MINISTRY OF EDUCATION & TRAINING UNIVERSITY OF MINING AND GEOLOGY LE MINH HANG RESEARCH PROPOSAL METHOD FOR IDENTIFICATION AND CLASSIFICATION OF OIL SPILLS AT SEA BY MICROWAVE REMOTE SENSING DATA Research field: Geodesy and mapping Code: 62520503 SUMMARY OF PHD THESIS Hanoi – 2013 The thesis has been completed at Photogrammetry & Remote Sensing Department, Faculty of Geodesy, University of Mining and Geology, Hanoi Full name of supervisors: 1. Assoc. Prof. Nguyen Dinh Duong Institute of geographic, Vietnam Academy of Science and Technology 2. Assoc. Prof . Tran Dinh Tri University of Mining and Geology Examiner 1: Assoc.Prof. Nguyen Dinh Minh University of Science, Vietnam National University, Hanoi Examiner 2: Dr. Tran Dinh Luat Vietnam Natural Resources and Environment Corporation, Ministry of Natural Resources and Environment Examiner 3: Dr. Nguyen Du Khang Vietnam Remote Sensing Center, Ministry of Natural Resources and Environment The thesis will be defended at the University examination Council at the Hanoi university of Mining and Geology At… h, …/…/, 2013 This thesis can be referenced at the National Library or at the library of the Hanoi University of Mining and Geology LIST OF SCIENCE WORKS HAVE BEEN PUBLISHED BY AUTHOR RELATED CONTENT OF THE THESIS 1. HANG le minh, DUONG Nguyen Dinh (2009), Oil spill detection and Classification by ALOS PALSAR at VietnamEastSea, The 7 th FIG Regional Conference: Spatial Data Serving People, Land Governance and the Environment-Building the Capacity, Hanoi, Vietnam 2. Lê Minh Hằng, Nguyễn Đình Dương (2010), Chuyển đổi dữ liệu từ raster sang vector áp dụng với đối tượng vùng trong quan trắc vết dầu trên biển, Tạp chí Khoa học kỹ thuật Mỏ - Địa chất, Số 30/4-2010, tr.63-69, Hà Nội. 3. Le Minh Hang, Nguyen Dinh Duong (2010), Practical implementation of vectorization of oil spills detected at sea on SAR image, The 31 th Asian Conference on Remote Sensing, Hanoi, Vietnam 4. Lê Minh Hằng, Nguyễn Đình Dương (2010), Xây dựng chương trình đọc tư liệu viễn thám siêu cao tần phục vụ phân tích vết dầu trên biển, Tuyển tập Báo cáo Hội nghị khoa học lần thứ 19 – Quyển 06 Trắc địa, tr.61- 66, Trường Đại học Mỏ - Địa chất, Hà Nội. 5. Nguyễn Đình Dương, Nguyễn Mai Phương, Lê Minh Hằng (2010), Chuẩn hóa tư liệu ảnh SAR trên biển trong mặt cắt ngang, Tuyển tập các công trình khoa học, Hội nghị khoa học Địa lý – Địa chính, tr.5 – 14, Trường Đại học Khoa học tự nhiên, Đại học quốc gia Hà Nội, Hà Nội 6. Lê Minh Hằng, Nguyễn Đình Dương (2011), Tổng quan về các phương pháp nhận dạng và phân loại vết dầu trên biển bằng tư liệu viễn thám siêu cao tần, Tạp chí Khoa học kỹ thuật Mỏ - Địa chất, Số 35/7-2011, tr.66-71, Hà Nội. 7. Lê Minh Hằng, Nguyễn Đình Dương (2011), Xây dựng chương trình đọc dữ liệu ảnh vệ tinh EnviSAT ASAR chế độ thu nhận WSM, Tạp chí Khoa học kỹ thuật Mỏ - Địa chất, Số 36/10-2011,tr.68-73, Hà Nội. 8. Nguyen Dinh Duong, Nguyen Mai Phuong, Le Minh Hang (2012), OilDetect 1.0 - A System for Analysis of Oil Spill in Sar Image, Vol 12, No 2, tr.12-18, Tạp chí AJG (Asian Journal of Geoinfomatics) 9. Lê Minh Hằng, Nguyễn Đình Dương (2012), Nghiên cứu tách vết dầu trên dữ liệu ảnh SAR bằng thuật toán nở vùng, Tạp chí Khoa học kỹ thuật Mỏ - Địa chất, Số 38/4-2012, tr. 68-72, Hà Nội. 1 INTRODUCTION 1. Background With the coastline stretching from north to south, there are many areas to exploit oil and gas in the Vietnam East Sea and lie on the main traffic at sea of the world so that the Vietnam East Sea often appear oil pollution at sea. In recent years, Vietnam has continuously occurred oil spill which unknown origin in the central coastal region. The phenomenon of the oil spill was detected only when oil spill was hit to ashore by the waves. Vietnam completely passive to response the oil spill at sea because of non- system for early detecting and monitoring of oil pollution at sea. Nowadays, remote sensing techniques are being applied to the early detecting and monitoring of oil pollution at sea all over the world, especially RADAR system. RADAR is a system having active microwave remote sensing, allowing the observation both day and night, in any kind of weather conditions, not affected by cloud, fog over sea surface and having wide swath. These are some advantages of microwave remote sensing data comparing with the optical data for monitoring and early detection of oil pollution at sea. Due to receiving backscatter energy of microwave sensors and declining the wave fluctuations at oil slick, oil spills are constrasted with surrounding sea using SAR image so that extraction and classification of oil spills in SAR image can be processed automatically. However, sea weather, the processing of microwave data systems in Vietnam is limited so that it is needed of research proposal methods for identification and classification of oil spills at sea by microwave remote sensing data consistent with the Vietnam conditions. 2. Objectives of the study - To study the methodology and the factors affecting the identification and classification oil spill at sea by SAR image data. - To research the method for identification and classification oil spill at sea by SAR image data. - To propose the method for identification and classification oil spill at sea 2 by SAR image data consistent with the Vietnam conditions. 3. Subjects of study - The characteristics of the transmitting and receiving signals of microwave satellite. - The impact of oil spill in declining the intensity fluctuations of waves and characteristics of backscatter at the microwave satellite sensor. - The factors affecting the accuracy of the identification and classification of oil spill at sea by SAR image. - The method for identification and classification of oil spill at sea by microwave remote sensing data. 4. Range of the research - The methodology is proposed to identify and classify the unknown origin oil spills at seamainly discharge from from ships by SAR image data. - The range of study is Vietnam East Sea. - The research ability RADAR image of synthetic aperture radar (SAR), with Band-L (ALOS PALSAR data) and Band-C (ENVISAT ASAR data). 5. Research Contents - Research methodologies and methods for identification and classification oil spill at sea by SAR image. - Propose the method for identification and classification oil spill at sea by SAR image in accordance with characteristics of SAR images observed the sea and in the wide mode. - Develop a program which can identify and classify oil spill and look-alike by SAR image. 6. The methodology - Analysis, synthesis of materials including scientific articles published in over the world and Vietnam, results of experiments for early detection and monitoring of oil pollution at sea and the software for detecting oil spill in SAR image. Hence, the authors proposed appropriate methodology, feasible with Vietnam conditions. - Study image processing algorithms, identifying and classifying oil spills at sea algorithm, compare with the other algorithms and choose an appropriate 3 algorithm for the purpose of thesis. 7. The meaning and practicing of science thesis 7.1. Scientific significance of the thesis - A complete scientific basis for identification and classification of marine oil stains from materials ultrasonic sensing. - Develop a method for identification and classification of oil traces from the literature on marine SAR images. - Thesis has contributed in implementing scientific research missions of research on state-level "Oil pollution in the East Sea Vietnam" with code KC09.22/06-10 by Assoc. Prof Nguyen Dinh Duong. 7.2. Practical significance of the subject - Improved application of the material in the SAR image monitoring and early detecting of oil pollution off the East Sea Vietnam - Provided adequate assessment theory and the results of experimental studies on Band-L material (ALOS PALSAR) and Band-C materials (ENVISAT ASAR). 8. The main acquisitions of the thesis 8.1. The SAR image data which has been calibrated to Normalized Radar Cross Section (NRCS) still exist effect of near - far range. Effect of near – far range affects the ability to detect automatically dark spots on SAR image by total threshold algorithm. 8.2. The method for detecting dark spots by region growing algorithm performance applications in case of oil spills which has been for a while and affects by weather. Oil slick in this case is not high contrast with the sea surface in the SAR image. As a result, oil slick on SAR image has many gray levels. 8.3. The method for identification and classification oil spill at sea by microwave remote sensing data proposed in the thesis can be applied in the condition of materials, infrastructure and information of Vietnam. 9. The new ideas of the thesis 9.1. Propose the method for automatically identifying and classifying oil spill by SAR image data. 4 9.2. Propose the method for limiting the influence of near-far range effect on SAR images data applied for identification and classification of oil spills at sea. The near-far range effect exists on microwave remote sensing data, especially for the wide mode. 9.3. Research the application of neural network MLP for identification and classification of oil spills and look-alike on SAR image with the number of various input parameters. 10. Volume and structure of thesis The structure of the thesis is presented in 118 pages, 62 figures and diagrams and 05 tables. Chapter 1. OVERVIEW THE RESULTS OF RESEARCH IN THE WORLD AND VIETNAM 1. Introduction 1.2. Overview of the research in the world The research of using SAR image data to detect oil spill on marine has studied since 1992 by Bern [11]. The author has used ERS-1 data (band- C) to investigate the possibility of detecting oil spill on sea surface. The research result includes: - Image pre-processing: No mention to eliminate the affecting of near-far range effect during image preprocessing for detecting on SAR images. - Detecting and localizing dark spots: Using threshold method to detect and localize dark spots on the image [5]. Beside the dark spots can be detected by other methods such as LOG algorithms, DoG, HMC [27] to detect the image, CFAR algorithm [9], and FCM algorithm [34]. - Slick feature extraction: The shape of discharge oil spill at sea is linear shape. So the method to identify and classify oil spills on SAR images are based on the geometric characteristics and shape of the detected region, physical characteristics of the backscatter level of the spot and its surroundings and spot contextual features. - Identification and classification method: A number of studies classified oil spill by experts through SAR image interpretation [7]. Besides oil spills are 5 identified and classified by semi-automatic method [7]. Other authors have proposed fully automatic method for identification and classification of oil spills through neural network [18], [23] or fuzzy logic [22]. Some organizations also have built research module to detect oil spill by SAR image such as Oil spill detection module in NEST software (appendix 12). NEST software uses semi-automated method. 1.3. Overview the results of research archivement in Vietnam This research is a part of the project KC09.22/06-10 which of "Oil pollution in the East Sea Vietnam" of Assor.Prof Nguyen Dinh Duong Institute of Geography – Vietnam academy of Science and Technology. Thesis author attended in this research to build observation systems for early detecting and monitoring of oil spill at sea by microwave remote sensing data. 1.4. Evaluate results of research achieved in the world and Vietnam The data in scientific articles are mostly ERS - 1.2, Envisat ASAR and Radarsat (Band - C). There has not been much research on material Band-L. The results of identification and classification of oil spill on SAR image primarily based on expert knowledge. The completely automated classification methods are still researched and experienced by different mode. Vietnam have not invested a research method for monitoring and detecting oil spill at sea. 1.5. These issues are developed in the thesis The content of the thesis inherits the research which the student have been done in KC09.22/06-10. Based on the results of research archivement and published scientific journals, the student will continue to study the application of image processing algorithms to improve the ability to identify and classify oil spill in SAR image data such as: - Research on application of contrast limited adaptive histogram equalization (CLAHE) to remove influence of near-far rang effect on SAR images. - Research on application of automatic threshold algorithm to detect dark spots on SAR image which adjusted near-far range effect. 6 - Research on using region growing algorithm to detect dark spots in the case the oil spill was weathered and had low contrast on SAR image. - Research on ability to discriminate oil spill and look-alike based on neural network models MLP with geometric characteristics index of each slick. - Experiment with 2 type data content of Band-C and band-L ranges. There are the main remote sensing data being used in Vietnam. Chapter 2. THE METHODOLOGY OF IDENTIFICATION AND CLASSIFICATION OIL SPILL AT SEA BY SAR IMAGE 2.1. Principles of Synthetic Aperture Radar (SAR) 2.1.1. RADAR image system 2.1.2. Synthetic Aperture Radar (SAR) According to the principles of the system, the SAR antenna will receive backscattering response from the object. Backscatter energy is received by microwave sensor of satellite depend on the surface roughness of the object. 2.2. SAR image of the sea surface 2.2.1. Sea surface description On sea surface there has three main waveform is capillary waves, gravitational waves and capillary-gravity waves. According to [25], the capillary-gravity waves will impact on microwave which are used in satellite observations of ocean. 2.2.2. Reflection of electromagnetic waves from the sea surface 2.2.2.1. Effect of dielectric constant Dielectric constant of the marine environment will affect the permeability of high-frequency waves. 2.2.2.2. Ocean surface roughness The impact between the microwave and capillary-gravity waves on sea surface is primarily due to Bragg scattering law. 2.2.2.3. Interaction of short and long wave As long as waves grow steeper, the radial velocity components increase, creating more smearing azimuth on SAR image [20]. 7 2.2.2.4. Interaction of short waves and currents The interaction of surface waves and currents will significantly change the wavelength of the waves on sea surface increase or decrease the response microwave scattering from the sea surface and the redistribution of Bragg scattering waves on SAR images. 2.3. Methodology of identification and classification oil spill at sea by SAR image 2.3.1. Oil spill on SAR imagery The viscosity of oil slick will reduce the short-wave oscillations, increase surface tension and reduce wind pressure at oil spill location. So, energy backscattering at that position will be reduced and as a result oil spill on SAR image is dark spot, contrast to sea surface (Figure 2.10). The contrast between oil spill and sea surface on SAR image data is the important characteristic for identification and classification oil spill and it is the advantages of SAR image to others remote sensing data. However, due to fluctuations of the sea surface are complex with the natural conditions at sea, the accuracy of the identification and classification results depends on the objective conditions. Figure 2.10. Oil spill on SAR image (a) Backscattering at oil spill position and region surrounding; (b) Oil spill on SAR image 2.3.2. Identification and classification of oil spill at sea by SAR images According to research agency Aerospace Europe (ESA) [16], 45% of the oil pollution comes from operative discharges from ships. The ships often discharge waste oil on the road and oil slick has linear shape. Scientists base on this shape to identify and classify oil spill on SAR image. 2.4. The affection of identify and classify oil spill on SAR image Bề mặt biển Vết dầu Vết dầu (b) Vết dầu trên biển Sóng phản xạ Sóng tán xạ . Minh Hằng, Nguyễn Đình Dương (2011), Tổng quan về các phương pháp nhận dạng và phân loại vết dầu trên biển bằng tư liệu viễn thám siêu cao tần, Tạp chí Khoa học kỹ thuật Mỏ - Địa chất, Số 35/7-2011,. Minh Hằng, Nguyễn Đình Dương (2010), Xây dựng chương trình đọc tư liệu viễn thám siêu cao tần phục vụ phân tích vết dầu trên biển, Tuyển tập Báo cáo Hội nghị khoa học lần thứ 19 – Quyển 06. The affection of identify and classify oil spill on SAR image Bề mặt biển Vết dầu Vết dầu (b) Vết dầu trên biển Sóng phản xạ Sóng tán xạ 8 2.4.1. Wind speed on sea surface