Automatic Satellite-Based Vessel Detection Method for Offshore Pipeline Safety

Transcription

1 Automatic Satellite-Based Vessel Detection Method for Offshore Pipeline Safety P. Masoud 1, L. Song 2, and N. Eldin 3 1 Department of Construction Management, University of Houston, 111 T1, Houston, Texas ; PH (713) ; FAX (713) ; masoud.poshtiban@gmail.com 2 Department of Construction Management, University of Houston, 111D T1, Houston Texas, , PH (713) ; FAX (713) ; lsong@uh.edu 3 Department of Construction Management, University of Houston, 110M T1, Houston Texas, , PH (713) ; FAX (713) ; neldin@uh.edu ABSTRACT Offshore pipelines are vital infrastructure systems for oil and gas transportation. Statistics around the globe confirm that third-party threats, such as vessel anchoring, fishing, and offshore construction, contribute the most to offshore pipeline damages and are the number one cause of death, injury, and pollution. This research studies satellite imagery and its application in automating vessel detection for the purpose of offshore pipeline protection. Current methods of relying on highresolution satellite images lead to a high implementation cost and less efficient image processing. This paper proposes a method of utilizing lower resolution satellite images for vessel detection in offshore pipeline safety zones. It applies a combination of cascade classifier and color segmentation method as well as a unique colorcoding scheme to achieve an accurate and efficient satellite image processing procedure. The proposed method was tested on 150 Google Earth satellite images with an average detection rate of 94% for large and medium vessels and an average false alarm rate of 19%. BACKGROUND Offshore pipelines are vital infrastructure systems that play an important role in transporting gases and liquids over long distances across the ocean. Offshore pipelines must be constantly and reliably operated and monitored to ensure maximum operating efficiency and safety. Offshore pipelines generally transport perilous pressurized products and operate in hostile ocean environments, including current dynamics, geo-hazards, as well as third-party threats. Leaks and bursts in such 1

2 pipeline networks cause significant economic losses, service interruption, and can also lead to enormous negative impact on the public and environment. There are several causes of offshore pipeline damages including construction damages, operation flaws, design inaccuracy, material weaknesses, pipe corrosion, ground movements, and third-party damages. In particular, third-party damages refer to accidental damages caused by activities not associated with the pipeline operations, including vessel anchoring, collision, fishing/trawling, dredging, offshore construction, and dropped objects. According to a UK study (MacDonald 2003), 51% of offshore pipeline accidents are caused by third-party damages. Moreover, thirdparty maritime activities are also recognized as the second major cause of offshore pipelines failure (Woodson 1990). Past studies also confirmed that third-party damages are the number one cause of death, injury, damage, and pollution (NRC 1994). PROBLEM STATEMENT Previous research on oil and gas pipelines monitoring has focused on the prefailure and leak detection techniques using sensing technologies, such as fiber optic, acoustic, ultrasonic, and magnetic sensors, or in-line inspection methods (e.g. smart pigging). However, they are reactive in nature and only confirm damages after the fact. Traditional pipeline patrolling (e.g. spot check on vessel or aircraft) is costly and tedious due to the spatial distribution of pipeline networks. Gatehouse (2010) developed a third-party vessel tracking technique based on Automatic Identification System (AIS). AIS is an automatic tracking system used on vessels for identifying and locating vessels. Vessels equipped by AIS transponders communicate data electronically with other nearby vessels and AIS base stations. Its primary purpose is to avoid collisions in poor visibility situations, but it can also be used for proactively monitoring vessels activities in pipeline safety zones. When a vessel is approaching or entering a pipeline safety zone, the operator can be notified and warned if the monitoring system detects a violating behavior. The detection algorithm is based on heuristic rules obtained from experts. However, two limitations were noted: (1) data from vessels far away from shore are not available due to limited coverage of AIS stations; and (2) vessels may not be equipped with tracking devices, thus tracking data is unavailable. Satellite sensing can complement to AIS because of its global coverage and the capability to identify vessels not equipped with AIS transponders. Over one thousand satellites fly over our planet every day. They provide global coverage of earth surface activities, including weather conditions, land movements, and traffic (onshore and offshore). Remote sensors attached to satellites collect data by detecting the energy that is reflected from earth including radio, microwave, infrared, visible optical light and multispectral signals. Surface objects (e.g., vessels and fishing boats) can be detected and classified by analyzing these sensory data. Furthermore, image processing and computer vision techniques can be applied to automate the identification of third-party entities. A sampling of their presence, frequency, and traffic density can supplement AIS data for pipeline risk management. 2

3 The long-term goal of this research is to integrate AIS and satellite imaging for pipeline safety zone monitoring. The objective of this study is to develop a costeffective method to automatically identify vessels from optical satellite images. Current methods of relying on high-resolution satellite images lead to a high implementation cost and less efficient image processing. This paper proposes a method of utilizing lower resolution satellite images for vessel detection. Along with AIS, this method will provide third-party activity statistics to support more accurate new pipeline route design and prioritization of maintenance effort. The section below describes related works. It is followed by the proposed methodology and its implementation and testing. LITERATURE REVIEW A considerable number of algorithms are available for image processing, in general, and objective recognition, in particular. The saliency method has been one of the popular methods used in various applications. Saliency is defined as features stands out relative to its neighbors in an image, or mathematically as the sum of the absolute value of the local wavelet decomposition of an image. Previous approaches of object detection based on saliency focused on low-level local contrasts such as edges, boundaries, colors, and gradients. Li et al. (2013) argues that a context is needed for saliency detection of vessel targets to be meaningful. They detected saliency not at the isolated pixel level, but at pixels surrounding block. Li s detection algorithm composed of four parts: (a) pyramid of image (e.g. color and intensity layers) is used to decrease the complex computation required for processing high-resolution images; (b) image partition for context consideration; (c) histogram analysis for each block of image to find the salient candidates and create the spatial distribution of the image; and (d) a simplified context-saliency detection algorithm. Their studies consist of a combination of random saliency map and image pyramid. A saliency extraction method was used to reduce the time required in processing high-resolution images. Spatial distribution and local contrasts are considered in the algorithm. The algorithm resulted in an average detect rate of 83.2% for large and medium vessels and false alarm rate of 33.5%. Zhu et al. (2010) proposed a concept for vessel detection consists of two simple steps: sea detection and vessel detection. They employed vessel detection from space-born optical images, in which several factors such as clouds, ocean waves, small islands are often detected as false vessel candidates (small clouds are the most difficult factors due to their random variation). A moderate variation of gray distribution exists in images of a sea region. Since edges of a vessel are observable, image segmentation with edge information can be used to extract possible regions of interest. However, this procedure may include many false alarms such as ocean waves, clouds, and islands due to their similar edge characteristics with those of a vessel. Therefore, once the segmentation is accomplished, shape analysis must be applied to minimize false alarms of vessels. Every vessel has its specific area, length, and width. As a result, very large or very small islands and clouds can be deleted from the image. Moreover, vessels are generally long and thin, so clouds and islands with very small 3

4 ratio will be eliminated as well. Support vector machine is used as the main classifier for vessel classification and all samples classified as either vessel or non-vessel. However, their study is not capable of detecting the vessels that are partly covered by clouds, vessels adjoin a large island, or when the gray scale of a neighbor area is very close to that of the vessel. To overcome this shortcoming, cascade classifier is employed in our research. In addition, samples of vessels or vessels partially covered by clouds are included in sampling procedure of this study. Qi et al. (2009) studied an object-oriented image analysis method to detect and classify vehicles from satellite images. Image objects identified through segmentation are organized in a hierarchical image object network. Feature space is then created by extracting features of these objects and later used for vehicle detection, classification, and traffic flow information analysis. Ortho-rectified image date from QuickBird satellite with four spectral bands, including red, green, blue, near infrared, and panchromatic, were employed for their study. The shadow region of the vehicles (moving objects) accounts for about 10% errors in classification. The shadow problems are resolved in our study by using training samples that include shadows around vessels. METHODOLOGY: AUTOMATIC VESSEL DETECTION IN SATELLITE IMAGES The proposed method is to provide a cost-effective method to automatically detect vessels from optical satellite images for offshore pipeline safety. To achieve the cost-effectiveness, instead of using expensive high-resolution satellite images, we proposed to develop a method that can work with regular resolution images, such as Google Earth images with a resolution ranging from 60 cm to 15 m depend on location. Given a satellite image, the system evaluates visual features of the image, and detects vessel objects in the vicinity of pipeline safety zone. This method includes 5 elements as shown in Figure 1. Acquire satellite image Identify location of pipelines Vessel detection model Vessel object labeling Generate training samples Cascade classifier training Figure 1. Automatic vessel detection. The fundamental idea of the proposed approach is to train a classifier using a set of satellite images containing vessel objects. Once established, this classifier can be used to automatically detect whether vessels present in a new image. To obtain a large set of satellite images as training samples, high-resolution satellite images may be acquired from commercial venders, but the cost can be high (e.g. over $1,000 for 4

5 an archived image of 100 square kilometers). As mentioned previously, this study will acquire images from Google Earth to reduce cost and improve efficiency. The classifier training requires both positive samples and negative samples. Positive samples are images that contain the objects of interest, i.e. vessels. We must manually mark vessels as ground truth in each of the positive sample set so that the classifier can be trained later to correctly identify vessel objects in new images. Negative samples refer to images that do not contain objects of interest, which can help to minimize false alarms. They contain backgrounds and noises that typically associated with the presence of vessels (e.g. ocean surface and waves), or non-vessel objects similar in appearance to vessels, such as small islands, clouds, and oil platform. Figure 2a and 2b show samples of a positive image and a negative image. a) Positive sample b) Negative sample c) HOG feature descriptor Figure 2. Training samples and feature representation. To achieve machine-learning, vessel objects must be represented numerically as a set of features or feature vectors, such as edges and width-length ratio. Typically used feature descriptors include Haar, Local Binary Patterns (LBP), and Histograms of Oriented Gradients (HOG) among others (MathWorks 2014). Haar and LBP features have been primarily used for detecting human faces. They work well for representing fine-scale textures. The HOG features have been used for detecting objects such as cars and traffic signs. They are reliable and efficient for capturing the overall shape of an object, such as a vessel. The basic idea of HOG is that an object s appearance and shape can often be characterized well by the distribution of local intensity gradients or edge directions (Dalal and Triggs 2005). A gradient is a directional change in the intensity or color in an image. In practice, this is implemented by dividing the image window into very small spatial regions, and for each region, accumulating a local histogram of gradient directions over the pixels of the region, such as illustrated in Figure 2c. The above mentioned positive images with numerically represented features forms the training set to establish the classifier based on the concept of supervised learning. Statistically, the detection of vessel presence is a classification problem which classifies an image region as categories, i.e. either vessel or non-vessel. A classifier is a mathematical function that maps input data (e.g. image features) to categories. Each feature (χ i ) contributes differently to each category membership (κ j ), and this contribution may be represented by a weight factor (β ij ). The category membership of a particular image region is a score combining the feature vector and the corresponding weights, such as κ j = χ i β ij in the case of linear classifier. The goal 5

6 of training a classifier is to minimize classification error by fine-tuning the weight factors using the training set of already manually recognized images. A cascade classifier is used in this study (MathWorks 2014). This classifier consists of training and detection in stages. Each stage is trained using a technique called boosting. Boosting provides the ability to train a highly accurate classifier by taking a weighted average of the decisions made by preceding stages. Each stage of the classifier analyzes a portion of an image defined by a sliding window and labels the region as either positive or negative. The size of the window varies to detect objects at different scales. During training, if any object is detected from a negative sample, this is a false positive decision. This false positive is then used as negative sample and each new stage of the cascade is trained to correct mistakes made by preceding stages. For detection purposes, positive indicates that an object was found or otherwise it is negative. When the label is negative, the classification of this region is complete, and the classier slides the window to the next region. If the label is positive, the classifier passes the region to the next stage. The classifier confirms a vessel found when the final stage classifies the region as positive. During the training phase, several parameters must be determined in order to achieve acceptable classifier accuracy, such as the number of stages and false alarm rate. The greater the number of stages, the greater the amount of training data the classifier requires. The false alarm rate is the fraction of negative training samples incorrectly classified as positive. The lower this rate is, the higher the complexity of each stage. These parameters can be fine-tuned experimentally according to a desired level of accuracy. Once the classifier is satisfactorily trained, it can be used to process new satellite images to detect vessels. Offshore pipelines usually spread out over a long distance and the presence of vessels in a relatively narrow pipeline safety zone (e.g. usually 200 meters along both side of a pipeline) is rare. Based on this observation, we proposed a unique colorcoding scheme to significantly reduce image processing time by focusing on pipeline safety zone and ignoring its surrounding area and thus noises (e.g. ocean surface and onshore objects). This is achieved by adding color layers to an image to segment the image according to the presence of pipelines. For example, as shown in Figure 3, the green color coded region contains the pipelines while the red zone does not. In particular, the dark green area (3c) represents the pipeline s danger zone, and the light green area (3b) refers to the vicinity of the danger zone. The red color zone (3a) will be ignored by the classifier, while it focuses its effort on analyzing green areas, resulting in much shorter processing time. a) No-pipeline zone b) Vicinity of a danger zone c) Pipeline danger zone Figure 3. Color-coding scheme to reduce image processing time. 6

7 IMPLEMENTATION AND PERFORMANCE The proposed method was coded in a MATLAB program and two applications were referenced in this program Training Image Labeler (TIL) and Computer Vision System Toolbox (MathWorks 2014). For testing purpose, a total of 755 passive satellite image samples containing various types of vessels were collected. In addition, 35 negative image samples were also included in the training dataset. These images were stored in Portable Network Graphics (PNG) format since it is a raster-based graphic format that supports lossless data compression. These color images were then converted into gray-scale images to increase the contrast for more efficient image processing. For labeling ground truth data in the positive training samples, a MATLAB application, TIL, was used, and this application allows a user to interactively specify a rectangular region around a vessel as Regions of Interest (ROIs). A ROI define the location of a vessel, which are later used as positive samples to train the classifier. The training of the custom vessel cascade classifier was implemented using the traincascadeobjectdetector function in the Computer Vision System Toolbox (MathWorks 2014). The training parameters were determined using the trial-and-error approach. The feature descriptor selected was HOG and false alarm rate and the number of cascade stages were set to 17.5% and 8 respectively. The color-coding scheme was applied to keep the classifier working only within the interested regions, i.e. pipeline safety zone. Once the location of an offshore pipeline location was determined, three sub-zones were defined: Danger Zone (DZ) in light green, Vicinity of the Danger Zone (VDZ) in dark green, and the no-pipeline zone in red. Our program is capable of detecting the color segmentations, ignoring the red color zone, and focusing on the green zones to detect vessels. When a vessel is found, the program labels the detected object with a yellow rectangular, as shown in Figure 4a. The performance of the classifier can be measured by the rates of true detection (i.e. true positive) and false alarm (i.e. false positive). A true positive occurs when a positive sample is correctly classified. A false positive occurs when a negative sample is mistakenly classified as positive. The proposed algorithm was tested on 150 Google Earth satellite images with an average detection rate of 94% and a false alarm rate of 19.75%. Comparing with past similar studies, Li et al. (2013) used commercial high-resolution satellite images and achieved an average detection rate of 83.2% and a false alarm rate of 33.5%, as shown in Figure 4b. CONCLUSION Current methods of relying on high-resolution satellite images lead to a high implementation cost and less efficient image processing. This study proposes a method of utilizing regular resolution satellite images for vessel detection in offshore pipeline safety zones. It applies a combination of cascade classifier and a unique color coding scheme to achieve an accurate and efficient satellite image processing procedure. This method was tested using publically available free Google Earth 7

8 satellite images, and it is cost effective while still achieving a similar detection rate as previous studies did using high-resolution images. a) Vessel detected and labeling b) Detection rate Figure 4. Vessel detection and performance. Despite its advantages, satellite imaging has several limitations. First, it has low temporal resolution as indicated by the revisit time, which is a measure of the time interval between two satellite visits to the same location on earth, ranging from a few days to weeks. For meaningful pipeline surveillance, satellite- and AIS-based sensing must be integrated. Second, optical image sensors lack the ability of data capturing in all-weather conditions and during night time. Synthetic Aperture Radar (SAR) imagery that is based on radio waves should be investigated in future research. REFERENCES Dalal, N. and Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection, Proc. CVPR 2005, San Diego, CA. GateHouse (2010). Offshore pipeline surveillance solution. White paper. GateHouse, Sundby, Denmark. Li, Z., Xie, X., Zhao, W., and Liu Y. (2013), A method of vessel detection in optical satellite based on saliency map, Proc. ICTIS 2013, ASCE, Wuhan, China. MathWorks (2014). MATLAB R2014a Help for Cascade Classifier, MathWorks, Natick, MA. McDonald, M. (2003). The update of the loss of contaminant data for offshore pipeline. Publication PARLOC Mott MacDonald Ltd., Offshore Operators Association and the Institute of Petroleum, U.K. National Research Council (NRC) (1994). Improving the safety of marine pipelines, National Academy Press, Washington D.C. Qi S., Yanping L., Qulin T. and Sulan Z. Research on vehicle information extraction from high-resolution satellite images, Proc. ICCTP 2009, ASCE, Harbin, China. Woodson, R. D. (1990). Offshore pipeline failures. < > (Sep. 30, 2013). Zhu C., Zhou H., Wang R., Guo J. (2010) A novel hierarchical method of vessel detection from spaceborne optical image based on shape and texture features, IEEE transactions on geoscience and remote sensing, 48(9). 8