MuSIP multi-sensor image processing system

Abstract The MuSIP Multi-Sensor Image Processing project has developed a proof-of-concept software demonstrator for the fusion and analysis of images within a knowledgebased environment. The target applications are the analysis of remotely sensed satellite images for the monitoring of forestry, and the analysis of medical images of the human head. A key element has been the development of data fusion techniques for combining multi-sensor and multi-temporal images. The system architecture is generic and modular, and the system control allows automatic planning and algorithm scheduling. The system includes a spatial database manager capable of handling very large quantities of raster and vector data within a tiled-image environment a sophisticated facility for database interrogation a window-based user interface, and a large set of image processing algorithms. The latter include algorithms for low level processing, image interpretation, automatic image registration, data fusion, and change detection. The system has been implemented on a Sun workstation with selected low level algorithms accelerated by a transputer array.

[1]  John A. Richards,et al.  Knowledge-based techniques for multi-source classification , 1990 .

[2]  Sebastiano B. Serpico,et al.  Remote Sensing Data Fusion By Means Of A Region-overlapping Technique , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

[3]  Gunilla Borgefors,et al.  Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  N. Nandhakumar,et al.  MULTISENSOR INTEGRATION - EXPERIMENTS IN INTEGRATING THERMAL AND VISUAL SENSORS. , 1987 .

[5]  Hsi-Jian Lee,et al.  Region matching and depth finding for 3D objects in stereo aerial photographs , 1990, Pattern Recognit..

[6]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[7]  Martin D. Levine,et al.  Low Level Image Segmentation: An Expert System , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Takashi Matsuyama.  Knowledge-Based Aerial Image Understanding Systems and Expert Systems for Image Processing , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Makoto Nagao,et al.  Control strategies in pattern analysis , 1984, Pattern Recognit..

[10]  Mike James,et al.  Classification Algorithms , 1986, Encyclopedia of Machine Learning and Data Mining.

[11]  Bernd Radig,et al.  Image sequence analysis using relational structures , 1984, Pattern Recognit..

[12]  Wesley E. Snyder,et al.  Matching oversegmented 3D images to models using association graphs , 1989, Image Vis. Comput..

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

[14]  David C. Hogg,et al.  Progress in automatic analysis of multi-temporal remotely-sensed data , 1989 .

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

[16]  John P. McDermott,et al.  Rule-Based Interpretation of Aerial Imagery , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.