Information fusion for rural land-use classification with high-resolution satellite imagery

We propose an information fusion method for the extraction of land-use information based on both the panchromatic and multispectral Indian Remote Sensing Satellite 1C (IRS-1C) satellite imagery. It integrates spectral, spatial and structural information existing in the image. A thematic map was first produced with a maximum-likelihood classification (MLC) applied to the multispectral imagery. Probabilistic relaxation (PR) was then performed on the thematic map to refine the classification with neighborhood information. Furthermore, we incorporated edges extracted from the higher resolution panchromatic imagery in the classification. An edge map was generated using operations such as edge detection, edge thresholding and edge thinning. Finally, a modified region-growing approach was used to improve image classification. The procedure proved to be more effective in land-use classification than conventional methods based only on multispectral data. The improved land-use map is characterized with sharp interregional boundaries, reduced number of mixed pixels and more homogeneous regions. The overall kappa statistics increased considerably from 0.52 before the fusion to 0.75 after.

[1]  Ralph Bernstein,et al.  Gaussian Maximum Likelihood and Contextual Classification Algorithms for Multicrop Classification , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[2]  M. Nagao,et al.  Edge preserving smoothing , 1979 .

[3]  David A. Landgrebe,et al.  Stochastic Model Utilizing Spectral and Spatial Characteristics , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Nabil Jean Naccache,et al.  SPTA: A proposed algorithm for thinning binary patterns , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Ioannis Pitas,et al.  Digital Image Processing Algorithms , 1993 .

[6]  Philip H. Swain,et al.  Bayesian contextual classification based on modified M-estimates and Markov random fields , 1996, IEEE Trans. Geosci. Remote. Sens..

[7]  Bir Bhanu,et al.  Analysis of terrain using multispectral images , 1997, Pattern Recognit..

[8]  P. Swain,et al.  On the accuracy of pixel relaxation labeling , 1981 .

[9]  Ching Y. Suen,et al.  Thinning Methodologies - A Comprehensive Survey , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Peng Gong.  Reducing boundary effects in a kernel-based classifier , 1994 .

[11]  David A. Landgrebe,et al.  The development of a spectral-spatial classifier for earth observational data , 1980, Pattern Recognit..

[12]  T. Kusaka,et al.  Hierarchical Classification Of Landsat Tm Image Using Spectral And Spatial Information , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

[13]  Alain Hillion,et al.  An information fusion method for multispectral image classification postprocessing , 1998, IEEE Trans. Geosci. Remote. Sens..

[14]  Azriel Rosenfeld,et al.  A Relaxation Method for Multispectral Pixel Classification , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Alan H. Strahler,et al.  The Use of Prior Probabilities in Maximum Likelihood Classification , 1980 .

[16]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[17]  Charles W. Therrien,et al.  An estimation-theoretic approach to terrain image segmentation , 1983, Comput. Vis. Graph. Image Process..

[18]  L. D. Miller,et al.  Spatial land-use inventory, modeling, and projection/Denver metropolitan area, with inputs from existing maps, airphotos, and LANDSAT imagery , 1978 .

[19]  Ching Y. Suen,et al.  A fast parallel algorithm for thinning digital patterns , 1984, CACM.

[20]  J. A. Richards,et al.  Pixel Labeling by Supervised Probabilistic Relaxation , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Godfried T. Toussaint,et al.  The use of context in pattern recognition , 1978, Pattern Recognit..

[22]  M. Goldberg,et al.  A hierarchical expert system for updating forestry maps with Landsat data , 1985, Proceedings of the IEEE.

[23]  Shmuel Peleg,et al.  A New Probabilistic Relaxation Scheme , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[25]  N. Khazenie,et al.  Spatial-temporal Autocorrelated Model For Contextual Classification , 1990 .

[26]  Laurent Mascarilla,et al.  Combination of remote sensing and geocoded data for satellite image interpretation based on neural networks , 1993, Proceedings of IGARSS '93 - IEEE International Geoscience and Remote Sensing Symposium.

[27]  C. Chow,et al.  Automatic boundary detection of the left ventricle from cineangiograms. , 1972, Computers and biomedical research, an international journal.

[28]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[29]  Olivier D. Faugeras,et al.  Improving Consistency and Reducing Ambiguity in Stochastic Labeling: An Optimization Approach , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  R. Kettig,et al.  Classification of Multispectral Image Data by Extraction and Classification of Homogeneous Objects , 1976, IEEE Transactions on Geoscience Electronics.

[31]  M. J. Carlotto,et al.  Spectral Shape Classification of Landsat Thematic Mapper Imagery , 1998 .

[32]  Stephen B. Vardeman,et al.  Contextual classification of multispectral image data , 1981, Pattern Recognit..

[33]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[34]  Xie Yuan-dan,et al.  Survey on Image Segmentation , 2002 .

[35]  Azriel Rosenfeld,et al.  Some experiments on variable thresholding , 1979, Pattern Recognit..

[36]  Horst Bischof,et al.  Multispectral classification of Landsat-images using neural networks , 1992, IEEE Trans. Geosci. Remote. Sens..

[37]  Philip J. Howarth,et al.  Performance analyses of probabilistic relaxation methods for land-cover classification☆ , 1989 .

[38]  F. E. Townsend.  The enhancement of computer classifications by logical smoothing , 1986 .

[39]  P. Gong,et al.  The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. , 1990 .

[40]  Emanuele Trucco,et al.  Computer and Robot Vision , 1995 .

[41]  Linda G. Shapiro,et al.  Image Segmentation Techniques , 1984, Other Conferences.

[42]  Yung-Sheng Chen,et al.  A modified fast parallel algorithm for thinning digital patterns , 1988, Pattern Recognit. Lett..

[43]  Hyonam Joo,et al.  A Context Classifier , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Stephen B. Vardeman,et al.  Estimation of Context for Statistical Classification of Multispectral Image Data , 1982, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Jon Sticklen,et al.  Knowledge-based segmentation of Landsat images , 1991, IEEE Trans. Geosci. Remote. Sens..

[46]  Stephen Wharton,et al.  A Spectral-Knowledge-Based Approach for Urban Land-Cover Discrmination , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[47]  King-Sun Fu,et al.  Shape Discrimination Using Fourier Descriptors , 1977, IEEE Trans. Syst. Man Cybern..

[48]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[49]  Makoto Nagao,et al.  A Structural Analysis of Complex Aerial Photographs , 1980, Advanced Applications in Pattern Recognition.

[50]  Pramod K. Varshney,et al.  Special issue on data fusion , 1997 .

[51]  John Haslett,et al.  Maximum likelihood discriminant analysis on the plane using a Markovian model of spatial context , 1985, Pattern Recognit..

[52]  Azriel Rosenfeld,et al.  Scene Labeling by Relaxation Operations , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[53]  Robert M. Haralick,et al.  Multispectral image context classification using stochastic relaxation , 1990, IEEE Trans. Syst. Man Cybern..

[54]  Philip J. Howarth,et al.  Land-use classification of SPOT HRV data using a cover-frequency method , 1992 .

[55]  P. Gong,et al.  Frequency-based contextual classification and gray-level vector reduction for land-use identification , 1992 .