Analysis of Hyperspectral Change Detection as Affected by Vegetation and Illumination Variations

摘要

This study examines the effectiveness of specific hyperspectral change detection algorithms on scenes with different illumination conditions such as shadows, low sun angles, and seasonal vegetation changes with specific emphasis placed on background suppression. When data sets for the same spatial scene on different occasions exist, change detection algorithms utilize linear predictors such as chronochrome and covariance equalization in an attempt to suppress background and improve detection of atypical manmade changes. Using a push-broom style imaging spectrometer mounted on a pan and tilt platform, visible to near infrared data sets of a scene containing specific objects are gathered. Hyperspectral system characterization and calibration is performed to ensure the production of viable data. Data collection occurs over a range of months to capture a myriad of conditions including daily illumination change, seasonal illumination change, and seasonal vegetation change. Choosing reference images, the degree of background suppression produced for various time-2 scene conditions is examined for different background classes. A single global predictor produces a higher degree of suppression when the conditions between the reference and time-2 remain similar and decreases as drastic illumination and vegetation alterations appear. Manual spatial segmentation of the scene coupled with the application of a different linear predictor for each class can improve suppression.