Techniques iIn Image Classification; A Survey

1
Mr. S.V.S.Prasad
Mr. S.V.S.Prasad
2
Dr. T. Satya Savithri
Dr. T. Satya Savithri
3
Dr. Iyyanki V. Murali Krishna
Dr. Iyyanki V. Murali Krishna
1 MLRIT

Send Message

To: Author

GJRE Volume 15 Issue F6

Article Fingerprint

ReserarchID

2QFJ5

Techniques iIn Image Classification; A Survey Banner
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

This paper reviews on the current trends, problems and prospects of image classification including the factors affecting it. By the end of the session we will be summarizing the popular advanced classification approaches and methods that are used to improve classification accuracy. The main motive of this review is to suggest a suitable image processing procedure in order to have a successful classification of remotely sensed data into a thematic map.

100 Cites in Articles

References

  1. S Stefan,K Itten (1997). A physicallybased model to correct atmospheric and illumination effects in optical satellite data of rugged terrain.
  2. E Vermote,D Tanre,J Deuze,M Herman,J Morcrette (1997). Second simulation of the satellite signal in the solar spectrum, 6S: an overview.
  3. Timo Tokola,Satu Löfman,Antti Erkkilä (1999). Relative Calibration of Multitemporal Landsat Data for Forest Cover Change Detection.
  4. J Heo,T Fitzhugh (2000). A standardized radiometric normalization method for change detection using remotely sensed imagery.
  5. C Song,C Woodcock,K Seto,M Lenney,S Macomber (2001). Classification and change detection using Landsat TM data: when and how to correct atmospheric effect.
  6. Y Du,P Teillet,J Cihlar (2002). Radiometric normalization of multitemporal highresolution satellite images with quality control for land cover change detection.
  7. E Mcgovern,N Holden,S Ward,J Collins (2002). The radiometric normalization of multitemporal Thematic Mapper imagery of the midlands of Ireland-a case study.
  8. Morton Canty,Allan Nielsen,Michael Schmidt (2004). Automatic radiometric normalization of multitemporal satellite imagery.
  9. D Hadjimitsis,C Clayton,V Hope (2004). An assessment of the effectiveness of atmospheric correction algorithms through the remote sensing of some reservoirs.
  10. P Teillet,B Guindon,D Goodenough (1982). On the Slope-Aspect Correction of Multispectral Scanner Data.
  11. D Civco (1989). Topographicssa normalization of Landsat Thematic Mapper digital imagery.
  12. I Gitas,G Mitri,G Ventura (2004). Object-based image classification for burned area mapping of Creus Cape Spain, using NOAA-AVHRR imagery.
  13. V Walter,Carlotto (1997). Object-based classification of remote sensing data for change detection.
  14. L Biehl,D Landgrebe (2002). MultiSpec-a tool for multispectral-hyperspectral image data analysis.
  15. D Landgrebe,D Lu,Weng (2003). Spectral mixture analysis of the urban landscapes in Indianapolis with Landsat ETM+ imagery.
  16. C Kontoes,D Rokos (1996). The integration of spatial context information in an experimental knowledge-based system and the supervised relaxation algorithm—two successful approaches to improving SPOT-XS classification.
  17. P Gong,P Howarth (1992). Frequencybased contextual classification and gray-levelvector reduction for land-use identification.
  18. Bing Xu,Peng Gong,Edmund Seto,Robert Spear (1994). Comparison of Gray-Level Reduction and Different Texture Spectrum Encoding Methods for Land-Use Classification Using a Panchromatic Ikonos Image.
  19. K Sharma,A Sarkar (1998). A modified contextual classification technique for remote sensing data.
  20. E Binaghi,P Madella,M Grazia Montesano,A Rampini (1997). Fuzzy contextual classification of multisource remote sensing images.
  21. S Magnussen,P Boudewyn,M Wulder (2004). Contextual classification of LandsatTM images to forest inventory cover types.
  22. D Michelson,B Liljeberg,P Pilesjo (2000). Comparison of algorithms for classifying Swedish land cover using Landsat TM and ERS-1 SAR data.
  23. F Cortijo,N De La Blanca (1998). Improving classical contextual classifications.
  24. Hubert-Moy,L Cotonnec,A Le Du,L Chardin,A Perez,P (2001). A comparison of parametric classification procedures of remotely sensed data applied on different landscape units.
  25. J Carr (1999). Classification of digital image texture using variograms.
  26. B Kartikeyan,A Sarkar,K Majumder (1998). A segmentation approach to classification of remote sensing imagery.
  27. Derek Peddle,Giles Foody,Airong Zhang,Steven Franklin,Ellsworth Ledrew (1994). Multi-Source Image Classification II: An Empirical Comparison of Evidential Reasoning and Neural Network Approaches.
  28. Yeqiao Wang,Daniel Civco (1994). Evidential Reasoning-Based Classification of Multi-Source Spatial Data for Improved Land Cover Mapping.
  29. D Peddle (1995). Knowledge formulation for supervised evidential classification. tandem coherence and JERS backscatter data.
  30. J Benediktsson,I Kanellopoulos (1999). Classification of multisource and hyperspectral data based on decision fusion.
  31. Christina Warrender,Marijke Augusteijn (1999). Fusion of image classifications using Bayesian techniques with Markov random fields.
  32. B Steele (2000). A quantitative assessment of a combined spectral and GIS rulebased land-cover classification in the Neuse river basin of North Carolina.
  33. Dengsheng Lu,Qihao Weng (2004). Spectral Mixture Analysis of the Urban Landscape in Indianapolis with Landsat ETM+ Imagery.
  34. Zhi Huang,Brian Lees (2004). Combining Non-Parametric Models for Multisource Predictive Forest Mapping.
  35. C Lo,Jinmu Choi (2004). A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images.
  36. A Nyoungui,E Tonye,A Akono (2002). Evaluation of speckle filtering and texture analysis methods for land cover classification from SAR images.
  37. A Baraldi,F Parmiggiani (1995). An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters.
  38. T Kurosu,S Yokoyama,K Chiba (2001). Land use classification with textural analysis and the aggregation technique using multi-temporal 124.
  39. Derek Peddle,David Ferguson (2002). Optimisation of multisource data analysis: an example using evidential reasoning for GIS data classification.
  40. J Lein (2003). Applying evidential reasoning methods to agricultural land cover classification.
  41. M Hung,M Ridd (2002). A subpixel classifier for urban land-cover mapping based on a maximum-likelihood approach and expert system rules.
  42. K Schmidt,A Skidmore,E Kloosterman,H Van Oosten,L Kumar,J Janssen (2004). Mapping Coastal Vegetation Using an Expert System and Hyperspectral Imagery.
  43. H Onsi (2003). Designing a rule-based classifier using syntactical approach.
  44. Arko Lucieer,Menno-Jan Kraak (2004). Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty.
  45. B Jeon,D Landgrebe (1999). Decision fusion approaches for multitemporal classification.
  46. R Barandela,M Juarez (2002). Supervised classification of remotely sensed data with ongoing learning capability.
  47. O Debeir,Van Den,I Steen,P Latinne,P Van Ham,E Wolff (2002). Textural and contextual land-cover classification using single and multiple classifier systems.
  48. Xue-Hua Liu,A Skidmore,H Van Oosten (2002). Integration of classification methods for improvement of land-cover map accuracy.
  49. F Qiu,J Jensen (2004). Opening the black box of neural networks for remote sensing image classification.
  50. Claudio Conese,Fabio Maselli (1994). Evaluation of contextual, per‐pixel and mixed classification procedures applied to a sub‐tropical landscape.
  51. K Tansey,A Luckman,L Skinner,H Balzter,T Strozzi,W Wagner (2004). Classification of forest volume resources using ERS tandem coherence and JERS backscatter data.
  52. P Rao,M Sai,K Sreenivas,M Rao,B Rao,R Dwivedi,L Venkataratnam (2002). Textural analysis of IRS-1D panchromatic data for land cover classification.
  53. E Podest,S Saatchi (2002). Application of multiscale texture in classifying JERS-1 radar data over tropical vegetation.
  54. O Butusov (2003). Textural classification of forest types from Land sat 7 imagery.
  55. M Augusteijn,L Clemens,K Shaw (1995). Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier.
  56. João Soares,Camilo Rennó,Antonio Formaggio,Corina Da Costa Freitas Yanasse,Alejandro Frery (1997). An investigation of the selection of texture features for crop discrimination using SAR imagery.
  57. M Shaban,O Dikshit (2001). Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city, Uttar Pradesh.
  58. K Chen,S Yen,D Tsay (1997). Neural classification of SPOT imagery through integration of intensity and fractal information.
  59. H Low,H Chuah,H Ewe (1999). A neural network land use classifier for SAR images using textural and fractal information.
  60. G Hay,K Niemann,G Mclean (1996). An object-specific image-texture analysis of H-resolution forest imagery.
  61. J Carr,F Miranda (1998). The semivariogram in comparison to the co-occurrence matrix for classification of image texture.
  62. C Lloyd,S Berberoglu,P Curran,P Atkinson (2004). A comparison of texture measures for the per-field classification of Mediterranean land cover.
  63. C Zhang,S Franklin,M Wulder (2004). Geostatistical and texture analysis of airborneacquired images used in forest classification.
  64. M Crawford,S Kumar,M Ricard,J Gibeaut,A Neuenschwander (1999). Fusion of airborne polarimetric and interferometric SAR for classification of coastal environments.
  65. M Shaban,O Dikshit (2002). Evaluation of the merging of SPOT multispectral and panchromatic data for classification of an urban environment.
  66. Wenzhong Shi,Changqing Zhu,Caiying Zhu,Xiaomei Yang (2003). Multi-Band Wavelet for Fusing SPOT Panchromatic and Multispectral Images.
  67. D Geneletti,B Gorte (2003). A method for object-oriented land cover classification combining Landsat TM data and aerial photographs.
  68. Yifang Ban (2003). Synergy of multitemporal ERS-1 SAR and Landsat TM data for classification of agricultural crops.
  69. Barry Haack,E Solomon,M Bechdol,N Herold (2002). SectorInsights.edu—Integration of Varied Spatial Resolution Data.
  70. S Teggi,R Cecchi,F Serafini (2003). TM and IRS-1C-PAN data fusion using multiresolution decomposition methods based on the 'a tròus' algorithm.
  71. D Yocky (1996). Multiresolution wavelet decomposition image merger of Landsat Thematic Mapper and SPOT panchromatic data.
  72. C Pohl,J Van Genderen (1998). Multisensor image fusion in remote sensing: concepts, methods, and applications.
  73. D Chen,D Stow (2003). Strategies for integrating information from multiple spatial resolutions into land-use/land-cover classification routines.
  74. S Ray (2004). Merging of IRS LISS III and PAN data-evaluation of various methods for a 194.
  75. George Hurtt,Xiangming Xiao,Michael Keller,Michael Palace,Gregory Asner,Rob Braswell,Eduardo Brondı́zio,Manoel Cardoso,Claudio Carvalho,Matthew Fearon,Liane Guild,Steve Hagen,Scott Hetrick,Berrien Moore,Carlos Nobre,Jane Read,Tatiana Sá,Annette Schloss,George Vourlitis,Albertus Wickel (2003). IKONOS imagery for the Large Scale Biosphere–Atmosphere Experiment in Amazonia (LBA).
  76. Sande Van Der,C De,Jong,S Roo,A (2003). A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment.
  77. Qiaofeng Zhang,Jinfei Wang (2003). A rule-based urban land use inferring method for fine-resolution multispectral imagery.
  78. Le Wang,Wayne Sousa,Peng Gong,Gregory Biging (2004). Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama.
  79. M Hodgson,J Jensen,J Tullis,K Riordan,C Archer (2003). Synergistic use lidar and color aerial photography for mapping urban parcel imperviousness.
  80. M Erikson,Benediktsson (1995). Classification of hyper dimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks.
  81. R Platt,A Goetz (2004). A comparison of AVIRIS and Land sat for land use classification at the urban fringe.
  82. A Apan,A Held,S Phinn,J Markley (2000). Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery.
  83. Serwan Baban,Kamaruzaman Yusof (2001). Mapping land use/cover distribution on a mountainous tropical island using remote sensing and GIS.
  84. Q Zhang,J Wang,X Peng,P Gong,P Shi (2002). Urban built-up land change detection with road density and spectral information from multitemporal LandsatTM data.
  85. J Epstein,K Payne,E Kramer (2002). Techniques for mapping suburban sprawl.
  86. P Harris,S Ventura (1995). The integration of geographic data with remotelysensed 202.
  87. G Okin,D Roberts,B Murray,W Okin (2001). Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments.
  88. Raymond Kokaly,Don Despain,Roger Clark,K Livo (2003). Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data.
  89. Victor Mesev (1998). Classification of Urban Areas: Inferring Land Use from the Interpretation of Land Cover.
  90. L Bronge (1999). Mapping Boreal Vegetation Using Landsat-TM and Topographic Map Data in a Stratified Approach.
  91. E Helmer,S Brown,W Cohen (2000). Mapping montane tropical forest successional stage and land use with multi-date Landsat imagery.
  92. S Narumalani,Y Zhou,D Jelinski (1998). Utilizing geometric attributes of spatial linformation to improve digital image classification.
  93. M Barnsley,S Barr (1996). Inferring urban land use from satellite sensor imagesusing kernel-based spatial reclassification.
  94. G Groom,R Fuller,A Jones (1996). Contextual correction: techniques for improving land cover mapping from remotely sensed images.
  95. Y Zhang (1999). Optimisation of building detection in satellite images by combining multispectral classification and texture filtering.
  96. Casson Stallings,Siamak Khorram,Rodney Huffman (1999). Incorporating Ancillary Data into a Logical Filter for Classified Satellite Imagery.
  97. H Murai,S Omatu (1997). Remote sensing image analysis using a neural network and knowledge-based processing.
  98. A Solberg,T Taxt,A Jain (1996). A Markov random field model for classification of multisource satellite imagery.
  99. L Bruzzone,C Conese,F Maselli,F Roli (1997). Multisource classification of complex rural areas by statistical and neural-network approaches.
  100. D Amarsaikhan,T Douglas* (2004). Data fusion and multisource image classification.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Mr. S.V.S.Prasad. 2015. \u201cTechniques iIn Image Classification; A Survey\u201d. Global Journal of Research in Engineering - F: Electrical & Electronic GJRE-F Volume 15 (GJRE Volume 15 Issue F6): .

Download Citation

Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Keywords
Classification
GJRE-F Classification: FOR Code: 280203
Version of record

v1.2

Issue date

August 20, 2015

Language

English

Experiance in AR

The methods for personal identification and authentication are no exception.

Read in 3D

The methods for personal identification and authentication are no exception.

Article Matrices
Total Views: 4160
Total Downloads: 2100
2026 Trends
Research Identity (RIN)
Related Research

Published Article

This paper reviews on the current trends, problems and prospects of image classification including the factors affecting it. By the end of the session we will be summarizing the popular advanced classification approaches and methods that are used to improve classification accuracy. The main motive of this review is to suggest a suitable image processing procedure in order to have a successful classification of remotely sensed data into a thematic map.

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]
×

This Page is Under Development

We are currently updating this article page for a better experience.

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Techniques iIn Image Classification; A Survey

Mr. S.V.S.Prasad
Mr. S.V.S.Prasad MLRIT
Dr. T. Satya Savithri
Dr. T. Satya Savithri
Dr. Iyyanki V. Murali Krishna
Dr. Iyyanki V. Murali Krishna

Research Journals