Evaluation of Lossy Compressed Mosaic for SPOT-6/7 Remote Sensing Data in SPACeMAP

Agnes Payani - Remote Sensing Technology and Data Center, National Institute of Aeronautics and Space (LAPAN), East Jakarta, 13710, Indonesia
Siti Wahyuningsih - Remote Sensing Technology and Data Center, National Institute of Aeronautics and Space (LAPAN), East Jakarta, 13710, Indonesia
Gusti Yudha - Remote Sensing Technology and Data Center, National Institute of Aeronautics and Space (LAPAN), East Jakarta, 13710, Indonesia
Nico Cendiana - Remote Sensing Technology and Data Center, National Institute of Aeronautics and Space (LAPAN), East Jakarta, 13710, Indonesia
Hanna Afida - Remote Sensing Technology and Data Center, National Institute of Aeronautics and Space (LAPAN), East Jakarta, 13710, Indonesia
Steward Augusto - Remote Sensing Technology and Data Center, National Institute of Aeronautics and Space (LAPAN), East Jakarta, 13710, Indonesia

Citation Format:

DOI: http://dx.doi.org/10.30630/joiv.5.3.507


SPACeMAP is a remote-sensing data portal system owned by LAPAN used to distribute mosaic data of Medium-Resolution to Very-High-Resolution for Provincial Governments. The frequently arising problem is that mosaic images have very large data size, especially for SPOT-6/7 mosaic images. The increasing number of data and users may affect the data loading process on the portal so that mosaic data compression can be considered. SPACeMAP has the Image Compressor feature using the Tile and Line algorithms with a compression ratio (target rate) recommended for optics (15 to 20). This study aims to determine the best algorithm and target rate to get compressed mosaic SPOT-6/7 imagery. The comparison method was done qualitatively through visual comparison and quantitatively by using Compression Ratio (CR), Bit per Pixel (BPP), and Peak Signal to Noise Ratio (PSNR).  Results of the experiment show that, quantitatively, both Tile and Line algorithms give a different performance, depends on the zoom level and land cover characteristics. In terms of the qualitative result, the Tile algorithm gives better overall results compare to the Line algorithm. Quantitatively, both algorithms show good performance in the homogenous area. The target rate difference on the testing range does not affect process duration, nevertheless, the Line algorithm has a long process duration compare to the Tile algorithm. However, compression mosaics with lower or higher resolution remote sensing data may provide different results. Hence, this need be addressed on further studies.


Compression; tile; line; geoportal; mosaic.

Full Text:



J. de la Beaujardiere, “OpenGIS® Web Map Server Implementation Specification,†2006.

H. K. Ramapriyan, "Standard-based Data and Information Systems for Earth Observation," Lect. Notes Geoinf. Cartogr., no. 9783540882633, pp. 37–61, 2010.

G. Percivall and T. Taylor, "Connecting the Internet of Things to the eo community and the geospatially enabled web using OGC standards," in International Geoscience and Remote Sensing Symposium (IGARSS), 2017, vol. 2017–July, pp. 5577–5580.

R. Jusuf, G. D. Yudha, M. I. Oktavia, and S. E. Siwi, "Increase in Response and Load Time Image To Display Remote Sensing Satellite Image on Web Gis Provincial Based Earth Monitoring System Application," J. Penginderaan Jauh dan Pengolah. Data Citra Digit., vol. 15, no. 2, pp. 101–110, 2018.

R. M. Awangga, "Sampeu: Servicing Web Map Tile Service over Web Map Service to Increase Computation Performance," IOP Conf. Ser. Earth Environ. Sci., vol. 145, no. 1, 2018.

R. D. Dimyati, P. Danoedoro, Hartono, and Kustiyo, "A Minimum Cloud Cover Mosaic Image Model of the Operational Land Imager Landsat-8 Multitemporal Data Using Tile Based," Int. J. Electr. Comput. Eng., vol. 8, no. 1, pp. 360–371, 2018.

M. Swaine et al., "Operational pipeline for a global cloud-free mosaic and classification of sentinel-2 images," in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2020, vol. 43, no. B3, pp. 195–200.

J. D. Shepherd, J. Schindler, and J. R. Dymond, "Automated Mosaicking of Sentinel-2 Satellite Imagery," Remote Sens., vol. 12, no. 22, pp. 1–14, 2020.

H. Li et al., "A Google Earth Engine-enabled software for efficiently generating high-quality user-ready Landsat mosaic images," Environ. Model. Softw., vol. 112, no. March 2018, pp. 16–22, 2019.

R. D. Dimyati, P. Danoedoro, Hartono, Kustiyo, and M. Dimyati, "Digital Interpretability of Annual Tile-based Mosaic of Landsat-8 OLI for Time-series Land Cover Analysis in the Central Part of Sumatra," Indones. J. Geogr., vol. 50, no. 2, pp. 168–184, 2019.

R. D. Dimyati and Projo Danoedoro, “Pengembangan Model Citra Mosaik Tahunan Tile-Based Mosaic ( TBM ) Landsat-8 Oli,†Universitas Gadjah Mada, 2019.

D. H. Sulyantara, K. Ulfa, R. P. Brahmantara, S. E. Siwi, Y. Prabowo, and C. Rangkuti, “Pengembangan Mosaik Data Spot 6/7 Menggunakan Metode Tile Based Berdasarkan Haze Index,†Media Komun. Geogr., vol. 21, no. 1, p. 40, 2020.

D. M. Chandler, "Seven Challenges in Image Quality Assessment: Past, Present, and Future Research," ISRN Signal Process., vol. 2013, pp. 1–53, 2013.

M. Herńandez-Cabronero, M. W. Marcellin, I. Blanes, and J. Serra-Sagrista, "Lossless compression of color filter array mosaic images with visualization via JPEG 2000," IEEE Trans. Multimed., vol. 20, no. 2, pp. 257–270, 2018.

M. Hernandez-Cabronero, V. Sanchez, I. Blanes, F. Auli-Llinas, M. W. Marcellin, and J. Serra-Sagrista, "Mosaic-based color-transform optimization for lossy and lossy-to-lossless compression of pathology whole-slide images," IEEE Trans. Med. Imaging, vol. 38, no. 1, pp. 21–32, 2019.

Z. Chen, Y. Hu, and Y. Zhang, "Effects of Compression on Remote Sensing Image Classification Based on Fractal Analysis," IEEE Trans. Geosci. Remote Sens., vol. 57, no. 7, pp. 4577–4590, 2019.

J. Triglav, "Wavelet Compression Beyond Limits?," GeoInformatics Magazine, vol. 3, no. February, Europe, pp. 11–15, 2000.

X. Xie et al., "A study on Fast SIFT Image Mosaic Algorithm Based on Compressed Sensing and Wavelet Transform," J. Ambient Intell. Humaniz. Comput., vol. 6, no. 6, pp. 835–843, 2015.

S. Karim, Y. Zhang, S. Yin, A. A. Laghari, and A. A. Brohi, "Impact of compressed and down-scaled training images on vehicle detection in remote sensing imagery," Multimed. Tools Appl., vol. 78, no. 22, pp. 32565–32583, 2019.

O. Gumelar, R. M. Saputra, G. D. Yudha, A. S. Payani, and S. D. Wahyuningsih, "Remote Sensing Image Transformation with Cosine and Wavelet Method for SPACeMAP Visualization," in IOP Conference Series: Earth and Environmental Science, 2020, vol. 500, no. 1.

Hexagon Geospatial, "GeoCompressor User Guide," Hexagon Geospatial, vol. 0, no. September. pp. 169–232, 2018.

S. S. R. R Kousalyadevi, "Performance Analysis of Multi Spectral Band Image Compression using Discrete Wavelet Transform," J. Comput. Sci., vol. 8, no. 5, pp. 789–795, 2012.

R. Praisline Jasmi, B. Perumal, and M. Pallikonda Rajasekaran, "Comparison of Image Compression Techniques using Huffman Coding, DWT and Fractal Algorithm," in International Conference on Computer Communication and Informatics, ICCCI 2015, 2015, pp. 1–5.

A. J. Abboud, A. N. Albu-Rghaif, and A. K. Jassim, "Balancing compression and encryption of satellite imagery," Int. J. Electr. Comput. Eng., vol. 8, no. 5, pp. 3568–3586, 2018.

R. Bausys and Giruta Kazakeviciute-Januskeviciene, "Qualitative Rating of Lossy Compression for Aerial Imagery by Neutrosophic WASPAS Method," Symmetry (Basel)., pp. 1–26, 2021.

S. G. A. Raake, "Evaluation of Intra-Coding Based Image Compression," in 8th European Workshop on Visual Information Processing (EUVIP), 2019, pp. 169–174.