Techniques in Image Classification; A Survey

Table of contents

1. Introduction

he image classification plays an important role in environmental and socioeconomic applications. In order to improve the classification accuracy, scientists have laid path in developing the advanced classification techniques. [1-9] However, classifying a remotely sensed data into a thematic map is still a nightmare because of the following factors such as landscape complexity, image sensing and processing and classification approaches. The review concentrates on recent classification approaches and techniques which are often not available.

2. a) Remote sensing classification process

RS classification is generally a complex procedure which needs many factors to be considered. This procedure includes following steps that begins with the identification of suitable classification system, choosing appropriate training samples, processing of an image and extracting its features, applying a right and indeed classification method, post classification and accuracy assessments.

Airborne and space borne sensor data comes under RS data stream, which varies in spatial, radiometric, spectral and temporal resolutions. In order to have better image classification a suitable RS data needs to be collected, which depends upon strength and weakness of sensor data. In literature the characteristics of remotely sensed data is summarized by [10], [11] in spectral, radio metric, spatial and temporal resolutions with polarization and angularity.

It is preferred to consider the factors while selecting suitable sensor data as per the user's need, which includes scaling, study area characteristics, availability of various image data and their characteristics, cost, time constraints and analyst's experience in using selected images. Scaling determines the study area; earlier research encountered a problem of image resolution of remotely sensed data in classification. In regular practice, a fine-scale classification system is adopted in order to achieve high spatial resolution data. For example, IKONS and SPOT 5 HRG are at regional level medium spatial resolution data. However, the influence of atmospheric conditions in moist and tropical regions cannot be neglected and they are often an obstacle for capturing the high quality sensor data. Therefore, it always proves to be beneficiary to have multiple sources of sensor data.

3. c) Selection of classification system and training samples

A better classification can be achieved only when we consider a suitable classification system with sufficient number of training samples. Generally, in a wide variety of applications we adopt hierarchy classification systems because different conditions are taken into account. A classification system should consider spatial resolution of selected RS data, compatibility with its previous work, image processing and classification algorithm availability and time constraints. The ultimate goal of choosing any classification system is to satisfy the need of an end user.

The image classification broadly depends on number of training samples and their representativeness. Training samples can be prepared by fieldwork or it can also be obtained from other means such as aerial photographs of fine spatial resolution and satellite images. The results of the classification are affected by the selection of training data, which generally may be based on single pixel, seed or polygon, also affected by fine spatial resolution image data if proper care is not taken. If coarse resolution data is used for classification data then the selection of TS becomes tedious under complex and heterogeneous case studies as it contains large volumes of mixed pixels.

4. d) Data Preprocessing

The image preprocessing is a technique which includes detection, restoration of bad lines, geometric rectification, radio metric calibration, atmospheric and topographic correction. If data is collected from different sources, it is necessary to check the quality before stepping into classification. If the single data image is utilized in classification atmospheric corrections may not be required but on the other hand it becomes mandatory for a multi-sensor data. A variety of correction techniques are presented [12][13][14][15][16][17][18][19][20][21][22][23].If the study area includes rugged or mountainous regions a topographic correction is needed, which is detailed [24][25][26][27][28][29][30].

5. e) Feature Extraction and Selection

The quality of an image classification depends on the selection of suitable variables. A variety of variables used in classification includes spectrum signature, vegetation indices, transformed images, textual information, multi temporal images, multi sensor images and ancillary data. The process of feature extraction is needed in order to minimize data redundancy in remotely sensed data or to excavate specific land cover information, that includes principle component analysis, minimum noise fraction transform discriminant analysis, decision boundary, feature extraction, non parametric weighted feature extraction, wavelet transform and spectral mixture analysis.

6. f) Selection of suitable classification method

The question of choosing a classification method is ambiguous because many factors such as spatial resolution of RD, multi-sensor data, availability of different classification software are involved. Each classification method has its own merits and demerits.

7. g) Post classification processing

Classification confusions arise in the regions such as urban areas, for example, consider between commercial and high intensity residential areas or between recreational grass and crops. In present example to reduce classification confusions we need to consider the property of spectral signature because it is similar to commercial and high intensity residential areas but on the other hand their population densities are different. Pasture and crops are largely located away from residential areas with sparse houses and low population densities, at this stage expert knowledge can be developed based on the relationship between housing or population densities and urban land use classes to help separate recreational grass from pasture and crops.

8. II.

9. Evaluation of Classification Performance

Evaluating the classified results is an important step in classification procedure. The evaluation process may include qualitative evaluation based on expert knowledge to quantitative accuracy based on sampling strategies. The classification accuracy assessment is the most common approach for the evaluation of classification performance [31][32].

10. a) Classification of accuracy assessment

By the knowledge of sources of errors, classification accuracy assessment can be implemented in addition to classification error, position error, which resulting from registration, interpolating error and poor quality of training which may affect the classification accuracy. The classification accuracy assessment includes three basic steps 1.Sampling design, 2.Response design,3. Estimation and Analysis procedures

11. b) Advanced classification procedures

The advanced classification procedures such as neural networks, fuzzy sets and expert systems are highly applied for image classification. In general image classification approaches it can be grouped as supervised or unsupervised, parametric and nonparametric or hard and soft classifiers or per pixel, sub pixel, per field. Table provides brief description of these categories.

12. c) Use of multiple features of remote sensed data

Any remote sensed data generally contains many unique and special spectral radio metric temporal and polarization characteristics; the effective use of these features can improve the classification accuracy. The summary of table 3 presents the research efforts in order to improve the classification accuracy by considering the features of remote sensed data.

13. III.

14. Discussions a) Uncertainties in image classification

Uncertainties in image classification occur at different stages, influence classification accuracy. Improving and understanding the stages those contribute to uncertainty results in quality image classification.

15. b) Impact of spatial resolution

Spatial resolution is an important factor that affects classification details and accuracy, which influences the selection of a classification approach. Various reduction techniques have been developed and presented by different authors in their literature.

16. c) Selection of suitable variables

In practice, making a complete use of multiple features of different sensor data, implementing feature extraction and selecting variables as input for a classification procedure becomes important.

IV.

17. Conclusion

This study helps upcoming scientists and researchers for opting a suitable classification procedure in their specific study. In our presentation we have concentrated extensively on the work done from the past decade that includes 1. Development and advanced classification algorithms such as sub pixel, per field and acknowledged based classification algorithms; 2. We have considered various remote sensing features including spectral, spatial, multi temporal and multi sensor information; 3. Incorporating an ancillary data into classification procedures that includes topography, soil, road and census data. [124], [125] [107], [126], [97], [127], [128] Visual fuzzy classification based on use of exploratory and interactive visualization techniques [129] Multi temporal classification based on decision fusion [130] Supervised classification with ongoing learning capability based on nearest neighbor rule [131] Combinative approaches of multiple classifiers Multiple classifier system (BAGFS: combines bootstrap aggregating with multiple feature subsets) [132] A consensus builder to adjust classification output (MLC, expert system, and neural network) [133] Integrated expert system and neural network classifier [133] Improved neuro-fuzzy image classification system, Spectral and contextual classifiers, Mixed contextual and per-pixel classification, Combination of iterated contextual probability classifier and MLC [134],[116], [135], [136] Combination of neural network and statistical consensus theoretic classifiers [137] Combination of MLC and neural network using Bayesian techniques [138] Combining

18. Use of textures

First-, second-, and third-order statistics in the spatial domain; texture features from the texture spectrum and from grey level different vector [144] Grey-level co-occurrence matrices(GLCM) [145], [146], [147], [148], [149] Co-occurrence matrices, grey-level difference, texture-tone analysis ,features derived from Fourier spectrum, and Gabor filters [150] GLCM, grey level difference histogram, sum and different histogram [151], [152] Fractal information [153], [154] Triangulated primitive neighborhood method, Semi variogram, Geo statistical analysis, Gabor filtering [155], [156], [157], [158] [194], [195], [109], [196], [197], [97], [198], [199] Hyper -spectral data AVIRIS [200], [201], [202], [203], [204], [205] HyMap hyper spectral digital data, DAIS hyper spectral data, EO-1 Hyperion, Data obtained from Field Spec Pro FR spectro radiometer [127] [206] [207], [208] Based on topography, Based on census data, Based on illumination and ecological zone, Based on shape index of the Patches [215], [210], [216], [217] Post classification processing Kernel-based spatial reclassification [218] Using zoning and housing density data to modify the initial classification result, Using contextual correction, [213], [219] Using filtering based on co occurrence Matrix, Using polygon and rectangular mode filters, Using expert system to perform post classification sorting, Using knowledge-based system to correct misclassification [220], [221], [222], [223] Use of multisource data Spectral, texture, and ancillary data (such as DEM, soil, existing GIS-based maps)

[123], [224], [225], [137],[226],

[227],[7], [228] References Références Referencias

Figure 1. Table 1 :
1
Criteria Categories Characteristics Example of
classifiers
Whether Supervised Land cover classes are defined. Sufficient Maximum likelihood,
training classification reference data is available and used as minimum distance,
samples are approaches training samples. The signatures artificial neural network,
used or not generated from the training samples are decision tree
then used to train the classifier to classify classifier.
the spectral data into a thematic map
Unsupervised Clustering-based algorithms are used to ISODATA, K-means
classification partition the spectral image into a number clustering algorithm
approaches of spectral classes based on the
statistical information inherent in the
image. No prior definitions of the classes
are used. The analyst is responsible for
labeling and merging the spectral classes
into meaningful classes.
Whether Parametric Gaussian distribution is assumed. The Maximum likelihood,
parameters classifiers parameters (e.g. mean vector and linear discriminant
such as covariance matrix) are often generated from analysis.
mean vector training samples. When landscape is
and complex, parametric classifiers often
covariance produce 'noisy' results. Another major
matrix are drawback is that it is difficult to integrate
used or not ancillary data, spatial and contextual
attributes, and non-statistical information into
a classification procedure.
Non-Parametric No assumption about the data is required. Artificial neural network,
classifiers Non-parametric classifiers do not employ decision tree classifier,
statistical parameters to calculate class evidential reasoning,
separation and are especially suitable for support vector machine,
incorporation of non-remote-sensing data expert system.
into a classification procedure.
Which kind of Per-pixel Traditional classifiers typically develop a Most of the classifiers,
pixel classifiers signature by combining the spectra of all such as maximum
information is training-set pixels from a given feature. The likelihood, minimum
used resulting signature contains the distance, artificial neural
contributions of all materials present in the network, decision tree,
training-set pixels, ignoring the mixed pixel and support vector
problems machine.
Sub pixel The spectral value of each pixel is assumed Fuzzy-set classifiers,
classifiers to be a linear or non-linear combination of sub pixel classifier,
defined pure materials (or end members), spectral mixture
providing proportional membership of each analysis.
pixel to each end member.
Which kind of Object-oriented Image segmentation merges pixels into E Cognition
pixel classifiers objects and classification is conducted
information is based on the objects, instead of an
used individual pixel. No GIS vector data are used.
Per-field GIS plays an important role in per-field GIS-based
classifiers classification, integrating raster and vector classification
data in a classification. The vector data are approaches
often used to subdivide an image into
parcels, and classification is based on the
parcels, avoiding the spectral variation
inherent in the same class.
Whether Hard Making a definitive decision about the land Most of the classifiers,
Figure 2. Table 2 :
2
Category Advanced classifiers References
Per-pixel Neural network [33], [34], [35],[36], [37], [38], [39], [40],
algorithms [41], [42], [43]
Decision tree classifier, Spectral angle classifier, [44], [45], [46],[47],
Supervised iterative classification (multistage [32],[8],[48],[49],[50], [51],[4]
classification)
Enhancement-classification approach,MFM-5-Scale [52],[53]
(Multiple-Forward-Mode approach to running the 5-Scale
geometric-optical reflectance model)
Iterative partially supervised classification based on a [54]
combined use of a Radial Basis Function network and a
Markov Random Field approach
Classification by progressive generalization Support [31],[55], [56], [57], [58],[59],[60], [61],
vector machine [62], [63]
Unsupervised classification based on independent [64],[65], [66]
component
analysis mixture model, Optimal iterative unsupervised
Model-based unsupervised classification, Linear [67], [68] ,[69], [70]
constrained discriminant analysis
Multispectral classification based on probability density [71],[72],[73][74],[75],[76], [77]
functions,
Layered classification, Nearest-neighbor classification,
Selected pixel classification
Sub pixel
algorithms
Note: Imagine sub pixel classifier, Fuzzy classifier, Fuzzy expert system [78], [3],[79],[80],[81], [82] Fuzzy neural network, Fuzzy-based multi sensor data fusion classifier, Rule-based machine-version approach [3], [83],[84], [80], [85], [86], [87] Linear regression or linear least squares inversion [88],[89] [120],[121],[122], [123],[7],
Figure 3. Table 3 :
3
Method Features References
Figure 4. Table 4 :
4
Features References
Method
Use of ancillary DEM Topography, land use, and soil Maps [209] [210]
data Road density, Road coverage, Census data [211] [212] [213], [214] [173]
Stratification
Figure 5.
Techniques in Image Classification; A Survey Techniques in Image Classification; A Survey Techniques in Image Classification; A Survey Techniques in Image Classification; A Survey Techniques in Image Classification; A Survey Techniques in Image Classification; A Survey Techniques in Image Classification; A Survey
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© 2015 Global Journals Inc. (US) © 20 15 Global Journals Inc. (US)
Note: 1. GONG, P. and HOWARTH, P.J., 1992, Frequencybased contextual classification and gray-level 2. KONTOES, C., WILKINSON, G.G., BURRILL, A., GOFFREDO, S. and MEGIER, J., 1993, An experimental system for the integration of GIS data in knowledge-based image analysis for remote © 2015 Global Journals Inc. (US) © 20 15 Global Journals Inc. (US) 227. TSO, B.C.K. and MATHER, P.M., 1999, Classification of multisource remote sensing imagery using a genetic algorithm and Markov
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Appendix A

Appendix A.1

Appendix B

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  8. Decision fusion approaches for multitemporal classification. B Jeon , D A Landgrebe . IEEE Transactions on Geoscience and Remote Sensing 1999. 37 p. .
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  12. Fusion of image classification using Bayesian techniques with Markov random fields. C E Warrender , M F Augusteihn . International Journal of Remote Sensing 1999. 20 p. .
  13. A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. C J Van Der Sande , S M De Jong , A P J Roo . International Journal of Applied Earth Observation and Geo information 2003. 4 p. .
  14. A hybrid approach to urban land use/cover mapping using Landsat 7, C P Lo , J Choi . 2004.
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  16. Geostatistical and texture analysis of airborneacquired images used in forest classification, C Zhang , S E Franklin , M A Wulder . 2004.
  17. Data fusion and multisource image classification. D Amarsaikhan , T Douglas . International Journal of Remote Sensing 2004. 25 p. .
  18. Spectral mixture analysis of the urban landscapes in Indianapolis with Landsat ETM+ imagery. D Lu , Q Weng . Photogrammetric Engineering and Remote Sensing 2004. 70 p. .
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  20. Mapping montane tropical forest successional stage and land use with multi-date Landsat imagery. E H Helmer , S Brown , W B Cohen . International Journal of Remote Sensing 2000. 21 p. .
  21. Enhanced Thematic Mapper Plus (ETM + ) images. International Journal of Remote Sensing 25 p. .
  22. Application of multiscale texture in classifying JERS-1 radar data over tropical vegetation. E Podest , S Saatchi . International Journal of Remote Sensing 2002. 23 p. .
  23. Classification of Mediterranean vegetation by TM and ancillary data for the evaluation of fire risk. F Maselli , A Rodolfi , L Bottai , S Romanelli , C Conese . International Journal of Remote Sensing 2000. 21 p. .
  24. Opening the black box of neural networks for remote sensing image classification. F Qiu , J R Jensen . International Journal of Remote Sensing 2004. 25 p. .
  25. Contextual correction: techniques for improving land cover mapping from remotely sensed images. G B Groom , R M Fuller , A R Jones . International Journal of Remote Sensing 1996. 17 p. .
  26. , G Hurtt , X Xiao , M Keller , M Palace , G P Asner , R Braswell , E S Brondizio , M Cardoso , C J R Carvalho , M G Fearon , L Guild , D Lu , Q Weng , Hagen , S Hetrick , S Moore Iii , B Nobre , C Read , J M Sa , T Schloss , A Vourlitis , G Wickel , AJ . 2003. 88 p. . IKONOS imagery for the Large ScaleBiosphere-Atmosphere Experiment in Amazonia (LBA). Remote Sensing of Environment
  27. An object-specific image-texture analysis of H-resolution forest imagery. G J Hay , K O Niemann , G F Mclean . Remote Sensing of Environment 1996. 55 p. .
  28. A neural network land use classifier for SAR images using textural and fractal information. H K Low , H T Chuah , H T Ewe . Geocarto International 1999. 14 p. .
  29. Designing a rulebased classifier using syntactical approach. H M Onsi . International Journal of Remote Sensing 2003. 24 p. .
  30. Remote sensing image analysis using a neural network and knowledge-based processing. H Murai , S Omatu . International Journal of Remote Sensing 1997. 18 p. .
  31. Classification and feature extraction of AVIRIS data. J A Benediktsson , J R Sveinsson , K Arnason . IEEE Transactions on Geo science and Remote Sensing 1995. 33 p. .
  32. Classification of multisource and hyperspectral data based on decision fusion. J A Benediktsson , I Kanellopoulos . IEEE Transactions on Geoscience and Remote Sensing 1999. 37 p. .
  33. Techniques for mapping suburban sprawl. J Epstein , K Payne , E Kramer . Photogrammetric Engineering and Remote Sensing 2002. 68 p. .
  34. Applying evidential reasoning methods to agricultural land cover classification. J K Lein . International Journal of Remote Sensing 2003. 24 p. .
  35. The semivariogram in comparison to the co-occurrence matrix for classification of image texture. J R Carr , F P Miranda . IEEE Transactions on Geoscience and Remote Sensing 1998. 36 p. .
  36. An investigation of the selection of texture features for crop discrimination using SARimagery. J V Soares , C D Renno , A R Formaggio , C C F Yanasse , A C Frery . Remote Sensing of Environment 1997. 59 p. .
  37. Classification of forest volume resources using ERS JERS -1 L -band SAR images. K J Tansey , A J Luckman , L Skinner , H Balzter , T Strozzi , W Wagner . International Journal of Remote Sensing 2004. 22 p. .
  38. Neural classification of SPOT imagery through integration of intensity and fractal information. K S Chen , S K Yen , D W Tsay . International Journal of Remote Sensing 1997. 18 p. .
  39. Fusion of spectral and shapefeatures for identification of urban surface cover types using reflective and thermalhyperspectral data. K Segl , S Roessner , U Heiden , H Kaufmann . ISPRS Journal of Photogrammetry and Remote Sensing 2003. 58 p. .
  40. Mapping coastal vegetation using an expert system and hyperspectral imagery. K S Schmidt , A K Skidmore , E H Kloosterman , H Van Oosten , L Kumar , J A M Janssen . Photogrammetric Engineering and Remote Sensing 2004. 70 p. .
  41. Mapping boreal vegetation using Landsat TM and topographic mapdata in a stratified approach. L B Bronge . Canadian Journal of Remote Sensing 1999. 25 p. .
  42. Multisource classification of complex rural areas by statistical and neural-network approaches. L Bruzzone , C Conese , F Maselli , F Roli . IEEE Transactions on Geoscience and Remote Sensing 1997. 63 p. . (Photogrammetric Engineering and Remote Sensing)
  43. Classification of hyper dimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks. L O Jimenez , A Morales-Morell , A Creus . analysis of AVIRIS data. Remote Sensing of Environment 1999. 37 p. . (IEEE Transactions on Geo science and Remote Sensing)
  44. Comparison of IKONOS and Quick Bird images for mapping mangrove species on the Caribbean coast of panama. remote Sensing of Environment, L Wang , W P Sousa , P Gong , G S Biging . 2004. 91 p. .
  45. Improvement of classification in urban areas by the use of textural features: the case study of Lucknow city. M A Shaban , O Dikshit . International Journal of Remote Sensing 2001. 22 p. .
  46. Synergistic use lidar and color aerial photography for mapping urban parcel imperviousness. photogrammetric Engineering and Remote Sensing, M E Hodgson , J R Jensen , J A Tullis , K D Riordan , C M Archer . 2003. 69 p. .
  47. Species classification of individually segmented tree crowns in high resolution aerial images using radiometric and morphologic image measures. M Erikson . Remote Sensing of Environment 2004. 91 p. .
  48. Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier. M F Augusteijn , L E Clemens , K A Shaw . IEEE Transactions on Geo science and Remote Sensing 1995. 33 p. .
  49. A subpixel classifier for urban land-cover mapping based on a maximum-likelihood approach and expert system rules. M Hung , M K Ridd . Photogrammetric Engineering and Remote Sensing 2002. 68 p. .
  50. Inferring urban land use from satellite sensor imagesusing kernel-based spatial reclassification, M J Barnsley , S L Barr . 1996.
  51. Assessment of the effectiveness of support vector machines for hyper spectral data. M Pal , P M Mather . Future Generation Computer System 2004. 20 p. .
  52. Textural analysis of IRS-1D panchromatic data for land cover classification. Narasimha , P V Rao , M V R Sesha Sai , K Sreenivas , M V Rao , B R M Rao , R S Dwivedi , L Venkataratnam . International Journal of Remote Sensing 2002. 23 p. .
  53. O B Butusov . Textural classification of forest types from Land sat 7 imagery. Mapping Sciences and Remote Sensing, 2003. 40 p. .
  54. Textural and contextual land-cover classification using single and multiple classifier systems. O Debeir , Van Den , I Steen , P Latinne , P Van Ham , E Wolff . Photogrammetric Engineering and Remote Sensing 2002. 68 p. .
  55. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. P M Harris , S J Ventura , G S Okin , D A Roberts , B Murray , W J Okin . Remote Sensing of Environment 1995. 2001. 77 p. . (The integration of geographic data with remotelysensed 202)
  56. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. P S Thenkabail , E A Enclona , M S Ashton , B Van Der Meer . Remote Sensing of Environment 2004a. 91 p. .
  57. Urban built-up land change detection with road density and spectral information from multitemporal LandsatTM data. Q Zhang , J Wang , X Peng , P Gong , P Shi . International Journal of Remote Sensing 2002. 23 p. .
  58. A rule-based urban land use inferring method for fine resolution multispectral imagery. Q Zhang , J Wang . Canadian Journal of Remote Sensing 2003. 29 p. .
  59. Supervised classification of remotely sensed data with ongoing learning capability. R Barandela , M Juarez . International Journal of Remote Sensing 2002. 23 p. .
  60. Mapping vegetation in Yellowstone National Park using spectral feature imagery to improve classification in an urban area. R F Kokalya , D G Despain , R N Clark , K E Livo . Photogrammetric Engineering and Remote Sensing 2003. 61 p. .
  61. A quantitative assessment of a combined spectral and GIS rulebased land-cover classification in the Neuse river basin of North Carolina. R S Lunetta , J Ediriwckrema , J Iiames , D M Johnson , J G Lyon , A Mckerrow , A Pilant . Photogrammetric Engineering and Remote Sensing 2003. 69 p. .
  62. A comparison of AVIRIS and Land sat for land use classification at the urban fringe. R V Platt , A F H Goetz . Photogrammetric Engineering and Remote Sensing 2004. 70 p. .
  63. Mapping land use/cover distribution on amountainous tropical island using remote sensing and GIS. S M J Baban , K W Yusof . International Journal of Remote Sensing 2001. 22 p. .
  64. Utilizing geometric attributes of spatial linformation to improve digital image classification. S Narumalani , Y Zhou , D E Jelinski . Remote Sensing Reviews 1998. 16 p. .
  65. Land use classification with textural analysis and the aggregation technique using multi-temporal, T Kurosu , S Yokoyama , K Chiba . 2001.
  66. The use of census data in urban image classification. V Mesev . Photogrammetric Engineering and Remote Sensing 1998. 64 p. .
  67. Monitoring urban land coverchange: an expert system approach to land cover classification of semiarid to aridurban centers. W L Stefanov , M S Ramsey , P R Christensen . Remote Sensing of Environment 2001. 77 p. .
  68. Integration of classification methods for improvement of land-cover map accuracy. X Liu , A K Skidmore , H V Oosten . ISPRS Journal of Photogrammetry and Remote Sensing 2002b. 56 p. .
  69. Optimisation of building detection in satellite images by combining multispectral classification and texture filtering. Y Zhang . ISPRS Journal of Photogrammetry and Remote Sensing 1999. 54 p. .
  70. Combining nonparametric models for multisource predictive forest mapping. Z Huang , B G Lees . Photogrammetric Engineering and Remote Sensing 2004. 70 p. .
Notes
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© 20 15 Global Journals Inc. (US)
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© 2015 Global Journals Inc. (US)
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F e XV Issue VI Version I Techniques in Image Classification; A Survey
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Date: 2015 2015-03-15