CFP last date
20 March 2024
Reseach Article

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

by Sowmya D. R., P. Deepa Shenoy, Venugopal K. R.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 161 - Number 11
Year of Publication: 2017
Authors: Sowmya D. R., P. Deepa Shenoy, Venugopal K. R.
10.5120/ijca2017913306

Sowmya D. R., P. Deepa Shenoy, Venugopal K. R. . Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey. International Journal of Computer Applications. 161, 11 ( Mar 2017), 24-37. DOI=10.5120/ijca2017913306

@article{ 10.5120/ijca2017913306,
author = { Sowmya D. R., P. Deepa Shenoy, Venugopal K. R. },
title = { Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 161 },
number = { 11 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 24-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume161/number11/27193-2017913306/ },
doi = { 10.5120/ijca2017913306 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:07:13.052687+05:30
%A Sowmya D. R.
%A P. Deepa Shenoy
%A Venugopal K. R.
%T Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 161
%N 11
%P 24-37
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. In remote sensing, the image processing techniques can be categories in to four main processing stages: Image pre-processing, Enhancement, Transformation and Classification. Image pre-processing is the initial processing which deals with correcting radiometric distortions, atmospheric distortion and geometric distortions present in the raw image data. Enhancement techniques are applied to preprocessed data in order to effectively display the image for visual interpretation. It includes techniques to effectively distinguish surface features for visual interpretation. Transformation aims to identify particular feature of earth’s surface and classification is a process of grouping the pixels, that produces effective thematic map of particular land use and land cover.

References
  1. F. Santi, M. Bucciarelli, D. Pastina, M. Antoniou, and M. Cherniakov, “Spatial Resolution Improvement in GNSS-Based SAR using Multistatic Acquisitions and Feature Extraction,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 10, pp. 6217–6231, 2016.
  2. Q. Wei, J. Bioucas-Dias, N. Dobigeon, and J.-Y. Tourneret, “Hyperspectral and Multispectral Image Fusion based on a Sparse Representation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 7, pp. 3658–3668, 2015.
  3. J. Zhao, Y. Zhong, H. Shu, and L. Zhang, “High-Resolution Image Classification Integrating Spectral-Spatial-Location Cues by Conditional Random Fields,” IEEE Transactions on Image Processing, vol. 25, no. 9, pp. 4033–4045, 2016.
  4. J. Ahlberg, “Optimizing Object, Atmosphere, and Sensor Parameters in Thermal Hyperspectral Imagery,” IEEE Transactions on Geoscience and Remote Sensing, 2016.
  5. E. Nova, J. Romeu, F. Torres, M. Pablos, J. M. Riera, A. Broquetas, and l. Jofre, “Radiometric and Spatial Resolution Constraints in Mmillimeter-Wave Close-Range Passive Screener Systems,” IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 4, pp. 2327–2336, 2013.
  6. H. Shen, X. Meng, and L. Zhang, “An Integrated Framework for the Spatio–Temporal–Spectral Fusion of Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 12, pp. 7135–7148, 2016.
  7. J. M. Rao, C. Rao, A. S. Kumar, B. Lakshmi, and V. Dadhwal, “Spatiotemporal Data Fusion using Temporal High-Pass Modulation and Edge Primitives,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 11, pp. 5853–5860, 2015.
  8. Z. Bian, Q. Xiao, B. Cao, Y. Du, H. Li, H. Wang, Q. Liu, and Q. Liu, “Retrieval of Leaf, Sunlit Soil, and Shaded Soil Component Temperatures using Airborne Thermal Infrared Multiangle Observations,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 8, pp. 4660–4671, 2016.
  9. Y. Han, H. Kim, J. Choi, and Y. kim, “A Shape Size Index Extraction for Classification of High Resolution Multispectral Satellite Images,” International Journal of Remote Sensing, vol. 33, no. 6, pp. 1682–1700, 2012.
  10. B. Yektakhah and K. Sarabandi, “All-Directions Through-the-Wall Radar Imaging using a Small Number of Moving Transceivers,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 11, pp. 6415–6428, 2016.
  11. Z. Chen, J. Pan, Y. He, and A. T. Devlin, “Estimate of Tidal Constituents in Nearshore Waters using X-Band Marine Radar Image Sequences,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 11, pp. 6700–6711, 2016.
  12. W. Huang, R. Carrasco, C. Shen, E. W. Gill, and J. Horstmann, “Surface Current Measurements using X-band Marine Radar with Vertical Polarization,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 5, pp. 2988–2997, 2016.
  13. N. Zhao, Y. Zhou, and E. L. Samson, “Correcting Incompatible DN Values and Geometric Errors in Nighttime Lights Time-Series Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 4, pp. 2039–2049, 2015.
  14. C. Devaraj and C. A. Shah, “Automated Geometric Correction of Landsat MSS L1G Imagery,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 1, pp. 347–351, 2014.
  15. R. Amin, D. Lewis, R. W. Gould, W. Hou, A. Lawson, M. Ondrusek, and R. Arnone, “Assessing the Application of Cloud–Shadow Atmospheric Correction Algorithm on HICO,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2646–2653, 2014.
  16. J. Jung, D.-j. Kim, and S.-E. Park, “Correction of Atmospheric Phase Screen in Time Series InSAR using WRF Model for Monitoring Volcanic Activities,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2678–2689, 2014.
  17. B. D. Bue, D. R. Thompson, M. Eastwood, R. O. Green, B.-C. Gao, D. Keymeulen, C. M. Sarture, A. S. Mazer, and H. H. Luong, “Real-Time Atmospheric Correction of AVIRIS-NG Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 12, pp. 6419–6428, 2015.
  18. M. Simard, B. V. Riel, M. Denbina, and S. Hensley, “Radiometric Correction of Airborne Radar Images over Forested Terrain with Topography,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 8, pp. 4488–4500, 2016.
  19. D. Frantz, A. R¨oder, M. Stellmes, and J. Hill, “An Operational Radiometric Landsat Preprocessing Framework for Large-Area Time Series Applications,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 7, pp. 3928–3943, 2016.
  20. D. R. Doelling, A. Wu, X. Xiong, B. R. Scarino, R. Bhatt, C. O. Haney, D. Morstad, and A. Gopalan, “The Radiometric Stability and Scaling of Collection 6 Terra-and Aqua-MODIS VIS, NIR, and SWIR spectral bands,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 8, pp. 4520–4535, 2015.
  21. J.-L. Lisani, J. Michel, J.-M. Morel, A. B. Petro, and C. Sbert, “An Inquiry on Contrast Enhancement Methods for Satellite Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 12, pp.7044–7054, 2016.
  22. F. Lenti, F. Nunziata, C. Estatico, and M. Migliaccio, “Conjugate Gradient Method in Hilbert and Banach Spaces to Enhance the Spatial Resolution of Radiometer Data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 397–406, 2016.
  23. F. Lenti, F. Nunziata, M. Migliaccio, and G. Rodriguez, “Two- Dimensional TSVD to Enhance the Spatial Resolution of Radiometer Data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2450–2458, 2014.
  24. M. Piles, A. Camps, M. Vall-Llossera, and M. Talone, “Spatial-Resolution Enhancement of SMOS Data: A Deconvolution-based Approach,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 7, pp. 2182–2192, 2009.
  25. M. A. Bendoumi, M. He, and S. Mei, “Hyperspectral Image Resolution Enhancement Using High-Resolution Multispectral Image based on Spectral Unmixing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 10, pp. 6574–6583, 2014.
  26. F. Kowkabi, H. Ghassemian, and A. Keshavarz, “Enhancing Hyper spectral End member Extraction using Clustering and Over segmentation- Based Preprocessing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 8, pp. 4572–4583, 2015.
  27. L. Drumetz, S. Henrot, M. A. Veganzones, J. Chanussot, and C. Jutten, “Blind Hyperspectral Unmixing using an Extended Linear Mixing Model to Address Spectral Variability,” IEEE Transactions on Geoscience and Remote Sensing, vol. 25, no. 8, pp. 3890–3905, 2016.
  28. Y. Zang, C. Wang, Y. Yu, L. Luo, K. Yang, and J. Li, “Joint Enhancing Filtering for Road Network Extraction,” IEEE Transactions on Geoscience and Remote Sensing, 2016.
  29. X. Zhang, B. Xiong, G. Kuang, and W. Xu, “A Geometric Parameter Extraction Method of Ship Target based on an Improved Snake Model,” IEEE Transactions on Geoscience and Remote Sensing, pp. 3152–3155, 2016.
  30. L. Carrer and L. Bruzzone, “Automatic Enhancement and Detection of Layering in Radar Sounder Data Based on a Local Scale Hidden Markov Model and the Viterbi Algorithm,” IEEE Transactions on Geoscience and Remote Sensing, 2016.
  31. R. Liu, “Compositing the Minimum NDVI for MODIS Data,” IEEE Transactions on Geoscience and Remote Sensing, 2016.
  32. L. Xu, J. Li, Y. Shu, and J. Peng, “SAR Image Denoising via Clustering- Based Principal Component Analysis,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 11, pp. 6858–6869, 2014.
  33. G. Yang, H. Shen, L. Zhang, Z. He, and X. Li, “A Moving Weighted Harmonic Analysis Method for Reconstructing High-Quality SPOT Vegetation NDVI Time-Series Data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 11, pp. 6008–6021, 2015.
  34. G. Moser, A. De Giorgi, and S. B. Serpico, “Multiresolution Supervised Classification of Panchromatic and Multispectral Images by Markov Random Fields and Graph Cuts,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 9, pp. 5054–5070, 2016.
  35. R. Restaino, M. Dalla Mura, G. Vivone, and J. Chanussot, “Context- Adaptive Pansharpening based on Image Segmentation,” IEEE Transactions on Geoscience and Remote Sensing, 2016.
  36. S. K. Mylonas, D. G. Stavrakoudis, J. B. Theocharis, and P. A. Mastorocostas, “Classification of Remotely Sensed Images using the Genesis Fuzzy Segmentation Algorithm,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 10, pp. 5352–5376, 2015.
  37. I. Walde, S. Hese, C. Berger, and C. Schmullius, “Graph-based Mapping of Urban Structure Types from High-Resolution Satellite Image Objects—Case Study of the German Cities Rostock and Erfurt,” IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 4, pp. 932–936, 2013.
  38. E. T. Gormus, N. Canagarajah, and A. Achim, “A Novel Decision Fusion Approach to Improving Classification Accuracy of Hyperspectral Images,” IEEE Transactions on Remote Sensing and Geoscience, vol. 23, no. 11, pp. 4158 4161, 2012.
  39. F. Palsson, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, “Model-Based Fusion of Multi-and Hyperspectral Images using PCA and Wavelets,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 5, pp. 2652–2663, 2015.
  40. M. Rizkinia, T. Baba, K. Shirai, and M. Okuda, “Local Spectral Component Decomposition for Multi-Channel Image Denoising,” IEEE Transactions on Image Processing, vol. 25, no. 7, pp. 3208–3218, 2016.
  41. R. Hecht, G. Meinel, and M. F. Buchroithner, “Estimation of Urban Green Volume based on Single-Pulse LiDAR Data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 11, pp. 3832–3840, 2008.
  42. F. Ling, Y. Zhang, G. M. Foody, X. Li, X. Zhang, S. Fang, W. Li, and Y. Du, “Learning-based Super resolution Land Cover Mapping,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 7, pp. 3794–3810, 2016.
  43. C. Xiao, M. Liu, D. Xiao, Z. Dong, and K.-L. Ma, “Fast Closed-Form Matting using a Hierarchical Data Structure,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 1, pp. 49–62, 2014.
  44. M. Espinola, J. A. Piedra-Fernandez, R. Ayala, L. Iribarne, and J. Z. Wang, “Contextual and Hierarchical Classification of Satellite Images based on Cellular Automata,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 2, pp. 795–809, 2015.
  45. T. Mei, L. An, and Q. Li, “Supervised Segmentation of Remote Sensing Image using Reference Descriptor,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 5, pp. 938–942, 2015.
  46. Z. Lei, T. Fang, and D. Li, “Land Cover Classification for Remote Sensing Imagery using Conditional Texton Forest with Historical Land Cover Map,” IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 4, pp. 720–724, 2011.
  47. V. H. Bhat, P. G. Rao, R. Abhilash, P. D. Shenoy, K. R Venugopal, and L.M Patnaik, “A Data Mining Approach for Data Generation and Analysis for Digital Forensic Application,” International Journal of Engineering and Technology, vol. 2, no. 3, p. 313, 2010.
  48. L. Gueguen, “Classifying Compound Structures in Satellite Images: A Compressed Representation for Fast Queries,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 4, pp. 1803–1818, 2015.
  49. P. Du, J. Xia, W. Zhang, K. Tan, Y. Liu, and S. Liu, “Multiple Classifier System for Remote Sensing Image Classification: A Review,” Sensors, vol. 12, no. 4, pp. 4764 4792, 2012.
  50. N. Gillis, D. Kuang, and H. Park, “Hierarchical Clustering of Hyperspectral Images using Rank-Two Nonnegative Matrix Factorization,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 4, pp. 2066–2078, 2015.
  51. S. Kraft, U. Del Bello, M. Bouvet, M. Drusch, and J.Moreno, “FLEX: ESA’s Earth Explorer 8 Candidate Mission,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 7, pp. 7125–7128, 2016.
  52. G. Moser, S. B. Serpico et al., “Generalized Minimum-Error Thresholding for Unsupervised Change Detection from SAR Amplitude Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 10, pp. 2972–2979, 2006.
  53. P. D. Shenoy, K. Srinivasa, K. R Venugopal, and L. M. Patnaik, “Dynamic Association Rule Mining using Genetic Algorithms,” Intelligent Data Analysis, vol. 9, no. 5, pp. 439–453, 2005.
  54. B. Solaiman, L. E. Pierce, and F. T. Ulaby, “Multisensor Data Fusion using Fuzzy Concepts: Application to Land-Cover Classification using ERS-1/JERS-1 SAR Composites,” IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 3, pp. 1316–1326, 2013.
  55. A. Baraldi and F. Parmiggiani, “A Refined Gamma MAP SAR Speckle Filter with Improved Geometrical Adaptivity,” IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 5, pp. 1245–1257, 2015.
  56. Y. Wang, A. I. Lyapustin, J. L. Privette, J. T. Morisette, and B. Holben, “Atmospheric Correction at AERONET Locations: A New Science and Validation Data Set,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 8, pp. 2450–2466, 2012.
  57. T. Daniels, W. L. Smith, and S. Kireev, “Simulation of Airborne Radiometric Detection of Wake Vortices,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 12, pp. 6336–6343, 2015.
  58. I. Sola, M. Gonzalez-Audicana, J. Alvarez-Mozos, and J. L. Torres, “Synthetic Images for Evaluating Topographic Correction Algorithms,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 3, pp. 1799–1810, 2014.
  59. L. Bruzzone and L. Carlin, “A Multilevel Context-based System for Classification of Very High Spatial Resolution Images,” IEEE transactions on Geoscience and Remote Sensing, vol. 44, no. 9, pp. 2587–2600, 2014
  60. K. Srinivasa, A. Singh, A. Thomas, K. R. Venugopal, and L. M Patnaik, “Generic Feature Extraction for Classification using Fuzzy C-means Clustering,” Intelligent Data Analysis, pp. 33–38, 2005.
  61. L. Bruzzone and L. Carlin, “A Multilevel Context-Based System for Classification of Very High Spatial Resolution Images,” IEEE transactions on Geoscience and Remote Sensing, vol. 44, no. 9, pp. 2587–2600, 2012.
  62. J. Feng, L. Jiao, X. Zhang, and D. Yang, “Bag-of-Visual-Words based on Clonal Selection Algorithm for SAR Image Classification,” IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 4, pp. 691–695, 2013.
  63. S. Bharathi, V. Shreyas, R. Anirudh, S. Sanketh, P. D. Shenoy, K.R Venugopal, and L.M Patnaik, “Performance Analysis of Segmentation Techniques for Land Cover Types using Remote Sensing Images,” 2012 Annual IEEE India Conference (INDICON), pp. 775–780, 2012.
  64. A. K. Shackelford and C. H. Davis, “A Hierarchical Fuzzy Classification Approach for High-Resolutionn Multispectral Data over Urban Areas,” IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 9, pp. 1920 1932, 2003.
  65. L. Zhang, X. Huang, B. Huang, and P. Li, “A Pixel Shape Index Coupled with Spectral Information for Classification of High Spatial Resolution Remotely Sensed Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 10, p. 2950, 2016.
  66. J. Yuan, D. Wang, and R. Li, “Remote Sensing Image Segmentation by Combining Spectral and Texture Features,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 1, pp. 16–24, 2014.
  67. L. Vibha, C. Hegde, P. D. Shenoy, K. R. Venugopal, and L.M Patnaik, “Dynamic Object Detection, Tracking and Counting in Video Streams for Multimedia Mining,” IAENG International Journal of Computer Science, vol. 35, no. 3, pp. 16–21, 2008.
  68. A. Ramachandra, S. Abhilash, R. KB, and K. R. Venugopal, “Feature Level Fusion based Bimodal Biometric using Transformation Domine Techniques,” IOSR Journal of Computer Engineering (IOSRJCE), vol. 3, no. 3, pp. 39–46, 2012.
  69. Q. Yu, P. Gong, N. Clinton, G. Biging, M. Kelly, and D. Schirokauer, “Object-Based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery,” Photogrammetric Engineering Remote Sensing, vol. 72, no. 7, pp. 799–811, 2014.
  70. A. Angel, M. Hickman, P. Mirchandani, and D. Chandnani, “Methods of Analyzing Traffic Imagery Collected from Aerial Platforms,” IEEE Transactions on Intelligent Transportation Systems, vol. 4, no. 2, pp. 99–107, 2003.
  71. J. Goldberger, S. Gordon, and H. Greenspan, “Unsupervised Image-Set Clustering using an Information Theoretic Framework,” IEEE Transactions on Image Processing, vol. 15, no. 2, pp. 449–458, 2012.
  72. H. S. Kumar, K. B Raja, K. R Venugopal, and L. M Patnaik, “Automatic Image Segmentation using Wavelets,” International Journal of Computer Science and Network Security, vol. 9, no. 2, pp. 305–313, 2009.
  73. M. Celenk, “A Color Clustering Technique for Image Segmentation,” Computer Vision, Graphics, and Image Processing, vol. 52, no. 2, pp. 145–170, 2014.
  74. I. Karoui, R. Fablet, J.-M. Boucher, and J.-M. Augustin, “Variational Region-based Segmentation using Multiple Texture Statistics,” IEEE Transactions on Image Processing, vol. 19, no. 12, pp. 3146–3156, 2013.
  75. R. R. Muskett, C. S. Lingle, J. M. Sauber, A. S. Post, W. V. Tangborn, B. T. Rabus, and K. A. Echelmeyer, “Airborne and Spaceborne DEM and Laser Altimetry-Derived Surface Elevation and Volume Changes of the Bering Glacier System, Alaska, USA, and Yukon, Canada, 1972– 2006”, Journal of Glaciology, vol. 55, no. 190, pp. 316–326, 2013.
  76. E. Berthier, E. Schiefer, G. K. Clarke, B. Menounos, and F. Remy, “Contribution of Alaskan Glaciers to Sea-Level Rise Derived from Satellite Imagery,” Nature Geoscience, vol. 3, no. 2, pp. 92–95, 2014.
  77. K. R Venugopal, K. Srinivasa, and L. M. Patnaik, Soft Computing for Data Mining Applications. Springer, 2009.
  78. T. Toutin, “Digital Elevation Model Generation Over Glacierized Region,” Encyclopedia of Snow Ice and Glaciers, pp. 202–213, 2011.
  79. P. D. Shenoy. K. R. Venugopal, Vibha, Harshavardhan L and L. M. Patnaik, “Lesion Detection using Segmentation and Classification of Mammograms,” Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications, pp. 311–316, 2007.
  80. C. S. Fraser and H. B. Hanley, “Bias-Compensated RPCs for Sensor Orientation of High-Resolution Satellite Imagery,” Photogrammetric Engineering and Remote Sensing, vol. 71, no. 8, pp. 909–915, 2015.
  81. S. Jia, G. Tang, J. Zhu, and Q. Li, “A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 88–102, 2016.
  82. G. Baudat and F. Anouar, “Generalized Discriminant Analysis using a Kernel Approach,” Neural computation, vol. 12, no. 10, pp. 2385–2404, 2015.
  83. Y. Wang, X. X. Zhu, B. Zeisl, and M. Pollefeys, “Fusing Meter- Resolution 4-D InSAR Point Clouds and Optical Images for Semantic Urban Infrastructure Monitoring,” IEEE Transactions on Geoscience and Remote Sensing, 2016.
  84. R. A. Schowengerdt, Remote Sensing: Models and Methods for Image Processing. Academic press, 2006.
  85. G. Camps-Valls, D. Tuia, L. G´omez-Chova, S. Jim´enez, and J. Malo, “Remote Sensing Image Processing,” Synthesis Lectures on Image, Video, and Multimedia Processing, vol. 5, no. 1, pp. 1–192, 2011.
  86. J. R. Schott, “Remote Sensing”. Oxford University Press, 2007.
  87. R. A. Schowengerdt, Techniques for Image Processing and Classifications in Remote Sensing. Academic Press, 2012.
  88. G. Cmara, R. C. M. Souza, U. M. Freitas, and J. Garrido, “SPRING: Integrating Remote Sensing and GIS by Object-Oriented Data Modelling,” Computers graphics, vol. 20, no. 3, pp. 395–403, 2006.
  89. E. Pottier, L. Ferro-Famil, S. Allain, S. Cloude, I. Hajnsek, K. Papathanassiou, A. Moreira, M. Williams, A. Minchella, M. Lavalle et al., “Overview of the PolSARpro V4. 0 Software. The Open Source Toolbox for Polarimetric and Interferometric Polarimetric SAR Data Processing,” 2009 IEEE International Geoscience and Remote Sensing Symposium, vol. 4, pp. IV–936, 2011.
  90. K. Johnston, J. M. Ver Hoef, K. Krivoruchko, and N. Lucas, Using ArcGIS Geostatistical Analyst. Esri Redlands, 2001, vol. 3.
  91. M. Gooch, J. Chandler, and M. Stojic, “Accuracy Assessment of Digital Elevation Models Generated using the Erdas Imagine OrthoMAX Digital Photogrammetric System,” The Photogrammetric Record, vol. 16, no. 93, pp. 519–531, 2010.
  92. M. V. Martin, W. D. Goran, R. C. Lozar, J. M. Messersmith, and M. S. Ruiz, “GRASS/GIS (Geographic Resources Analysis Support System/Geographic Information System) Implementation Guide,” DTIC Document, Tech. Rep., 1989.
  93. K. R Venugopal and R. Buyya, Mastering C++. Tata McGraw-Hill Education, 2013.
  94. S. Steiniger and A. J. Hunter, “Free and Open Source GIS Software for Building a Spatial Data Infrastructure,” Geospatial free and open source software in the 21st century, pp. 247–261, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Classification Image Enhancement Remote Sensing Resolution Satellite Sensors