Landsat real-time processing
A novel method for performing real-time acquisition and processing Landsat/EROS data covers all aspects including radiometric and geometric corrections of multispectral scanner or return-beam vidicon inputs, image enhancement, statistical analysis, feature extraction, and classification. Radiometric transformations include bias/gain adjustment, noise suppression, calibration, scan angle compensation, and illumination compensation, including topography and atmospheric effects. Correction or compensation for geometric distortion includes sensor-related distortions, such as centering, skew, size, scan nonlinearity, radial symmetry, and tangential symmetry. Also included are object image-related distortions such as aspect angle (altitude), scale distortion (altitude), terrain relief, and earth curvature. Ephemeral corrections are also applied to compensate for satellite forward movement, earth rotation, altitude variations, satellite vibration, and mirror scan velocity. Image enhancement includes high-pass, low-pass, and Laplacian mask filtering and data restoration for intermittent losses. Resource classification is provided by statistical analysis including histograms, correlational analysis, matrix manipulations, and determination of spectral responses. Feature extraction includes spatial frequency analysis, which is used in parallel discriminant functions in each array processor for rapid determination. The technique uses integrated parallel array processors that decimate the tasks concurrently under supervision of a control processor. The operator-machine interface is optimized for programming ease and graphics image windowing.
- Research Organization:
- Loral Instrumentation, San Diego, CA
- OSTI ID:
- 6987597
- Report Number(s):
- CONF-8604187-
- Journal Information:
- Am. Assoc. Pet. Geol., Bull.; (United States), Journal Name: Am. Assoc. Pet. Geol., Bull.; (United States) Vol. 70:4; ISSN AAPGB
- Country of Publication:
- United States
- Language:
- English
Similar Records
Frontiers of massively parallel scientific computation
Geospatial Visualization of Global Satellite Images with Vis-EROS