High resolution satellite image compression using zero padding discrete wavelet transform and discrete cosine transform algorithms

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(1)PM. UNIVERSITI PUTRA MALAYSIA. R. IG. H. T. U. HIGH RESOLUTION SATELLITE IMAGE COMPRESSION USING ZERO PADDING DISCRETE WAVELET TRANSFORM AND DISCRETE COSINE TRANSFORM ALGORITHMS. ©. C. O. PY. HALAH SAADOON SHIHAB AL-OBAIDI. ITMA 2016 18.

(2) PM. By. PY. R. IG. H. T. U. HIGH RESOLUTION SATELLITE IMAGE COMPRESSION USING ZERO PADDING DISCRETE WAVELET TRANSFORM AND DISCRETE COSINE TRANSFORM ALGORITHMS. ©. C. O. HALAH SAADOON SHIHAB AL-OBAIDI. Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in Fulfillment of the Requirements for the Degree of Master of Science. December 2016 ii.

(3) ©. T. H. IG. R. PY. O. C. PM. U.

(4) COPYRIGHT. PM. All material contained within the thesis, including without limitation text, logos, icons, photographs and all other artwork, is copyright material of Universiti Putra Malaysia unless otherwise stated. Use may be made of any material contained within the thesis for non-commercial purposes from the copyright holder. Commercial use of material may only be made with the express, prior, written permission of Universiti Putra Malaysia.. ©. C. O. PY. R. IG. H. T. U. Copyright © Universiti Putra Malaysia. iii.

(5) DEDICATION. This work is dedicated To. PM. My dear Husband Sermed for your support My two Boys Ahmed and Aws for all the inconvenience you went through, with patience, during this study The memory of my dear Father, for your inspiration. U. My dear Mother, Brother, and Family, for all your warm feelings. ©. C. O. PY. R. IG. H. T. My Dear Friends for all your moral support. iv.

(6) Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfillment of the requirement for the Degree of Master of Science. PM. HIGH RESOLUTION SATELLITE IMAGE COMPRESSION USING ZERO PADDING DISCRETE WAVELET TRANSFORM AND DISCRETE COSINE TRANSFORM ALGORITHMS. By. December 2016. T. : Associate Professor Suhaidi b. Shafie, PhD : Institute of Advanced Technology. H. Chairman Faculty. U. HALAH SAADOON SHIHAB AL-OBAIDI. PY. R. IG. Raw satellite images are considered high in resolution, especially multispectral images captured by remote sensing satellites. Hence, choosing the suitable compression technique for such images should be carefully considered, to achieve high values of compression ratio (CR) to decrease the data storage on-board satellites, and the bandwidth required to transmit data from the satellite to earth, while simultaneously maintaining the important scientific information of the image when reconstructed at the ground station. The Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT)-based compression techniques have been utilized in most of the space missions launched throughout the last few decades due to their efficiency. However, both techniques have some drawbacks that should be addressed, such as blocking artefacts for DCT and computational complexity for DWT.. ©. C. O. Several different hybrid compression methods based on DCT and DWT were used in previous works to utilize the positive properties and decrease the effect of the negative properties of these transforms. However, none of these hybrid methods were implemented on high resolution satellite images, and each method had one or more shortcomings. Hence, a proposed hybrid (DWT-DCT) method was presented and implemented in the current work on several satellite images, simulating an image compression system on-board a small satellite. This method can achieve higher values of compression ratio, through adding the 2D-DCT to the 2D-DWT second level coefficients instead of further complex levels of 2D-DWT compression. Besides, this method can maintain an acceptable reconstructed image quality through replacing the standard DWT thresholding and quantization processes with an alternative process that employed the zero-padding technique in a way that controlled the change in Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR), and helped to decrease the DWT processing time.. i.

(7) ©. C. O. PY. R. IG. H. T. U. PM. Image compression was performed on several satellite images using DCT, DWT, and the proposed Hybrid method with the aim of comparing the results. Hence, it was indicated that the proposed hybrid (DWT-DCT) technique achieved better performance than the standalone DCT or DWT techniques. All the results were obtained objectively (by calculating the CR and PSNR values for each case using the MATLAB software package), and subjectively (Visually). A gain of 43.6%, 57.8% and 96.5% of CR was achieved using the proposed method relative to the DWT, for the satellite images Baghdad, Basra and Erbil respectively, with a negligible reduction in the reconstructed image quality. Hence, it was concluded that using the proposed method improved the satellite image compression, and it is feasible to be used onboard satellites.. ii.

(8) Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai memenuhi keperluan untuk Ijazah Master Sains. PM. PEMAMPATAN IMEJ SATELIT BERESOLUSI TINGGI MENGGUNAKAN ALGORITMA TRANSFORMASI WAVELET DISKRET DAN TRANSFORMASI KOSINUS DISKRET DENGAN PELAPIK SIFAR. Oleh. Disember 2016. T. : Profesor Madya Suhaidi b. Shafie, PhD : Institut Teknologi Maju. H. Pengerusi Fakulti. U. HALAH SAADOON SHIHAB AL-OBAIDI. O. PY. R. IG. Imej satelit mentah dianggap tinggi dalam resolusi, terutamanya imej pelbagaispectral yang ditangkap oleh satelit penderiaan jauh. Oleh itu, memilih teknik mampatan yang sesuai untuk imej tersebut harus dipertimbangkan dengan teliti bagi mencapai nilai nisbah mampatan (CR) yang tinggi supaya dapat mengurangkan penyimpanan data pada satelit, dan lebar jalur yang diperlukan untuk menghantar data dari satelit ke bumi, serta pada masa yang sama dapat mengekalkan maklumat saintifik yang penting apabila imej dibina semula di stesen bumi. Teknik mampatan berasaskan Transformasi Kosinus Diskret (DCT) dan Transformasi Wavelet Diskret (DWT) telah digunakand alam kebanyakan misi angkasa yang dilancarkan sepanjang beberapa dekad yang lalu kerana kecekapan teknik tersebut. Walaubagaimanapun, kedua-dua teknik tersebut mempunyai beberapa kelemahan yang perlu ditangani, seperti artifak tersekat untuk DCT dan kerumitan pengiraan untuk DWT.. ©. C. Beberapa kaedah pemampatan hibrid berbeza yang berasaskan DCT dan DWT telah digunakan dalam kerja-kerja sebelum ini untuk menggunakan ciri-ciri positif dan mengurangkan kesan daripada ciri-ciri negatif daripada kaedah penjelmaan ini. Walaubagaimanapun, tiada kaedah hibrid yang dilaksanakan pada imej- imej satelit beresolusi tinggi, dan setiap kaedah mempunyai satu atau lebih kekurangan. Oleh itu, kaedah yang dicadangkan iaitu hibrid (DWT-DCT) telah dibentangkankan dan dilaksanakan dalam kerja-kerja semasa untuk beberapa imej satelit, serta simulasi sistem pemampatan imej bagi sebuah satelit kecil. Kaedah ini boleh mencapai nilainilai nisbah mampatan yang lebih tinggi, melalui cara menambah 2D-DCT kepada pekali tahap kedua 2D-DWT dan bukannya tahap kompleks bagi mampatan 2D-DWT. Selain itu, kaedah ini juga boleh mengekalkan kualiti imej binaan semula melalui penggantian proses standard pengambangan dan pengkuantuman DWT dengan proses alternatif yang menggunakan teknik pelapik sifar dengan cara mengawal perubahan dalam ratio mampatan (CR) dan puncak isyarat kepada nisbah hingar(PSNR), dan ini membantu untuk mengurangkan masa pemprosesan DWT. iii.

(9) ©. C. O. PY. R. IG. H. T. U. PM. Pemampatan imej telah dilakukan keatas beberapa imej satelit menggunakan DCT, DWT, dan kaedah Hibrid yang dicadangkan dengan tujuan untuk perbandingkan. Oleh itu, keputusan menunjukkan bahawa teknikyang dicadangkan iaitu hibrid (DWTDCT) mencapa iprestasi yang lebih baik daripada teknik DCT atau DWT secara sendirian. Semua keputusan telah dijalankan secara objektif (dengan mengira nilai CR dan PSNR bagi setiap kes dengan menggunakan pakej perisian MATLAB), dan subjektif (visual). A keuntungan sebanyak 43.6%, 57.8% dan 96.5% daripada CR dicapai menggunakan cadangan kaedah relatif kepada DWT, untuk imej satelit Baghdad, Basra dan Erbil masing-masing, dengan pengurangan kecil dalam kualiti imej yang. Oleh itu, kesimpulannya kaedah yang dicadangkan dapat meningkatkan pemampatan imejsatelit dan berdaya maju untuk digunakan pada satelit.. iv.

(10) AKNOWLEDGEMENTS. I would like to extend my thanks to all the academic and administrative staff of Universiti Putra Malaysia, for all the help that was given to me in order to ensure that I finish the requirements that were necessary for me to be granted this degree.. U. PM. I extend my deepest thanks and gratitude to Dr. Suhaidi Shafie, the chairman of my supervisory committee, for his continuous support and guidance, and to Dr. Abd Rahman Ramli and Dr. Fauzan Ahmad, the internal and external members of my supervisory committee respectively, for all the help and valuable comments they gave to me during the research work. The support of all the members of my supervisory committee helped me to accomplish and cover all the required stages for this research.. ©. C. O. PY. R. IG. H. T. Finally, I must extend my deepest thanks and gratitude to my Husband, Sermed, and my two boys, Ahmed and Aws, for all the hardships that they went through patiently during my study, for having a busy wife and mother.. v.

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(12) This thesis was submitted to the Senate of the Universiti Putra Malaysia and has been accepted as fulfillment of the requirement for the degree of Master of Science. The members of the Supervisory Committee were as follows:. PM. Suhaidi b. Shafie, PhD Associate Professor Faculty of Engineering Universiti Putra Malaysia (Chairman). T H. C. O. PY. R. IG. Fauzan Ahmad, PhD Senior Lecturer Universiti Teknologi Malaysia Malaysia (Member). U. Abdul Rahman b. Ramli, PhD Associate Professor Faculty of Engineering Universiti Putra Malaysia (Member). ©. ________________________________ ROBIAH BINTI YUNUS, PhD Professor and Dean School of Graduate Studies Universiti Putra Malaysia. Date:. vii.

(13) Declaration by graduate student. IG. H. T. U. PM. I hereby confirm that:  this thesis is my original work;  quotations, illustrations and citations have been duly referenced;  this thesis has not been submitted previously or concurrently for any other degree at any institutions;  intellectual property from the thesis and copyright of thesis are fully-owned by Universiti Putra Malaysia, as according to the Universiti Putra Malaysia (Research) Rules 2012;  written permission must be obtained from supervisor and the office of Deputy Vice-Chancellor (Research and innovation) before thesis is published (in the form of written, printed or in electronic form) including books, journals, modules, proceedings, popular writings, seminar papers, manuscripts, posters, reports, lecture notes, learning modules or any other materials as stated in the Universiti Putra Malaysia (Research) Rules 2012;  there is no plagiarism or data falsification/fabrication in the thesis, and scholarly integrity is upheld as according to the Universiti Putra Malaysia (Graduate Studies) Rules 2003 (Revision 2012-2013) and the Universiti Putra Malaysia (Research) Rules 2012. The thesis has undergone plagiarism detection software. R. Signature: ___________________________________. Date: _________________. ©. C. O. PY. Name and Matric No.: Halah Saadoon Shihab Al-Obaidi / GS40866. viii.

(14) Declaration by Members of Supervisory Committee. PM. This is to confirm that:  the research conducted and the writing of this thesis was under our supervision;  supervision responsibilities as stated in the Universiti Putra Malaysia (Graduate Studies) Rules 2003 (Revision 2012-2013) were adhered to.. Associate Professor Dr. Suhaidi b. Shafie. Signature: Name of Member of Supervisory Committee:. Associate Professor Dr. Abdul Rahman b. Ramli. R. IG. H. T. U. Signature: Name of Chairman of Supervisory Committee:. PY. Signature: Name of Member of Supervisory Committee:. ©. C. O. Dr. Fauzan Ahmad. ix.

(15) TABLE OF CONTENTS. Page i iii v vi viii xiii xiv xviii. PM. ABSTRACT ABSTRAK ACKNOWLEDGEMENTS APPROVAL DECLARATION LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS. U. CHAPTER. INTRODUCTION 1.1 Satellite Images 1.2 Satellite Image Compression 1.2.1 The Need for Satellite Image Compression 1.2.2 Satellite Image Compression Categories 1.3 Problem Statement 1.4 Research Objectives 1.5 Scope of the Study 1.6 Thesis Layout. 1 1 1 1 2 3 3 4 4. 2. LITERATURE REVIEW 2.1 Introduction 2.1.1 Images 2.1.2 Image Compression 2.1.3 Satellite Image Compression 2.2 Satellite Remote Sensing 2.2.1 Multispectral Images 2.2.2 The Difference between High resolution and Low resolution Images 2.3 Image Compression Techniques 2.3.1 Types of Redundancy 2.3.2 Image Compression Metrics 2.4 Main Lossy Compression Algorithms used on-board Satellites 2.4.1 Discrete Cosine Transform (DCT) 2.4.2 Discrete Wavelet Transform (DWT) 2.5 Zero Padding 2.6 Hybrid Algorithms used in image compression 2.7 Related Previous Works 2.8 Summary. 5 5 5 5 6 6 7 7 8 8 10 11 14 17 21 21 22 24. METHODOLOGY 3.1 Introduction 3.2 2D-DCT Satellite Image Compression and Decompression 3.2.1 On-Board Satellite Image Compression 3.2.2 Satellite Image Decompression. 25 25 28 28 29. ©. C. O. PY. R. IG. H. T. 1. 3. x.

(16) 3.3 2D-DWT Satellite Image Compression and Decompression 3.3.1 Wavelet Selection 3.3.2 On-Board Satellite Image Compression 3.3.3 Employing the Zero-Padding Technique 3.3.4 Satellite Image Decompression at the Ground Station 3.3.5 The Importance of using the Zero Padding Technique in the Current Work 3.3.6 The effect of employing the Zero Padding technique in DWT method for High resolution satellite images and Low resolution images 3.4 Use of a Proposed Hybrid (DWT-DCT) Algorithm for Satellite Image Compression and Decompression 3.4.1 The Proposed Hybrid Method 3.4.2 Satellite Image Compression 3.4.3 Satellite Image Reconstruction 3.4.4 A Comparison between the DWT and the proposed hybrid method 3.4.5 Advantages of using the Proposed Hybrid method in Satellite Image Compression 3.5 Final Comparison of the proposed hybrid algorithm with the standalone DCT algorithm and DWT algorithm 3.5.1 Objective Comparisons 3.5.2 Subjective Comparisons 3.6 Summary. 29 29 30 30 33 33. RESULTS AND DISCUSSION 4.1 Introduction 4.2 2D-DCT Satellite Image Compression and Decompression 4.2.1 2D-DCT Objective Results 4.2.2 2D-DCT Subjective Results 4.2.3 Discussion of DCT results 4.3 2D-DWT Satellite Image Compression and Decompression 4.3.1 Wavelet Selection 4.3.2 2D-DWT Objective Results 4.3.3 2D-DWT Subjective Results 4.3.4 Comparison of DWT Regular Quantization and Zero Padding Method Results 4.3.5 Difference of the Zero Padding effect on High and Low resolution images 4.3.6 Discussion of DWT results when the Zero Padding method is employed 4.4 Use of a Proposed Hybrid (DWT-DCT) Algorithm for Satellite Image Compression and Decompression 4.4.1 The Proposed Hybrid Method 4.4.2 Objective Results 4.4.3 Subjective Results 4.4.4 A Comparison between the DWT and the proposed hybrid method 4.4.5 Discussion of the Results 4.5 Final Comparison among Image Compression Algorithms. 44 44 44 44 46 46 46 46 48 52 57. IG. H. T. U. PM. 34. ©. C. O. PY. R. 4. xi. 36 36 37 40 41 42 42 43 43 43. 59 63 63 63 64 65 66 67 68.

(17) 68 71 73 74. CONCLUSION AND RECOMMENDATIONS 5.1 Conclusion 5.2 Contribution of the Current Work 5.3 Recommendations for Future Work. 76 76 77 77. 75. PM. 5. 4.5.1 Objective Comparisons 4.5.2 Subjective Comparisons 4.5.3 Discussion of Final Comparison Results 4.6 Comparison between the Proposed Hybrid method and Previous works 4.7 Summary. ©. C. O. PY. R. IG. H. T. U. REFERENCES APPENDICES BIODATA OF STUDENT LIST OF PUBLICATIONS. xii. 79 85 118 119.

(18) LIST OF TABLES. Table. Page. 2.1. Landsat 8 Multispectral Bands. 8. 2.2. Transform Methods. 2.3. Recommended Luminance Q-Table. 2.4. List of related previous works. 3.1. The specifications of the satellite images used in the current work. 3.2. Description of the Zero-Padding cases. 4.1. Wavelet Selection (for different bands of Baghdad image). 47. 4.2. Objective Results for 2 levels of 2D-DWT (Baghdad image). 48. 4.3. Objective Results for 2 levels of 2D-DWT (Basra image). 4.4. Objective Results for 2 levels of 2D-DWT (Erbil image). 50. 4.5. Visual comparison between different satellite images before & after 2 levels of 2D-DWT compression & decompression using the zero-padding technique. 61. 4.6. Visual comparison between different benchmark images before & after 2 levels of 2D-DWT compression & decompression using the zero-padding technique. 62. 4.7. A Comparison of CR and PSNR results between the DWT and the proposed hybrid method (Baghdad). 66. A Comparison of CR and PSNR results between the DWT and the proposed hybrid method (Basra). 66. A Comparison of CR and PSNR results between the DWT and the proposed hybrid method (Erbil). 67. PM. 13 15. O. PY. R. IG. H. T. U. 23. ©. C. 4.8. 4.9. xiii. 27 31. 49.

(19) LIST OF FIGURES. Figure. Page A basic transform-based lossy image compression system. 2. 2.1. On-board to Ground Image Path. 6. 2.2. Remote Sensing. 2.3. Image Compression Techniques According to the Type of Redundancies they remove. 2.4. A Basic Transform-based Lossy Image Compression. 12. 2.5. A Basic DCT-based Lossy Image Compression. 15. 2.6. Examples of types of wavelets. 2.7. A Basic DWT-based Lossy Image Compression. 18. 2.8. 2D-DWT Decomposition of an Image. 19. 2.9. 3-Levels of 2D-DWT Image Compression. 20. 3.1. Summary of the Research Methodology. 26. 3.2. LANDSAT8 Band6 Satellite Images used in the Current Work. 27. PM. 1.1. PY. R. IG. H. T. U. 7. 18. 2D DCT Image Compression and Decompression. 28. 3.4. Two-levels of 2D DWT Image Compression using the zeropadding technique. 30. O. 3.3. Level 2 of 2D-DWT Zero-Padding technique cases. 32. 3.6. Benchmark images used for comparison. 35. 3.7. The Proposed Hybrid (DWT-DCT) method. 37. 3.8. 2D-DWT satellite image compression (2-levels). 38. 3.9. The Zero-padding cases (2nd level of 2D-DWT). 38. 3.10. Final compressed images (2 cases). 40. 3.11. Final reconstructed images (2 cases). 41. 4.1. DCT objective results (represented by CR and PSNR). 45. C. 3.5. ©. 10. xiv.

(20) Wavelet Selection. 47. 4.3. Second level 2D-DWT objective results (Baghdad Image). 49. 4.4. Second level 2D-DWT objective results (Basra Image). 50. 4.5. Second level 2D-DWT objective results (Erbil Image). 51. 4.6. 2 levels of 2D-DWT (Baghdad). 52. 4.7. 2 levels of 2D-DWT (Basra). 4.8. 2 levels of 2D-DWT (Erbil). 4.9. Second level of 2D-DWT zero padding cases (Baghdad). 54. 4.10. Second level of 2D-DWT zero padding cases (Basra). 55. 4.11. Second level of 2D-DWT zero padding cases (Erbil). T. 56. 4.12. Processing time difference between the DWT standard quantization & the zero-padding method. 4.13. The Quality difference between the DWT-standard quantization & the zero-padding method. 58. 4.14. The reconstructed image quality of the DWT-regular quantization & the zero-padding technique when CR=26. 58. 4.15. The quality of reconstructed images (PSNR) using different high resolution satellite images and low resolution benchmark images. 60. 4.16. The Objective results for the 2nd level of the proposed hybrid method (Baghdad). 64. The Objective results for the 2nd level of the proposed hybrid method (Basra). 65. 4.18. The Objective results for the 2nd level of the proposed hybrid method (Erbil). 65. 4.19. Objective comparison among DCT, DWT & the Proposed Hybrid methods (Baghdad satellite image). 68. 4.20. Objective comparison among DCT, DWT & the Proposed Hybrid methods (Basra satellite image). 69. 4.21. Objective comparison among DCT, DWT & the Proposed Hybrid methods (Erbil satellite image). 70. PM. 4.2. 52. O. PY. R. IG. H. U. 53. ©. C. 4.17. xv. 57.

(21) Visual comparison among the 3 methods at a CR value of around 28. 71. 4.23. Visual comparison among the 3 methods at a CR value of around 18.6. 72. 4.24. Visual comparison among the 3 methods at a CR value of around 16.5. 73. 4.25. Comparison of the proposed hybrid method results with previous works results for high resolution satellite image. 74. A.1. The 11 Multispectral bands of Baghdad satellite image captured by LANDSAT8. 85. A.2. Basra Satellite image after 2D-DCT compression for different values of the quality factor (Q). A.3. Reconstructed Basra Satellite image after 2D-DCT compression for different values of the quality factor (Q). A.4. Reconstructed Baghdad Satellite image for all 2nd level ZeroPadding Cases (when all 1st level detail coefficients=0). 88. A.5. Reconstructed Basra Satellite image for all 2nd level ZeroPadding Cases (when all 1st level detail coefficients=0). 89. A.6. Reconstructed Erbil Satellite image for all 2nd level ZeroPadding Cases (when all 1st level detail coefficients=0). PY. R. IG. H. T. U. PM. 4.22. 87. 90. Processing time difference between the DWT standard quantization & the zero-padding method (Basra). 91. A.8. Processing time difference between the DWT standard quantization & the zero-padding method (Erbil). 91. Compressed Baghdad Satellite image using the Proposed Hybrid (DWT-DCT) compression method for all 2nd level Zero-Padding Cases. 93. A.10. Reconstructed Baghdad Satellite image using the Proposed Hybrid (DWT-DCT) compression method for all 2nd level ZeroPadding Cases. 94. A.11. Reconstructed Basra Satellite image using the Proposed Hybrid (DWT-DCT) compression method for all 2nd level Zero-Padding Cases. 95. O. A.7. C. A.9. ©. 86. xvi.

(22) Reconstructed Erbil Satellite image using the Proposed Hybrid (DWT-DCT) compression method for all 2nd level Zero-Padding Cases. 96. A.13. The CR results of the DWT & the Proposed Hybrid methods (Baghdad Satellite Image). 97. A.14. The CR results of the DWT & the Proposed Hybrid methods (Basra Satellite Image). 97. A.15. The CR results of the DWT & the Proposed Hybrid methods (Erbil Satellite Image). 98. ©. C. O. PY. R. IG. H. T. U. PM. A.12. xvii.

(23) LIST OF ABBREVIATIONS. Compression Ratio. PSNR. Peak Signal to Noise Ratio. MSE. Mean Square Error. DCT. Discrete Cosine Transform. 2D-DCT. Two Dimensional Discrete Cosine Transform. DWT. Discrete Wavelet Transform. 2D-DWT. Two Dimensional Discrete Wavelet Transform. B6. Band 6. NIR. Near Infrared. TIRS. Thermal Mapping and Estimated Soil Moisture. SWIR. Short Wave Infrared. JPEG. Joint Photographic Experts Group. U. T. H. IG. Radio Frequency Red Green Blue. PY. RGB. R. RF. PM. CR. Earth Observation. DPCM. Differential Pulse Code Modulation. O. EO. ©. C. CCSDS-LDC. Consultative Committee for Space Data SystemsLossless Data Compression. KLT. Karhunen-Loeve Transform. DFT. Discrete Fourier Transform. WHT. Walsh-Hadamard Transform. NASA. National Aeronautics and Space Administration. CNES. Centre National d’Etudes Spatiales. EZW. Embedded Zero Tree Wavelet algorithm xviii.

(24) Set Partitioning in Hierarchical Trees. ECG. Electrocardiogram signals. ZPSVD. Zero Padding Singular Values Decomposition. USGS. United States Geological Survey. ©. C. O. PY. R. IG. H. T. U. PM. SPIHT. xix.

(25) CHAPTER 1. INTRODUCTION. 1.1. Satellite Images. PM. Image sensors mounted on board satellites record the amounts of reflected energy from objects on the ground, remotely. This information is stored and transformed to an image format. Hence, this process is called Remote Sensing.. H. T. U. Optical remote sensing utilizes sensors that capture imaging data in multiple spectral bands, such as visible, near infrared and shortwave infrared, to create a data set for each image of the earth's surface. Hence, multispectral images are used in remote sensing to a large degree, and they are starting to be used in several other applications such as medical imagery and quality control, among others.. IG. Raw multispectral images captured by remote sensing satellites are considered very high in:. R. a. Resolution: the number of pixels for each image. b. Bit depth: the bits per pixel (bpp).. PY. Hence, the size of a grayscale satellite image may be as high as hundreds of megabytes, and the bit depth is usually represented by 16 bpp most of the time, due to the presence of many details, whereas a few tens of kilobytes with 8 bpp are usually enough to represent a regular grey scale image.. Satellite Image Compression. O. 1.2. ©. C. Image compression can be identified as decreasing the amount of data required to represent an image, for storage or processing purposes.. 1.2.1. The Need for Satellite Image Compression. Since resources are limited onboard small satellites i.e. satellites with a total mass of less than 500 kg, due to the limited capabilities of the hardware components available, as compared to the components on the ground, and since multispectral images are very large, image compression is needed in many space missions to reduce onboard data storage and transmission bandwidth requirements from the satellite to the ground station.. 1.

(26) Satellite Image Compression Categories. 1.2.2. Image compression schemes are divided into two main categories:. PM. a. Lossless image compression: The image can be compressed and decompressed without any information loss. b. Lossy image compression: Allows the image to be reconstructed with a tolerated amount of loss of information. However, it provides a much higher compression ratio than lossless compression, hence it will be used in this work.. H. T. U. High resolution satellite images cannot be dealt with in the same manner as low resolution images due to their large sizes (high resolution and bit depth), especially when taking into consideration the limited resources on-board small satellites. Hence, choosing the suitable compression technique for such images should be carefully considered, in order to achieve high compression ratio (CR) to decrease the bandwidth required to transmit data from the satellite to earth, while maintaining the important scientific information of the image when reconstructed on earth as much as possible.. ©. C. O. PY. R. IG. The Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) algorithms represent the primary stage for the most commonly used lossy image compression methods, such as JPEG (Joint Photographic Experts Group) and JPEG2000, as shown in Figure (1) that illustrates a basic transform-based lossy image compression system which has 3 main forward steps and 3 inverse steps. All the steps will be discussed in detail in Chapter 2. However, these transform methods have several weaknesses, such as blocking artifacts for DCT-based image compression methods, and high complexity for DWT-based methods.. Figure 1.1 : A basic transform-based lossy image compression system The lossy step in this system is the 2nd quantization step, due to the rounding of coefficients that it includes which cannot be retrieved when the image is reconstructed. Hence, this step is not included in lossless image compression systems.. 2.

(27) Problem Statement. 1.3. Due to the limited on-board resources, satellite image compression is needed to decrease the onboard data storage (mass memory) and the downlink bandwidth. Hence, increasing the compression ratio is a primary goal to help decrease these resources.. H. T. U. PM. DCT and DWT-based compression methods have been proven to be the most commonly used lossy image compression techniques onboard satellites. However, these techniques have some drawbacks that should be addressed [1], [2], such as the appearance of blocking artefacts, and the limited values of CR for DCT, while for DWT, the fact that higher values of CR needs higher levels of DWT, which can cause an increase in computational complexity, a degradation of reconstruction quality. Hence a compression method that helps in addressing these drawbacks (without losing the benefits of the DCT or DWT methods) is needed. Hence, several methods that combined DCT and DWT were proposed in previous works to overcome their drawbacks, as in [3], [2], [4] and [5]. However, it was indicated that each method had one or more shortcomings, and none of them were implemented for high resolution satellite images.. R. IG. The DWT transform method have been proven to be more efficient for compression than the DCT transform. However, it includes embedding the important approximation coefficients within the thresholding and quantization processes. This may lead to a loss of some important information from the satellite image and adds more computations to the compression process.. Research Objectives. PY. 1.4. O. The objective of this research is to simulate an image compression system onboard a small satellite that can improve the compression process, through:. ©. C. 1. Presenting and implementing a proposed hybrid (DWT-DCT) algorithm that can exploit the positive properties, and decrease the effect of the negative properties of the DCT and DWT-based compression methods, to improve the compression process on-board satellites and increase the compression ratio. 2. Employing the zero-padding technique that only deals with the DWT detail coefficients, in the DWT and the proposed hybrid compression methods. This helps to maintain the important information of the satellite image, and decrease the time required for compression.. 3. Verifying the feasibility of employing the proposed hybrid method on-board satellites, by comparing it with the standalone DCT and DWT algorithms.. 3.

(28) Scope of the Study. 1.5. T. U. PM. The current work will present and implement a proposed Hybrid (DWT-DCT) compression method for high resolution satellite images of spectral band 6, simulating an image compression process onboard a small satellite and image decompression at the ground station, with the aim of increasing the compression ratio in order to decrease the data storage (memory) onboard satellites and the RF bandwidth required to transmit data from the satellite to the ground station. This can be achieved while maintaining a reasonably good quality of the reconstructed image at the ground station, preserving its important scientific information through employing the zeropadding technique in the DWT part of the proposed method, as a replacement for the regular DWT thresholding and quantization processes. Due to the absence of these processes, employing the zero-padding technique can also help to decrease the compression time. Hence, the proposed hybrid method can make use of the good characteristics of both the DWT and DCT transform algorithms, while trying to avoid their undesirable characteristics by applying 2D-DCT after 2 levels of 2D-DWT and employing the zero-padding technique.. 1.6. Thesis Layout. IG. H. The proposed method will also be compared with the standalone DWT and DCT methods in order to check the feasibility of using it onboard satellites.. Chapter 1 includes an introduction to satellite images, satellite image compression and why it is needed, image compression categories, an overview of a basic lossy image compression system, and the current work’s problem statement, objectives and scope. Chapter 2 describes the literature review of the current work in detail, containing sections that identify satellite-based remote sensing, an overview of the image compression techniques used on-board satellites, main lossy compression algorithms such as the DCT and DWT algorithms, and hybrid algorithms used in image compression are also presented. Chapter 3 discusses the methodology used during the current research work. Chapter 4 presents the results and comparisons of the current work, discussing them objectively (using charts and tables) and subjectively (visually). Chapter 5 gives conclusions of the current work, summarizing its contribution. This chapter also includes several recommendations for future research related to it.. PY. . R. This thesis includes five chapters, in addition to a number of appendices, as follows:. O. . ©. C.   . 4.

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