A phenomenological approach to multisource data integration: Analysing infrared and visible data

A new method is described for combining multisensory data for remote sensing applications. The approach uses phenomenological models which allow the specification of discriminatory features that are based on intrinsic physical properties of imaged surfaces. Thermal and visual images of scenes are analyzed to estimate surface heat fluxes. Such analysis makes available a discriminatory feature that is closely related to the thermal capacitance of the imaged objects. This feature provides a method for labelling image regions based on physical properties of imaged objects. This approach is different from existing approaches which use the signal intensities in each channel (or an arbitrary linear or nonlinear combination of signal intensities) as features - which are then classified by a statistical or evident approach.

[1]  Jake K. Aggarwal,et al.  Pyramid-based image segmentation using multisensory data , 1990, Pattern Recognit..

[2]  Robert J. Woodham,et al.  Analysing Images of Curved Surfaces , 1981, Artif. Intell..

[3]  Tong Lee,et al.  Probabilistic and Evidential Approaches for Multisource Data Analysis , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Jake K. Aggarwal,et al.  Integrated Analysis of Thermal and Visual Images for Scene Interpretation , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  J. Norman,et al.  Contrasts among Bidirectional Reflectance of Leaves, Canopies, and Soils , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[6]  P. Christensen,et al.  Martian dust mantling and surface composition: Interpretation of thermophysical properties , 1982 .

[7]  H Matlock,et al.  PREDICTION OF TEMPERATURE AND STRESSES IN HIGHWAY BRIDGES BY A NUMERICAL PROCEDURE USING DAILY WEATHER REPORTS , 1977 .

[8]  Morris Goldberg,et al.  An Expert System for Remote Sensing , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Rama Chellappa,et al.  A Method for Enforcing Integrability in Shape from Shading Algorithms , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Joseph H. Pierluissi,et al.  A Transmittance Model For Atmospheric Methane , 1985, Optics & Photonics.

[11]  Berthold K. P. Horn,et al.  Hill shading and the reflectance map , 1981, Proceedings of the IEEE.

[12]  John R. Schott,et al.  Comparison Of Modelled And Empirical Atmospheric Propagation Data , 1984, Optics & Photonics.

[13]  B. Jakosky,et al.  Global duricrust on Mars: Analysis of remote‐sensing data , 1986 .

[14]  Bruce Blanchard,et al.  Visible/Infrared/Microwave Agriculture Classification, Biomass, and Plant Height Algorithms , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Katsushi Ikeuchi,et al.  Numerical Shape from Shading and Occluding Boundaries , 1981, Artif. Intell..

[16]  F. Incropera,et al.  Fundamentals of Heat Transfer , 1981 .

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

[18]  B K Horn,et al.  Calculating the reflectance map. , 1979, Applied optics.

[19]  Roland Chin,et al.  Automated Rain-Rate Classification of Satellite Images Using Statistical Pattern Recognition , 1985, IEEE Transactions on Geoscience and Remote Sensing.