A Review of Soft Classification Approaches on Satellite Image and Accuracy Assessment

Classification is a widely used technique for image processing and is used to extract thematic data for preparing maps in remote sensing applications. A number of factors affect the classification process. But classification is only half part of image processing and incomplete without accuracy assessment. Accuracy assessment of classification tells how accurately the classification process has been carried out. This research paper presents a review study of image classification through soft classifiers and also presents accuracy assessment of soft classifiers using entropy. Soft classifiers help in the development of more robust methods for remote sensing applications as compared to the hard classifiers. In this paper, two supervised soft classifiers, FCM, and PCM have been used to demonstrate the improvement in the classification accuracy by membership vector, RMSE, and also it has tried to generate fraction output from FCM, PCM, and noise with entropy.

[1]  Giles M. Foody,et al.  Cross-entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data , 1995 .

[2]  Paul M. Mather,et al.  An assessment of the effectiveness of decision tree methods for land cover classification , 2003 .

[3]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[4]  John Althausen.  What remote sensing system should be used to collect the data , 2001 .

[5]  Yannis Avrithis,et al.  Fuzzy image classification using multiresolution neural networks with applications to remote sensing , 1997, Proceedings of 13th International Conference on Digital Signal Processing.

[6]  A. Cracknell.  Review article Synergy in remote sensing-what's in a pixel? , 1998 .

[7]  Jayanta Kumar Ghosh.  Automated interpretation of sub-pixel vegetation from IRS LISS-II images , 2004 .

[8]  C. Özkan,et al.  Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities , 2004 .

[9]  R. G. Pontius,et al.  Categorical Coefficients of Agreement for Assessing Soft-Classified Maps at Multiple Resolutions , 2004 .

[10]  R. Lucas,et al.  Non-linear mixture modelling without end-members using an artificial neural network , 1997 .

[11]  L. P. C. Verbeke,et al.  Using genetic algorithms in sub-pixel mapping , 2003 .

[12]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[13]  Hassan Ghassemian,et al.  Measurement of uncertainty by the entropy: application to the classification of MSS data , 2006 .

[14]  Jerry M. Mendel,et al.  A fuzzy logic method for modulation classification in nonideal environments , 1999, IEEE Trans. Fuzzy Syst..

[15]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[16]  James C. Bezdek,et al.  Modified Objective Function Algorithms , 1981 .

[17]  R. M. Lark,et al.  Uncertainty in prediction and interpretation of spatially variable data on soils , 1997 .

[18]  J. Eastman,et al.  Bayesian Soft Classification for Sub-Pixel Analysis: A Critical Evaluation , 2002 .

[19]  Manoj K. Arora,et al.  An evaluation of fuzzy classifications from IRS 1C LISS III imagery: A case study , 2003 .

[20]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[21]  Giles M. Foody,et al.  Incorporating mixed pixels in the training, allocation and testing stages of supervised classifications , 1996, Pattern Recognit. Lett..

[22]  Paul Aplin,et al.  Sub-pixel land cover mapping for per-field classification , 2001 .

[23]  Giles M. Foody,et al.  An evaluation of some factors affecting the accuracy of classification by an artificial neural network , 1997 .

[24]  Elisabetta Binaghi,et al.  A fuzzy set-based accuracy assessment of soft classification , 1999, Pattern Recognit. Lett..

[25]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[26]  Anil Kumara,et al.  A COMPARISON OF THE PERFORMANCE OF FUZZY ALGORITHM VERSUS STATISTICAL ALGORITHM BASED SUB-PIXEL CLASSIFIER FOR REMOTE SENSING DATA , 2006 .

[27]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[28]  Russell G. Congalton,et al.  Quality assurance and accuracy assessment of information derived from remotely sensed data , 2001 .

[29]  Giles M. Foody,et al.  Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data , 1996 .

[30]  M. Batistella,et al.  COMPARISON OF LAND-COVER CLASSIFICATION METHODS IN THE BRAZILIAN AMAZON BASIN , 2004 .

[31]  L. Bastin.  Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels , 1997 .

[32]  Hugh G. Lewis,et al.  A generalized confusion matrix for assessing area estimates from remotely sensed data , 2001 .

[33]  G. M. Foody.  The role of soft classification techniques in the refinement of estimates of ground control point location , 2002 .

[34]  James M. Keller,et al.  A possibilistic approach to clustering , 1993, IEEE Trans. Fuzzy Syst..

[35]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[36]  G. Foody.  Sharpening fuzzy classification output to refine the representation of sub-pixel land cover distribution , 1998 .