for small targets is not sufficient. Image semantic segmentation aims to classify every pixel of given images [17], and the feature map of the last layer is as big as the original image. Remote sensing ship targets are mostly small targets, and the semantic segmentation method outputs pixel-level high-precision segmentation effects. Further, semantic segmentation is significant because it provides information not only for identification but also for the position of the target in the image. In our process of remote sensing ship detection and segmentation, a semantic segmentation method is applied to recognise ships and find the locations of ships at the pixel level. The main contributions of our paper are as follows: First, we use the semantic segmentation method based on CNN to identify the ship area. By using CNN, the features of targets can be extracted automatically, which avoids manually performing the complex feature selection and extraction process. We use different rate size atrous convolution layers in a common CNN, which is used to enlarge the receptive field and capture more spatial features of images from different ranges of neighbourhoods. As ship targets have fewer features in remote sensing images, we use the attention model to combine the lower-layer information and high-layer information to improve the detail feature capability of boundaries. To detect remote sensing ship target, which is small in the image, we improve the pooling layer by using the pass-through layer. 2 Ship detection background Traditional remote sensing ship target detection has certain limitations. This chapter describes the difficulties and knowledge of neural networks in ship detection. 2.1 Ship analysis and method Remote sensing ship detection differs from the detection of familiar objects. The size, direction, and shape of ships vary greatly. Area detection, as well as shape and corner feature detection in ship detection for ships, is limited. Additionally, the wake characteristics of a moving ship introduce interference to the traditional detection method. CNN has many convolution kernels, each of which is designed to extract different features. As the convolutional layer deepens, deeper features can be obtained. Regardless of which direction the bow is pointing, CNN has higher adaptability. However, traditional networks rely on manual tuning, there are significant limitations, but CNN continuously adjusts its parameters during the training process. 2.2 Deep learning background 2.2.1 Convolutional neural network CNN is a typical example of neurological influencing on deep learning, which is a current research hotspot in the field of image processing [18]. CNN is dedicated to processing neural networks with structures similar to image data and can directly use multi-dimensional images for network input [19]. The parameters of the convolutional layers are as follows: Kernel size: Define the field of view of convolution. Stride: Define the step size of the kernel. Padding: Define the method for handling the border of a sample. CNN uses the spatial information of images with filters by convolving the kernel and the image pixels. CNN is highly efficient for extracting and is widely used in image processing and machine learning. Neural network output layers are defined as (1)where l is the layer of net, i is the input unit i, j is the hidden layer unit j, w denotes the weights of input layer unit i and the hidden layer unit j, b denotes the offset of hidden layer j, and f denotes a rectified linear unit (ReLU) function. Pooling: In CNN, sub-sampling is also known as pooling. The pooling function replaces the output of the corresponding location in the network using the overall statistical features of the neighbouring regions. Pooling reduces model complexity, reduces the number of parameters, and enhances generalisation capabilities. It has a particular local translation invariance to the input. For input with a small amount of translation, the pooling can be approximated, and the pooling can improve the statistical efficiency of the network to some extent. Pooling is a means of reducing the image by half in CNN. There are usually three types of pooling methods, those being max, mean, and L2 pooling, and we often use max pooling in network layers. Upsampling: Upsampling is the inverse of pooling and doubles the size of the image. We know that after the pooling, each filter loses three weights. These weights cannot be restored, but in the upsampling layer, the relative position of the pooling filter used in the pooling process can be obtained. Therefore, upsampling first doubles the input feature map and then uses the data of the input feature map according to the pooling indices. 2.2.2 Atrous convolution Also known as dilated convolution, atrous convolution has been shown to be effective for image segmentation and recognition. The advantage of atrous convolution is that the receptive field is increased in the absence of pooling loss information, so each convolution output contains a larger range of information. Atrous convolution reduces the size of the kernel and could save memory. It is effective for images when global information or speech text requires long-sequence information dependencies [10, 20-22]. Atrous convolution has been successfully used in semantic segmentation and object detection to extract denser feature maps. Atrous convolution has holes in kernels when compared with common convolution layers. The size of holes is determined according to the dilution rate, which defines a spacing between the values in a kernel. A 3 × 3 kernel with a dilution rate of 2 will have the same field of view as a 5 × 5 kernel, while only using nine parameters. Higher rates produce an expanded kernel, which can capture more context information from the feature maps of previous layers [23]. Examples of atrous convolution with different dilation rates are shown in Fig. 1. Fig. 1Open in figure viewerPowerPoint Atrous convolution processes: The kernel size is 3 × 3. The rates size are 1, 6, and 12. Common convolutional neural networks are equivalent to that of rate = 1. As the dilation rate increases, the field-of-view is enlarged We assume that is a 1D input signal, and the convolutional kernel is k × k, is the filter of length k, r is rate parameter corresponding to the stride with which we sample the input signal, and is the output of atrous convolution. The atrous convolution can be expressed as (2) 2.2.3 ResNet ResNet was introduced in 2015 and won first place in the ImageNet competition classification task [24]. ResNet can efficiently deepen a CNN network, making extracted features more complex. ResNet updates the parameters with gradients when the residuals vary greatly. When the residuals change lightly, it can also guarantee that the gradient will not disappear. In addition to this, the transfer of the front layer features is equivalent to the combination of different levels of features. ResNet extracts more complex features while preserving the simpler features of the front layer, accelerates convergence, and is easier to train. 2.3 Passthrough layer The passthrough layer YOLOv2 was initially derived from YOLO V2 and is used to extract finer feature maps. This is achieved by extracting each 2×2 local area of the previous layer and then converting them into a channel dimension [25]. For example, a feature map of 26×26×512 becomes a new feature map of 13×13×2048 after being processed by the passthrough layer. The feature map size is divided by 4, while the channels are multiplied by 4. The specific process is as follows. 3 Methodology 3.1 Attention feature map The attention model introduced in this paper has a similar structure to ResNet. Unlike ResNet, the attention model not only adds operations but also sends critical information to the back layer, which is designed to simulate the working process of the human brain [26]: when people observe an image, they pay attention to the whole image. It is unique and there is a particular weight distinction. With this feature, the focus is given only to the parts that are used and enhance during the image training test [25]. The details are implemented as shown in Figs. 2 and 3: (3) Fig. 2Open in figure viewerPowerPoint Pass-through layer. The left side of Fig. 5 shows the input, and the numbers 1, 2, 3, and 4 represent the first two pixels of the first line and the first two pixels of the second line, respectively. On the right side of the figure is the Reorg result, with the input pixels in the first pixel Fig. 3Open in figure viewerPowerPoint Attention model. The traditional network has only the trunk branch portion on the right. ResNet adds paths and superimposes on the left side. The attention model improves the single overlay, adding the soft mask branch section on the left, consisting of bottom-up and top-down structures. First, pooling is used to extract high-level features and increase the receptive field and then expands the size to the same size as the original input using upsampling to the same degree as that of the downsampling. Next, the soft mask branch is combined with the output of the trunk branch to enhance meaningful information. This kind of structure draws on ResNet's identity mapping idea, combining the residual structure, and can achieve deeper feature extraction and improve performance. is the output of the soft mask branch, is the output of the trunk branch is the final output of the attention model. The output is more significant than the original feature map. This output inherits the excellent performance of ResNet and increases the discriminability of features. To observe the effectiveness of the attention model, this paper observes the feature map of the traditional FCN network and the improved attention model infrared pedestrian target. The specific results are shown in Fig. 4 Fig. 4Open in figure viewerPowerPoint Original images, SegNet feature map, and attention feature map The figure shows a comparison of the two methods. The original images, traditional method results, and attention model results are located on the left, center, and right, respectively. The attention model has a significant enhancement to the salient areas, especially in the ship area. As the number of network layers continues to deepen, the attention of the target area is continuously concentrated, which helps to better identify and segment the pedestrian target area and can detect the target effectively. 3.2 Input and output To display the experimental results more intuitively, the network input is set to traditional RGB format. The detection result is output as a 2D image. Before the network training, we need to get the target annotation information. The network output result is marked as a binary image, with the target area being marked as 1, while the position outside the target area is marked as 0 (Fig. 5). Fig. 5Open in figure viewerPowerPoint Original image and its label 3.3 Training The input image size is the same as that of 256×256×3. After multiple downsampling and upsampling operations, the result of 256×256 is output. The network continuously optimises the network through reverse delivery during the training process. For the two-class detection method of this paper, binary cross entropy is chosen as the loss function. At the same time, Adam method from Kingma and Ba is used instead of the stochastic gradient descent method to continuously reduce the loss function. Each iterative learning rate after Adam bias correction is limited to a specific range, and the parameters become smooth. To prevent overflow, we use the following improved binary cross entropy formula: (4) 3.4 Proposed structure This section provides the details of the proposed method for segment remote sensing ships. First, we describe the SegNet model. Then, we introduce the CNN model and detailed its principles. The encoder uses the first 13-layer convolutional network of VGG16. Each encoder layer corresponds to a decoder layer. The output of the final decoder layer is sent to the soft-max classifier to generate each pixel independently. Each encoder layer consists of several convolutional layers and a pooling layer [27]. The output is equivalent to downsampling with a coefficient of 2. Due to the superposition of the maximum pooling and sub-sampling, the boundary detail loss is increased, so it must be included in the coding feature map. Boundary information is captured and stored before sub-sampling [28]. Multi-scale features can be extracted by using atrous convolution [29]. The proposed novel use of deep learning networks aims to improve remote sensing ship detection and segmentation. The structure of the improved network can be seen in Fig. 6. Fig. 6Open in figure viewerPowerPoint Network structure of remote sensing ship segmentation. The structure is mainly divided into four parts, including image input, encoder, decoder, and output. For the remaining more detail features, we combine more pooling layer results in the encoder and decoder using atrous convolution. Blue shading marks convolution, batch normalisation, and the ReLU function. Grey shading marks the pooling layer, while green shading marks upsampling. We define the pink layer as atrous convolution and purple as binary cross entropy 3.5 Parameters in proposed struture We start our net from a deep network, pretrained on VGG16 referring to the architecture of SegNet. The fully connected layers that were used for classification are removed, but the batch normalisation layers remain. In this network structure, the pooling layer and the convolution layer on the left side extract the high-dimensional features, and the spatial dimensions of the produced feature maps are made smaller by pooling. This part is called encoder. The right side includes deconvolution and upsampling, which enlarges the image, and we call this the decoder. The upsampling operation is used in the super-resolution reconstruction. Finally, the maximum values of different classifications re-output through binary cross entropy. The detail parameters in the proposed net are as follows (Table 1). Table 1. Parameters in the proposed structure Layer Input Kernel size Stride Padding Output 256,256,3 3,3,64 1 same 256,256,64 256,256,64 3,3,64 1 same 256,256,64 pool1 256,256,64 2,2 2 — 128,128,64 128,128,64 3,3,128 1 same 128,128,128 128,128,128 3,3,128 1 same 128,128,128 pool2 128,128,128 2,2 2 — 64,64,128 64,64,128 3,3,256 1 same 64,64,256 att 64,64,256 — — — 64,64,256 64,64,128 3,3,256 1 same 64,64,256 64,64,128 3,3,256 1 same 64,64,256 64,64,128 3,3,256 1 — 64,64,256 pool 64,64,256 2,2 2 — 32,32,256 32,32,256 3,3,512 1 same 32,32,512 att 32,32,512 — — — 32,32,512 32,32,512 3,3,512 1 same 32,32,512 32,32,512 3,3,512 1 same 32,32,512 32,32,512 — — — 32,32,512 passthrough4 32,32,512 2,2 — — 16,16,512×4 16,16,512*4 3,3,512 1 same 16,16,512 att 16,16,512 — — — 16,16,512 16,16,512 3,3,512 1 same 16,16,512 16,16,512 3,3,512 1 same 16,16,512 16,16,512 — — — 16,16,512 passthrough5 16,16,512 2,2 — — 8,8,512×4 atrous conv1 16,16,512×4 3,3,512 1 same 16,16,512 atrous conv2 16,16,512×4 6,6,512 1 same 16,16,512 atrous conv3 16,16,512×4 9,9,512 1 same 16,16,512 4 Experiments and evaluation The main process is described as follows. First, training samples and test samples are prepared. Ship samples are then labelled with LabelMe. Training sets and test datasets are then made, and ship models are trained with training samples. Evaluation indicators are then selected for ship target recognition. Finally, the test set recognition results are evaluated (Fig. 7). Fig. 7Open in figure viewerPowerPoint Image segmentation processing. Training images and training labels are first sent to convolutional neural networks. When the datasets are trained, we obtain the encoder and decoder weights 4.1 Experiment 1: Datasets and training Dataset: We tested our model on a remote sensing ship dataset from Google Earth of famous military harbours, such as India's Kadamba Naval Base, Japan's Yokosuka Naval Port, China's Dalian Naval Port, and Taiwan's largest military port, Zuoying. We use a total of 21,468 images from GoogleEarth. The remote sensing images have resolutions between 2 and 0.4 m. However, because the amount of ship images from this is not sufficient to get target features in the training process, we combine these with 1061 images from the HRSC2016 datasets [15] and 3982 images from Kaggle Ship Detection Challenge. Further, we expanded the dataset by rotating the images using , , and . Ground truth images are required to realise ship target segmentation. Labelme is an appropriate tool for this purpose and can separate targets from the background at the pixel level. Then, we used 70% as the training set and 30% as the testing set. Training platform and parameters: We implemented the TensorFlow neural network learning framework on a platform running the 64-bit Ubuntu 16.04 LTS platform operating system with Python 3.5 and OpenCV 3.0.9, an Intel Core i7 CPU, and 16 GB of RAM. We built a semantic segmentation network for model training and testing of remote sensing ship target recognition, and our proposed model is based on TensorFlow-GPU. The related parameter settings in all of the proposed networks are consistent. The initialisation is random [30]. 4.2 Experiment 3: Segmentation results and evaluation Segmentation result: After training, testing, and comparing, we obtained the result shown in Fig. 8. To see the detailed results more clearly and intuitively, we select the larger vessel target segmentation results for observation. We can see from Fig. 8 that the proposed method has improved results relative to SegNet for area and boundaries. Fig. 8Open in figure viewerPowerPoint Image segmentation results. The five columns from left to right show the original images, ground truth, SegNet results, proposed results, and proposed final results Evaluation: To further verify the effectiveness of the research method, we use the accuracy, recall, and F-measure parameters to quantitatively evaluate the final recognition results of the ships. Accuracy is the measure of the proportion of information that is correctly extracted relative to correct information that is not extracted, and the recall is used to measure how much of the extracted information is correct. Accuracy and recall are essential parameters in the identification of infrared vessels. The identification of vessel targets requires both high accuracy and high recall rate. Therefore, the F-measure is also introduced to measure it. The formula is as follows: (5) (6) (7)where TP is true positive, FP is false positive, TN is true negative, and FN is false negative. Further, we use two metrics, pixel accuracy (PA) and mean intersection-over-union (MIoU) [30], for evaluation. For ease of explanation, we assume the parameters are as follows. There are a total of k + 1 classes (from L0 to Lk, which contain an empty class or background), and represents the number of pixels that belong to class i but are predicted to belong to the class j. That is, represents the true quantity, whereas and are interpreted as false positive and false negative, respectively, although they are both of the sums of a false positive and false negative. Pixel accuracy, which is the simplest metric, is the ratio of the correct pixels to the total pixels and can be expressed by the following formula: (8)However, our algorithm cannot be 100% completely matched with the manually labelled data, there is a positioning accuracy evaluation formula: Intersection-over-union (IoU). It defines the ratio of ground truth G and candidate area c. The ideal situation is complete overlap, that is, the ratio would be 1 (9)The segmentation result using the proposed method has good performance. We can get the result as follows (Table 2). Table 2. Performance of segmentation result Method Precision Recall F1-Score IoU PA segNet 0.698 0.687 0.703 0.738 0.896 proposed 0.750 0.738 0.744 0.783 0.935 In order to better display the experimental results, we select ships with different sizes, different types, different directions, and wakes, as shown in Fig. 9. It can be seen from the test results that docked ships results in better detection areas. In addition, ships with weak contrast as well as sporting ships are also well detected. Ships with large size gaps are in the same field of view and our method can detect all of them. Fig. 9Open in figure viewerPowerPoint Small and multi-scale ship target detection result 5 Conclusion We proposed a remote sensing ship detection method based on CNN. First, we use the attention model with traditional neural networks to retain more detail features. Second, to retain more high-level information, we put the passthrough layer at the end of the encoder. 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Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition., Salt Lake City, Utah, 2018, pp. 4013– 4022 Citing Literature Volume13, Issue6May 2019Pages 1016-1022 FiguresReferencesRelatedInformation