A shape-based approach to change detection of lakes using time series remote sensing images

Shape analysis has not been considered in remote sensing as extensively as in other pattern recognition applications. However, shapes such as those of geometric patterns in agriculture and irregular boundaries of lakes can be extracted from the remotely sensed imagery even at relatively coarse spatial resolutions. This paper presents a procedure for efficiently retrieving and representing shapes of interesting features in remotely sensed imagery using supervised classification, object recognition, parametric contour tracing, and proposed piecewise linear polygonal approximation techniques. In addition, shape similarity can be measured by means of a computationally efficient metric. The study was conducted on a time series of radiometric and geometric rectified Landsat Multispectral Scanner (MSS) images and Thematic Mapper (TM) images, covering the scenes containing lakes in the Nebraska Sand Hills region. The results validate the effectiveness of the proposed processing chain in change detection of lake shapes and show that shape similarity is an important parameter in quantitatively measuring the spatial variations of objects.