Land cover mapping at sub-pixel scales using linear optimization techniques

Mixed pixels result when the sensorʹs instantaneous field-of-view includes more than one land cover class on the ground. For mixed pixels, fuzzy classifiers can be used, which assign a pixel to several land cover classes in proportion to the area of the pixel that each class covers. These fraction values can be assigned to sub-pixels, based on the assumption of spatial dependence and the application of linear optimization techniques. A newly proposed sub-pixel mapping algorithm was first applied to a synthetic data set with a 1-km resolution, derived from a 20-m resolution image. This algorithm yielded land cover maps at 500, 200, and 100 m resolution with accuracies close to 89%. Subsequent mode filtering further increased these values. When applied to a real data set, the accuracy reached 78%. While this study suggests the potential of the proposed technique, there is still ample scope for improvements and extensions.