Forest Height Extraction Based on TomoSAR Technique Using a Novel Phase Error Correction Method

Tomography synthetic aperture radar (TomoSAR) is a cutting-edge radar observation technique that has the ability to produce three-dimensional images and can effectively extract forest vertical structure parameters, including forest height, a key forest parameter closely related to forest biomass and carbon storage. However, the phase errors in the TomoSAR data are unavoidable due to the elements such as orbit errors, which can seriously affect the quality of tomographic imaging and thus affect the accuracy of forest parameter extraction. To address this issue, various methods have been proposed. Nevertheless, they still exhibit restrictions when addressing phase errors with complex trends. To solve such problem, a novel method was developed and implemented in this paper, which includes two steps and remove parts of the phase errors with different trends sequentially. First, a wavelet decomposition and polynomial fitting-based approach was applied to each track to remove the slowly but significantly spatially-varying part of the phase errors. Secondly, the modified autofocusing algorithm is proposed to correct the remaining phase errors, which adopted the two-dimensional image entropy as the optimization indicator, providing stronger robustness compared to traditional indicator. Furthermore, in order to overcome the initial value dependency of the traditional search method, the proposed autofocusing algorithm used the particle swarm algorithm as search engine. After the phase error correction, the forest height was extracted by identifying the upper and lower boundary of the forest from the corrected TomosAR profiles. Two P-band datasets obtained in north China are adopted to examine the proposed phase error correction method. Experimental results show that compared with traditional autofocusing algorithm, the proposed method can achieve higher quality tomographic imaging results. On the basis of TomoSAR imaging, higher precision forest height extraction is obtained based on the new method.