Large area land cover mapping based on pyramid transformation with high-resolution PolSAR image

Abstract

High-resolution PolSAR images are wildly used in land cover mapping. However, there are two problems when working with large areas, i.e., long processing time and large computer memory. Considering that properties of homogeneous area, such as forest, farm land, bare land, etc., are almost the same under different resolutions, which means that these areas can be processed in relatively low-resolution without decreasing mapping accuracy, this paper proposes a new land cover mapping method, to shorten processing time and reduce the demand of memory when high resolution images are used. In this method, relatively low-resolution images, used for homogeneous areas mapping, are obtained via pyramid transformation from the original image, due to the fact that pyramid transformation has good performance in retaining main information in the transformed images. The traditional pyramid transformation is adapted to PolSAR images. To verify the proposed method, iterated Freeman-Wishart classification is used based on pyramid transformation in this paper. Mapping accuracy and processing time of classification based on pyramid transformation and without pyramid transformation are compared. Experimental results show that processing time of classification based on pyramid transformation is reduced dramatically and the classification accuracy is improved.

Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

“…The pyramid-based image process is a popular method for solving basic problems in image analysis or manipulation including data compression and shape analysis [ 28 – 30 ]. This method has recently attracted many scholars in solving various tasks that involve image processing such as automatic segmentation [ 31 ], texture classification [ 32 , 33 ], nonlinear image registration [ 34 ], and remote sensing [ 35 ].…”