Airport Detection Method Research Articles

Fast Line Segment Detection and Large Scene Airport Detection for PolSAR

In this paper, we propose a fast Line Segment Detection algorithm for Polarimetric synthetic aperture radar (PolSAR) data (PLSD). We introduce the Constant False Alarm Rate (CFAR) edge detector to obtain the gradient map of the PolSAR image, which tests the equality of the covariance matrix using the test statistic in the complex Wishart distribution. A new filter configuration is applied here to save time. Then, the Statistical Region Merging (SRM) framework is utilized for the generation of line-support regions. As one of our main contributions, we propose a new Statistical Region Merging algorithm based on gradient Strength and Direction (SRMSD). It determines the merging predicate with consideration of both gradient strength and gradient direction. For the merging order, we set it by bucket sort based on the gradient strength. Furthermore, the pixels are restricted to belong to a unique region, making the algorithm linear in time cost. Finally, based on Markov chains and a contrario approach, the false alarm control of line segments is implemented. Moreover, a large scene airport detection method is designed based on the proposed line segment detection algorithm and scattering characteristics. The effectiveness and applicability of the two methods are demonstrated with PolSAR data provided by UAVSAR.

A Feature Fusion Airport Detection Method Based on the Whole Scene Multispectral Remote Sensing Images

Being one of the most important infrastructures, airports play a vital role in both civil fields and military fields. However, detect airports directly based on the whole scene remote sensing images (RSIs) with complex background remains challenging. To address this issue, this article proposes a method that mainly combines spectral features and geometric features of airports with concrete runways to detect multiple airports simultaneously from a whole scene multispectral image with medium-high spatial resolution and with comparatively few bands (contains blue, green, red, and near-infrared bands). Specifically, a decision tree algorithm was developed based on the analysis of spectral features to extract main concrete areas within the whole RSI. Then, the geometric features are used to aim at extracting the point marks of candidate airports. The influence of different image spatial resolutions of the proposed method is explored and the detection effect and processing efficiency of proposed method is verified based on whole scene RSIs with complex background. The analysis of experimental results shows that Sentinel-2 images is more suitable for airport detection than Gaofen-6 and Landsat-8 images based on the proposed method. In addition, the proposed method provides high-accuracy detection of category Ⅳ airports based on Sentinel-2 images with different background complexity in experimental areas indicate the proposed method has a high robust and a good applicability. Finally, run-time test of the proposed method was conducted, and it demonstrates the proposed method has the higher processing efficiency when applying to regional airport detection.

Airport Detection in SAR Images Via Salient Line Segment Detector and Edge-Oriented Region Growing

Airport detection in synthetic aperture radar (SAR) images has attracted much concern in the field of remote sensing. Affected by other salient objects with geometrical features similar to those of airports, traditional methods often generate false detections. In order to produce the geometrical features of airports and suppress the influence of irrelevant objects, we propose a novel method for airport detection in SAR images. First, a salient line segment detector is constructed to extract salient line segments in the SAR images. Second, we obtain the airport support regions by grouping these line segments according to the commonality of these geometrical features. Finally, we design an edge-oriented region growing (EORG) algorithm, where growing seeds are selected from the airport support regions with the help of edge information in SAR images. Using EORG, the airport region can be mapped by performing region growing with these seeds. We implement experiments on real radar images to validate the effectiveness of our method. The experimental results demonstrate that our method can acquire more accurate locations and contours of airports than several state-of-the-art airport detection algorithms.

Airport Detection Based on Improved Faster RCNN in Large Scale Remote Sensing Images

The extensive acquisition and using of high-resolution remote sensing images have greatly promoted the development of airport detection. However, due to the complex shape, background and different scale of the airport location, the real-time and accuracy of airport detection are also facing major challenges. These factors can reduce the detection accuracy. To solve the above problems, we propose an improved faster region-based convolutional neural network (RCNN) detection method for airport detection in large scale remote sensing images. Multi-scale training is applied to faster RCNN to enhance the robustness of network for detecting airport with different sizes. Meanwhile, we adopt the modified multitask loss function to improve the accuracy of airport detection. Online hard example mining strategy is introduced to decrease the redundant negative samples in the training process. Then the non-maximum suppression method is used to remove the redundant boxes of the detected airport. Finally, we conduct sufficient experiments with the airport data obtained from Google Earth and make comparison with the state-of-the-art airport detection methods. The results show that the proposed method can accurately detect different airports under complex background with high detection rate, low false alarm rate and short running time.

AIRPORT RUNWAY SEMANTIC SEGMENTATION BASED ON DCNN IN HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

Abstract. Due to the diverse structure and complex background of airports, fast and accurate airport detection in remote sensing images is challenging. Currently, airport detection method is mostly based on boxes, but pixel-based detection method which identifies airport runway outline has been merely reported. In this paper, a framework using deep convolutional neural network is proposed to accurately identify runway contour from high resolution remote sensing images. Firstly, we make a large and medium airport runway semantic segmentation data set (excluding the south Korean region) including 1,464 airport runways. Then DeepLabv3 semantic segmentation network with cross-entropy loss is trained using airport runway dataset. After the training using cross-entropy loss, lovasz-softmax loss function is used to train network and improve the intersection-over-union (IoU) score by 5.9%. The IoU score 0.75 is selected as the threshold of whether the runway is detected and we get accuracy and recall are 96.64% and 94.32% respectively. Compared with the state-of-the-art method, our method improves 1.3% and 1.6% of accuracy and recall respectively. We extract the number of airport runway as well as their basic contours of all the Korean large and medium airports from the remote sensing images across South Korea. The results show that our method can effectively detect the runway contour from the remote sensing images of a large range of complex scenes, and can provide a reference for the detection of the airport.

Multi-Layer Abstraction Saliency for Airport Detection in SAR Images

The detection of airports using synthetic aperture radar (SAR) images has attracted considerable attention. Traditional methods easily result in inaccurate detection due to the complex scenes and multiplicative speckle noise. Therefore, airport detection from SAR images is still a challenging task. In order to limit the influence of unnecessary and attractive details and noise, we propose a multi-layer abstraction saliency model for airport detection in SAR images in this paper. Specifically, we first obtain airport support regions and superpixels in the first layer. According to the dis-similarity between foreground and background superpixels, airport components are explored by iterative refinement for each airport support region in the second layer. In the third layer, airport adobes are produced by clustering. Based on the characteristics of an airport in SAR images, we propose three saliency cues, including local contrast (LC), adobe deformation (AD), and global uniqueness (GU), to obtain adobe-level saliency. Furthermore, we assign saliency to each pixel by Bayesian inference. Finally, we can explore airport location using integrated saliency map. The proposed approach is tested on an airport data set collected from Gaofen-3, TerraSAR, and RadarSat. Our method achieves 88.89% detection rate. The experimental results demonstrate that the proposed algorithm is effective and outperforms the previously airport detection methods. The code will be available at https://github.com/NengyuanLiu/MyAirportSaliency .

A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning

Airports have a profound impact on our lives, and uncovering their distribution around the world has great significance for research and development. However, existing airport databases are incomplete and have a high cost of updating. Thus, a fast and automatic worldwide airport detection method can be of significance for global airport detection at regular intervals. However, previous airport detection studies are usually based on single remote sensing (RS) imagery, which seems an overwhelming burden for worldwide airport detection with traversal searching. Thus, we propose a hierarchical airport detection method consisting of broad-scale extraction of worldwide candidate airport regions based on spatial analysis of released RS products, including impervious surfaces from FROM-GLC10 (fine resolution observation and monitoring of global land cover 10) product, building distribution from OSMs (open street maps) and digital surface model from AW3D30 (ALOS World 3D—30 m). Moreover, narrow-scale aircraft detection was initially conducted by the Faster R-CNN (regional-convolutional neural networks) deep learning method. To avoid overestimation of background regions by Faster R-CNN, a second CNN classifier is used to refine the class labeling with negative samples. Specifically, our research focuses on target airports with at least 2 km length in three experimental regions. Results show that spatial analysis reduced the possible regions to 0.56% of the total area of 75,691 km2. The initial aircraft detection by Faster R-CNN had a mean user’s accuracy of 88.90% and ensured that all the aircrafts could be detected. Then, by introducing the CNN reclassifier, the user’s accuracy of aircraft detection was significantly increased to 94.21%. Finally, through an experienced threshold of aircraft number, 19 of the total 20 airports were detected correctly. Our results reveal the overall workflow is reliable for automatic and rapid airport detection around the world with the help of released RS products. This research promotes the application and progression of deep learning.

End-to-End Airport Detection in Remote Sensing Images Combining Cascade Region Proposal Networks and Multi-Threshold Detection Networks

Fast and accurate airport detection in remote sensing images is important for many military and civilian applications. However, traditional airport detection methods have low detection rates, high false alarm rates and slow speeds. Due to the power convolutional neural networks in object-detection systems, an end-to-end airport detection method based on convolutional neural networks is proposed in this study. First, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learning for airport images using a limited amount of data. Second, to further improve the detection rate and reduce the false alarm rate, the concepts of “divide and conquer” and “integral loss’’ are introduced to establish cascade region proposal networks and multi-threshold detection networks, respectively. Third, hard example mining is used to improve the object discrimination ability and the training efficiency of the network during sample training. Additionally, a cross-optimization strategy is employed to achieve convolution layer sharing between the cascade region proposal networks and the subsequent multi-threshold detection networks, and this approach significantly decreases the detection time. The results show that the method established in this study can accurately detect various types of airports in complex backgrounds with a higher detection rate, lower false alarm rate, and shorter detection time than existing airport detection methods.

Airport Detection on Remote Sensing Images Using Fater Region-based Convolutional Neural Network

An airport detection method on remote sensing images based on transfer learning and hard example mining is proposed. We use Faster region-based convolutional neural network framework with end-to-end advantage instead of the sliding windows and artificial features in the existing traditional methods. Transfer learning is used to solve this problem that the number of airport remote sensing data itself is limited. Hard example mining is used to make full use of hard examples. As a result, it can improve the ability to discriminate objects and make training more efficient. The experimental results show that the proposed method can detect different types of airports accurately under complex backgrounds. Compared with other airport detection methods, the proposed method has higher detection rate 92.6%, lower false-alarm rate 15% and shorter average processing time 0.2s.

Fast Detection of Airports on Remote Sensing Images with Single Shot MultiBox Detector

This paper introduces a method for fast airport detection on remote sensing images (RSIs) using Single Shot MultiBox Detector (SSD). To our knowledge, this could be the first study which introduces an end-to-end detection model into airport detection on RSIs. Based on the common low-level features between natural images and RSIs, a convolution neural network trained on large amounts of natural images was transferred to tackle the airport detection problem with limited annotated data. To deal with the specific characteristics of RSIs, some related parameters in the SSD, such as the scales and layers, were modified for more accurate and rapider detection. The experiments show that the proposed method could achieve 83.5% Average Recall at 8 FPS on RSIs with the size of 1024*1024. In contrast to Faster R-CNN, an improvement on AP and speed could be obtained.

Multiresolution Airport Detection via Hierarchical Reinforcement Learning Saliency Model

Traditional airport detection methods usually utilize geometric characteristics to locate targets, but they are not suitable for low-resolution remote sensing images. Taking both low and high resolution into account, we present a novel hierarchical reinforcement learning (HRL) saliency model to detect airport target. Different from conventional saliency models focusing on nature images, our HRL model is more effective for multiresolution remote sensing images. According to airport characteristic, we design a reinforcement learning structure to suppress background and highlight interesting airport regions level by level. To generate a final saliency map, we fuse bottom–up region features with top–down line feature based on target attribute, which can restrain other salient regions except for airports. Moreover, a learning stop criterion based on latent Dirichlet allocation (LDA) topic model is proposed at each level to judge the state of saliency detection, thus learning process can be adaptively controlled. Besides, a back-level propagation mechanism is employed to reinforce airport target between levels. HRL saliency model can take the advantage of hierarchical structure to quickly locate interest regions in remote sensing images with large cover area. Furthermore, HRL is robust for illumination and resolution variety. Extensive experimental results on a remote sensing dataset containing 730 images of 40 different airports demonstrate that the proposed HRL model outperforms 18 state-of-the-art saliency models in terms of two popular evaluation measures. Besides, it has significantly higher detection rate than other six airport detection methods.

Airport Detection and Aircraft Recognition Based on Two-Layer Saliency Model in High Spatial Resolution Remote-Sensing Images

Efficient airport detection and aircraft recognition are essential due to the strategic importance of these regions and targets in economic and military construction. In this paper, a novel airport detection and aircraft recognition method that is based on the two-layer visual saliency analysis model and support vector machines (SVMs) is proposed for high-resolution broad-area remote-sensing images. In the first layer saliency (FLS) model, we introduce a spatial-frequency visual saliency analysis algorithm that is based on a CIE Lab color space to reduce the interference of backgrounds and efficiently detect well-defined airport regions in broad-area remote-sensing images. In the second layer saliency model, we propose a saliency analysis strategy that is based on an edge feature preserving wavelet transform and high-frequency wavelet coefficient reconstruction to complete the pre-extraction of aircraft candidates from airport regions that are detected by the FLS and crudely extract as many aircraft candidates as possible for additional classification in detected airport regions. Then, we utilize feature descriptors that are based on a dense SIFT and Hu moment to accurately describe these features of the aircraft candidates. Finally, these object features are inputted to the SVM, and the aircraft are recognized. The experimental results indicate that the proposed method not only reliably and effectively detects targets in high-resolution broad-area remote-sensing images but also produces more robust results in complex scenes.

Efficient Airport Detection Using Line Segment Detector and Fisher Vector Representation

In this letter, a two-stage method for airport detection on remote sensing images is proposed. In the first stage, a new algorithm composed of several line-based processing steps is used for extraction of candidate airport regions. In the second stage, the scale-invariant feature transformation and Fisher vector coding are used for efficient representation of the airport and nonairport regions and support vector machines employed for classification. In order to evaluate the performance of the proposed method, extensive experiments are conducted on airports around the world with different layouts. The measures used in the evaluation are accuracy, sensitivity, and specificity. The proposed method achieved an accuracy of 94.6%, which was benchmarked with two previous methods to prove its superiority.

A Novel Airport Detection Method via Line Segment Classification and Texture Classification

Airports are one of the most important traffic facilities; thus, airport detection is of great significance in economic and military construction. This letter proposes a novel method for airport detection, with the entire algorithm based on line segment classification and texture classification. First, a fast line segment detector is applied to extract the line segments in images and compute the features of these line segments. Then, the line segments are discriminated by a trained runway line classifier, and the regions of interest (ROIs) are extracted from the line segments, which are classified as runway lines. Finally, whether the ROI is actually an airport is determined by analyzing the classification results of the image blocks. This method is unique in terms of the computing of line segment features and line segment classification. Experimental results demonstrate the effectiveness and robustness of the proposed method.

Airport detection in remote sensing images: a method based on saliency map

The detection of airport attracts lots of attention and becomes a hot topic recently because of its applications and importance in military and civil aviation fields. However, the complicated background around airports brings much difficulty into the detection. This paper presents a new method for airport detection in remote sensing images. Distinct from other methods which analyze images pixel by pixel, we introduce visual attention mechanism into detection of airport and improve the efficiency of detection greatly. Firstly, Hough transform is used to judge whether an airport exists in an image. Then an improved graph-based visual saliency model is applied to compute the saliency map and extract regions of interest (ROIs). The airport target is finally detected according to the scale-invariant feature transform features which are extracted from each ROI and classified by hierarchical discriminant regression tree. Experimental results show that the proposed method is faster and more accurate than existing methods, and has lower false alarm rate and better anti-noise performance simultaneously.

Airport Detection From Large IKONOS Images Using Clustered SIFT Keypoints and Region Information

This letter presents a new method for airport detection from large high-spatial-resolution IKONOS images. To this end, we describe airport by a set of scale-invariant feature transform (SIFT) keypoints and detect it using an improved SIFT matching strategy. After obtaining SIFT matched keypoints, to both discard the redundant matched points and locate the possible regions of candidates that contain the target, a novel region-location algorithm is proposed, which exploits the clustering information from matched SIFT keypoints, as well as the region information extracted through the image segmentation. Finally, airport recognition is achieved by applying the prior knowledge to the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy.

One airport detection method based on support vector machine

This paper proposes a novel airport detection method, which integrates the texture features and shape features of the airport. Eight texture features, such as the mean of the region, the deviation of the region, the smoothness of the region, the skewness of a histogram, the uniformity of the region, the randomness of the region, the mean of the gradient image and the deviation of the gradient image, are used to represent the features of the region. In this method, first the long lines are detected and the regions where the lines locate are segmented. Second, support vector machine (SVM) based on Gaussian kernel is used as a classifier which discriminates the runway from other candidate regions. Experimental results show that the error rate of the proposed method is lower than those of conventional methods which detect airport only by the shape feature of runway. The detection accuracy of the proposed method is nearly ten times higher than that of Liu’s methods, and the method has favorable speed for a real-time system.