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COMPRESSION METHODS FOR SATELLITE IMAGES USING WAVELET TRANSFORM AND PERFORMANCE EVALUATION

Yıl 2024, Cilt: 8 Sayı: 2, 72 - 81
https://doi.org/10.46460/ijiea.1440970

Öz

Research on image compression spans various fields, focusing on achieving efficient compression while preserving a specific image quality. Satellite images captured by observation satellites possess unique characteristics distinct from other images. Analyzing these specific qualities is decisive, leading to the proposal of tailored compression methods and transforms suitable for satellite image characteristics. This study comprehensively assesses the performance of six well-known compression methods in the literature, utilizing wavelet transform and metrics such as bits per pixel (BPP), compression ratio (CR), Peak Signal-to-Noise Ratio (PSNR), calculation time (CT), and Mean Squared Error (MSE). The compressed satellite images, generated through six methods and the Coif3 wavelet, are systematically compared and evaluated using performance metrics. The average values obtained for all six methods are 96.37%, 47.10 dB, and 7.92 seconds for CR, PSNR, and CT receptively, while WDR exhibits CR at 96.36%, PSNR at 48.84 dB, and CT at 6.58 seconds. The findings indicate that the Wavelet Difference Reduction (WDR) compression method utilizing the Coif3 wavelet outperforms others when considering all parameters together. We suggest that operators and manufacturers choose wavelet transform and WDR compression methods for effective compression of observation satellite images to achieve optimal results.

Kaynakça

  • [1] Othman, G., & Zeebaree, D. Q. (2020). The Applications of Discrete Wavelet Transform in Image processing: A review. Journal of soft computing and data mining, 1(2), 31-43.
  • [2] Indradjad, A., Nasution, A. S., Gunawan, H., & Widipaminto, A. (2019). A comparison of Satellite Image Compression methods in the Wavelet Domain. In IOP Conference Series: Earth and Environmental Science (Vol. 280, No. 1, p. 012031). IOP Publishing.
  • [3] De Oliveira, V. A., Chabert, M., Oberlin, T., Poulliat, C., Bruno, M., Latry, C., ... & Camarero, R. (2022). Satellite Image Compression and Denoising with Neural Networks. IEEE Geoscience and Remote Sensing Letters, 19, 1-5.
  • [4] Delaunay, X., Chabert, M., Charvillat, V., & Morin, G. (2010). Satellite Image Compression by Post-Transforms in the Wavelet Domain. Signal processing, 90(2), 599-610.
  • [5] Teke, M. (2016). Satellite Image Processing Workflow for RASAT and Göktürk-2. Journal of Aeronautics and Space Technologies, 9(1), 1-13.
  • [6] Taş, İ. Ç. Application of Panoramic Dental X-Ray Images Denoising. International Journal of Innovative Engineering Applications, 7(1), 13-20.
  • [7] Toraman, S., & Turkoglu, I. (2020). Using Wavelet Transform and Machine Learning Techniques, a Wew Method for Classifying Colon Cancer Patients and Healthy People from FTIR Signals. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(2), 933-942.
  • [8] Vura, S., Patil, P., & Patil, S. B. (2023). A Study of Different Compression Algorithms for Multispectral Images. Materials Today: Proceedings, 80, 2193-2197.
  • [9] Kitaeff, V. V., Cannon, A., Wicenec, A., & Taubman, D. (2015). Astronomical Imagery: Considerations for a Contemporary Approach with JPEG2000. Astronomy and Computing, 12, 229-239.
  • [10] Ma, X. (2023). High-resolution Image Compression Algorithms in Remote Sensing Imaging. Displays, 102462.
  • [11] Yu, G., Vladimirova, T., & Sweeting, M. N. (2009). Image Compression Systems on Board Satellites. Acta Astronautica, 64(9-10), 988-1005.
  • [12] Liao, L., Xiao, J., Li, Y., Wang, M., & Hu, R. (2020). Learned Representation of Satellite Image Series for Data Compression. Remote Sensing, 12(3), 497.
  • [13] Shihab, H. S., Shafie, S., Ramli, A. R., & Ahmad, F. (2017). Enhancement of Satellite Image Compression Using a Hybrid (DWT–DCT) Algorithm. Sensing and Imaging, 18, 1-30.
  • [14] Swetha, V., Patil, G. P., & Patil, B. S. (2021, July). Lossless Compression of Satellite Images using a Versatile Hybrid Algorithm. In IOP Conference Series: Materials Science and Engineering (Vol. 1166, No. 1, p. 012048). IOP Publishing.
  • [15] Bacchus, P., Fraisse, R., Roumy, A., & Guillemot, C. (2022, July). Quasi Lossless Satellite Image Compression. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 1532-1535). IEEE.
  • [16] Jamuna Rani, M., & Azhagu Jaisudhan Pazhani, A. (2022). Computational Efficient Compression Scheme for Satellite Images. Earth Science Informatics, 15(3), 1723-1736.
  • [17] Faria, L. N., Fonseca, L. M., & Costa, M. H. (2012). Performance Evaluation of Data Compression Systems Applied to Satellite Imagery. Journal of Electrical and Computer Engineering, 2012, 18-18.
  • [18] Hagag, A., Hassan, E. S., Amin, M., Abd El-Samie, F. E., & Fan, X. (2017). Satellite Multispectral Image Compression Based on Removing Sub-bands. Optik, 131, 1023-1035.
  • [19] Zhang, W., Li, D., Zhang, H., Yu, P., & Gao, W. (2024). Lightweight Bit-Depth Recovery Network for Gaofen Satellite Multispectral Image Compression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
  • [20] Wang, K., Jia, J., Zhou, P., Ma, H., Yang, L., Liu, K., & Li, Y. (2024). Efficient Onboard Compression for Arbitrary-Shaped Cloud-Covered Remote Sensing Images via Adaptive Filling and Controllable Quantization. Remote Sensing, 16(18).
  • [21] Yilmaz, Ö., Aksoy, M., Kesilmiş, Z. (2019). Misalignment Fault Detection by Wavelet Analysis of Vibration Signals. International Advanced Researches and Engineering Journal, 3(3), 156-163.
  • [22] Oz, I., Oz, C., Yumusak, N. (2001) Image Compression Using 2-D Multiple-Level Discrete Wavelet Transform (DWT). Eleco 2001 International Conference on Electrical and Electronics Engineering, Turkey
  • [23] Akmaz, D. (2022). Recognition Of Power Quality Events Using Wavelet Transform, K-Nearest Neighbor Algorithm And Gain Ratio Feature Selection Method. International Journal of Innovative Engineering Applications, 6(1), 42-47.
  • [24] Oz, I.(2006). Image and Video Compression by Using Two Dimensional Wavelet Transform (Doctoral dissertation, Sakarya University).
  • [25] Saken, M., Yağci, M. B., & Yumuşak, N. (2021). Impact of Image Segmentation Techniques on Celiac Disease Classification Usingscale Invariant Texture Descriptors for Standard Flexible Endoscopic Systems. Turkish Journal of Electrical Engineering and Computer Sciences, 29(2), 598-615.
  • [26] Coşkun, M., Gürüler, H., Istanbullu, A., Peker, M. (2015). Determining the Appropriate Amount of Anesthetic Gas Using DWT and EMD Combined with Neural Network. Journal of medical systems, 39, 1-10.
  • [27] Coşkun, M., & İstanbullu, A. (2012). EEG İşaretlerinin FFT ve Dalgacık Dönüşümü ile Analizi. XIV. Akademik Bilişim Konferansı, Uşak, Türkiye.
  • [28] Akay, M., & Tuncer, T. (2021). Çok Seviyeli Dalgacık Dönüşümü ve Yerel İkili Örüntüler Tabanlı Otomatik EEG Duygu Tanıma Yöntemi. International Journal of Innovative Engineering Applications, 5(2), 75-80.
  • [29] Şengür, A., Türkoğlu, İ., & Ince, M. C. (2006). A Comparative Study on Entropic Thresholding Methods. IU-Journal of Electrical & Electronics Engineering, 6(2), 183-188.
  • [30] Yumusak, N., Temurtas, F., Cerezci, O., & Pazar, S. (1998, August). Image thresholding using measures of fuzziness. In IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No. 98CH36200) (Vol. 3, pp. 1300-1305). IEEE.
  • [31] Černá, D., Finěk, V., & Najzar, K. (2008). On the exact values of coefficients of coiflets. Open Mathematics, 6(1), 159-169.
  • [32] Taher, M. M., & Redha, S. M. (2022). Use The Coiflets and Daubechies Wavelet Transform To Reduce Data Noise For a Simple Experiment. Iraqi Journal of Statistical Sciences, 19(2), 91-103.
  • [33] Kumar, R., & Singh, S. (2014). Comparative Analysis of Wavelet Based Compression Methods. International Journal of Computer Networking, Wireless and Mobile Communications, 143-150.
  • [34] Oz, I. (2024). Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet. Turkish Journal of Science and Technology, 19(1), 279-294. https://doi.org/10.55525/tjst.1428424
  • [35] Marangoz, A. M., Sefercik, U. G., & Damla, YÜCE, (2020). Three-dimensional earth modelling performance analysis of Gokturk-2 satellite. Turkish Journal of Engineering, 4(3), 164-168.
  • [36] Wang, K., Jia, J., Zhou, P., Ma, H., Yang, L., Liu, K., & Li, Y. (2024). Efficient Onboard Compression for Arbitrary-Shaped Cloud-Covered Remote Sensing Images via Adaptive Filling and Controllable Quantization. Remote Sensing, 16(18).

DALGACIK DÖNÜŞÜMÜ İLE UYDU GÖRÜNTÜSÜ SIKIŞTIRMA METOTLARI VE PERFORMANS DEĞERLENDİRMESİ

Yıl 2024, Cilt: 8 Sayı: 2, 72 - 81
https://doi.org/10.46460/ijiea.1440970

Öz

Görüntü sıkıştırma üzerine birçok alanda araştırma yapılmakta ve hedef belirli bir görüntü kalitesini korurken iyi bir sıkıştırma oranı elde etmektir. Gözlem uyduları tarafından çekilen uydu görüntüleri, diğer görüntülerden farklı özelliklere sahiptir. Bu özelliklerin analizi ile bu alana özgü dönüşüm ve sıkıştırma teknikleri geliştirilebilir. Bu çalışmada uydu görüntüsü sıkıştırılmış, dalgacık dönüşümü ve literatürde çok bilinen altı sıkıştırma yönteminin performansı; piksel başına bit (PBB), sıkıştırma oranı (SO), tepe sinyal gürültü oranı (TSGO), hesaplama süresi (HS) ve ortalama kare hata (OKH) gibi ölçütler kullanarak kapsamlı bir şekilde değerlendirilmiştir. Coif3 dalgacık dönüşümü ve bu altı sıkıştırma metodu kullanılarak elde edilen sıkıştırılmış uydu görüntüsü sistematik olarak karşılaştırılırmış ve değerlendirilmiştir. Altı yöntemin ortalama değerleri SO için %96.37, TSGO için %47.10 db ve HS için7.92 saniye iken, WDR metodunda SO, %96.36, TSGO %48.34 db ve HS 6.58 saniye olarak elde edilmiştir. Bulgular, Coif3 dalgacık dönüşümü kullanan WDR sıkıştırma yönteminin, tüm performans parametreleri dikkate alındığında diğer yöntemleri geride bıraktığını göstermektedir. Bu çalışma sonuçlarına göre uydu operatörleri ve işletmecilerine gözlem uydusu görüntüsü sıkıştırma işleminde başarılı sonuçlarından dolayıdalgacık dönüşümü ve WDR metodunu öneriyoruz.

Kaynakça

  • [1] Othman, G., & Zeebaree, D. Q. (2020). The Applications of Discrete Wavelet Transform in Image processing: A review. Journal of soft computing and data mining, 1(2), 31-43.
  • [2] Indradjad, A., Nasution, A. S., Gunawan, H., & Widipaminto, A. (2019). A comparison of Satellite Image Compression methods in the Wavelet Domain. In IOP Conference Series: Earth and Environmental Science (Vol. 280, No. 1, p. 012031). IOP Publishing.
  • [3] De Oliveira, V. A., Chabert, M., Oberlin, T., Poulliat, C., Bruno, M., Latry, C., ... & Camarero, R. (2022). Satellite Image Compression and Denoising with Neural Networks. IEEE Geoscience and Remote Sensing Letters, 19, 1-5.
  • [4] Delaunay, X., Chabert, M., Charvillat, V., & Morin, G. (2010). Satellite Image Compression by Post-Transforms in the Wavelet Domain. Signal processing, 90(2), 599-610.
  • [5] Teke, M. (2016). Satellite Image Processing Workflow for RASAT and Göktürk-2. Journal of Aeronautics and Space Technologies, 9(1), 1-13.
  • [6] Taş, İ. Ç. Application of Panoramic Dental X-Ray Images Denoising. International Journal of Innovative Engineering Applications, 7(1), 13-20.
  • [7] Toraman, S., & Turkoglu, I. (2020). Using Wavelet Transform and Machine Learning Techniques, a Wew Method for Classifying Colon Cancer Patients and Healthy People from FTIR Signals. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(2), 933-942.
  • [8] Vura, S., Patil, P., & Patil, S. B. (2023). A Study of Different Compression Algorithms for Multispectral Images. Materials Today: Proceedings, 80, 2193-2197.
  • [9] Kitaeff, V. V., Cannon, A., Wicenec, A., & Taubman, D. (2015). Astronomical Imagery: Considerations for a Contemporary Approach with JPEG2000. Astronomy and Computing, 12, 229-239.
  • [10] Ma, X. (2023). High-resolution Image Compression Algorithms in Remote Sensing Imaging. Displays, 102462.
  • [11] Yu, G., Vladimirova, T., & Sweeting, M. N. (2009). Image Compression Systems on Board Satellites. Acta Astronautica, 64(9-10), 988-1005.
  • [12] Liao, L., Xiao, J., Li, Y., Wang, M., & Hu, R. (2020). Learned Representation of Satellite Image Series for Data Compression. Remote Sensing, 12(3), 497.
  • [13] Shihab, H. S., Shafie, S., Ramli, A. R., & Ahmad, F. (2017). Enhancement of Satellite Image Compression Using a Hybrid (DWT–DCT) Algorithm. Sensing and Imaging, 18, 1-30.
  • [14] Swetha, V., Patil, G. P., & Patil, B. S. (2021, July). Lossless Compression of Satellite Images using a Versatile Hybrid Algorithm. In IOP Conference Series: Materials Science and Engineering (Vol. 1166, No. 1, p. 012048). IOP Publishing.
  • [15] Bacchus, P., Fraisse, R., Roumy, A., & Guillemot, C. (2022, July). Quasi Lossless Satellite Image Compression. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 1532-1535). IEEE.
  • [16] Jamuna Rani, M., & Azhagu Jaisudhan Pazhani, A. (2022). Computational Efficient Compression Scheme for Satellite Images. Earth Science Informatics, 15(3), 1723-1736.
  • [17] Faria, L. N., Fonseca, L. M., & Costa, M. H. (2012). Performance Evaluation of Data Compression Systems Applied to Satellite Imagery. Journal of Electrical and Computer Engineering, 2012, 18-18.
  • [18] Hagag, A., Hassan, E. S., Amin, M., Abd El-Samie, F. E., & Fan, X. (2017). Satellite Multispectral Image Compression Based on Removing Sub-bands. Optik, 131, 1023-1035.
  • [19] Zhang, W., Li, D., Zhang, H., Yu, P., & Gao, W. (2024). Lightweight Bit-Depth Recovery Network for Gaofen Satellite Multispectral Image Compression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
  • [20] Wang, K., Jia, J., Zhou, P., Ma, H., Yang, L., Liu, K., & Li, Y. (2024). Efficient Onboard Compression for Arbitrary-Shaped Cloud-Covered Remote Sensing Images via Adaptive Filling and Controllable Quantization. Remote Sensing, 16(18).
  • [21] Yilmaz, Ö., Aksoy, M., Kesilmiş, Z. (2019). Misalignment Fault Detection by Wavelet Analysis of Vibration Signals. International Advanced Researches and Engineering Journal, 3(3), 156-163.
  • [22] Oz, I., Oz, C., Yumusak, N. (2001) Image Compression Using 2-D Multiple-Level Discrete Wavelet Transform (DWT). Eleco 2001 International Conference on Electrical and Electronics Engineering, Turkey
  • [23] Akmaz, D. (2022). Recognition Of Power Quality Events Using Wavelet Transform, K-Nearest Neighbor Algorithm And Gain Ratio Feature Selection Method. International Journal of Innovative Engineering Applications, 6(1), 42-47.
  • [24] Oz, I.(2006). Image and Video Compression by Using Two Dimensional Wavelet Transform (Doctoral dissertation, Sakarya University).
  • [25] Saken, M., Yağci, M. B., & Yumuşak, N. (2021). Impact of Image Segmentation Techniques on Celiac Disease Classification Usingscale Invariant Texture Descriptors for Standard Flexible Endoscopic Systems. Turkish Journal of Electrical Engineering and Computer Sciences, 29(2), 598-615.
  • [26] Coşkun, M., Gürüler, H., Istanbullu, A., Peker, M. (2015). Determining the Appropriate Amount of Anesthetic Gas Using DWT and EMD Combined with Neural Network. Journal of medical systems, 39, 1-10.
  • [27] Coşkun, M., & İstanbullu, A. (2012). EEG İşaretlerinin FFT ve Dalgacık Dönüşümü ile Analizi. XIV. Akademik Bilişim Konferansı, Uşak, Türkiye.
  • [28] Akay, M., & Tuncer, T. (2021). Çok Seviyeli Dalgacık Dönüşümü ve Yerel İkili Örüntüler Tabanlı Otomatik EEG Duygu Tanıma Yöntemi. International Journal of Innovative Engineering Applications, 5(2), 75-80.
  • [29] Şengür, A., Türkoğlu, İ., & Ince, M. C. (2006). A Comparative Study on Entropic Thresholding Methods. IU-Journal of Electrical & Electronics Engineering, 6(2), 183-188.
  • [30] Yumusak, N., Temurtas, F., Cerezci, O., & Pazar, S. (1998, August). Image thresholding using measures of fuzziness. In IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No. 98CH36200) (Vol. 3, pp. 1300-1305). IEEE.
  • [31] Černá, D., Finěk, V., & Najzar, K. (2008). On the exact values of coefficients of coiflets. Open Mathematics, 6(1), 159-169.
  • [32] Taher, M. M., & Redha, S. M. (2022). Use The Coiflets and Daubechies Wavelet Transform To Reduce Data Noise For a Simple Experiment. Iraqi Journal of Statistical Sciences, 19(2), 91-103.
  • [33] Kumar, R., & Singh, S. (2014). Comparative Analysis of Wavelet Based Compression Methods. International Journal of Computer Networking, Wireless and Mobile Communications, 143-150.
  • [34] Oz, I. (2024). Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet. Turkish Journal of Science and Technology, 19(1), 279-294. https://doi.org/10.55525/tjst.1428424
  • [35] Marangoz, A. M., Sefercik, U. G., & Damla, YÜCE, (2020). Three-dimensional earth modelling performance analysis of Gokturk-2 satellite. Turkish Journal of Engineering, 4(3), 164-168.
  • [36] Wang, K., Jia, J., Zhou, P., Ma, H., Yang, L., Liu, K., & Li, Y. (2024). Efficient Onboard Compression for Arbitrary-Shaped Cloud-Covered Remote Sensing Images via Adaptive Filling and Controllable Quantization. Remote Sensing, 16(18).
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

İbrahim Öz 0000-0003-4593-917X

Erken Görünüm Tarihi 27 Aralık 2024
Yayımlanma Tarihi
Gönderilme Tarihi 21 Şubat 2024
Kabul Tarihi 16 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 8 Sayı: 2

Kaynak Göster

APA Öz, İ. (2024). COMPRESSION METHODS FOR SATELLITE IMAGES USING WAVELET TRANSFORM AND PERFORMANCE EVALUATION. International Journal of Innovative Engineering Applications, 8(2), 72-81. https://doi.org/10.46460/ijiea.1440970
AMA Öz İ. COMPRESSION METHODS FOR SATELLITE IMAGES USING WAVELET TRANSFORM AND PERFORMANCE EVALUATION. ijiea, IJIEA. Aralık 2024;8(2):72-81. doi:10.46460/ijiea.1440970
Chicago Öz, İbrahim. “COMPRESSION METHODS FOR SATELLITE IMAGES USING WAVELET TRANSFORM AND PERFORMANCE EVALUATION”. International Journal of Innovative Engineering Applications 8, sy. 2 (Aralık 2024): 72-81. https://doi.org/10.46460/ijiea.1440970.
EndNote Öz İ (01 Aralık 2024) COMPRESSION METHODS FOR SATELLITE IMAGES USING WAVELET TRANSFORM AND PERFORMANCE EVALUATION. International Journal of Innovative Engineering Applications 8 2 72–81.
IEEE İ. Öz, “COMPRESSION METHODS FOR SATELLITE IMAGES USING WAVELET TRANSFORM AND PERFORMANCE EVALUATION”, ijiea, IJIEA, c. 8, sy. 2, ss. 72–81, 2024, doi: 10.46460/ijiea.1440970.
ISNAD Öz, İbrahim. “COMPRESSION METHODS FOR SATELLITE IMAGES USING WAVELET TRANSFORM AND PERFORMANCE EVALUATION”. International Journal of Innovative Engineering Applications 8/2 (Aralık 2024), 72-81. https://doi.org/10.46460/ijiea.1440970.
JAMA Öz İ. COMPRESSION METHODS FOR SATELLITE IMAGES USING WAVELET TRANSFORM AND PERFORMANCE EVALUATION. ijiea, IJIEA. 2024;8:72–81.
MLA Öz, İbrahim. “COMPRESSION METHODS FOR SATELLITE IMAGES USING WAVELET TRANSFORM AND PERFORMANCE EVALUATION”. International Journal of Innovative Engineering Applications, c. 8, sy. 2, 2024, ss. 72-81, doi:10.46460/ijiea.1440970.
Vancouver Öz İ. COMPRESSION METHODS FOR SATELLITE IMAGES USING WAVELET TRANSFORM AND PERFORMANCE EVALUATION. ijiea, IJIEA. 2024;8(2):72-81.