You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
18 September 2018Research of forest type identification based on multi-dimensional POLSAR data in northeast China
Forests play an important role in the global carbon cycle and natural air conditioning. Monitoring and mapping of forest distribution are of great significance. With the successive launch of new synthetic aperture radar (SAR) sensors, microwave remote sensing data acquisition methods have been developed from single-band, single-polarization and single-angle to multi-frequency, multi-polarization, multi-angle, multi-temporal and so on. That provides an unprecedented potential and opportunity for SAR in the research and application of forest identification. In this paper, the data source mainly included the quad-polarization C-band GaoFen-3(GF-3) and dual-polarization L-band ALOS-1 PALSAR. First, the single-look complex (SLC) data was preprocessed with multi-look, filtering, radiation calibration, geocoding, registration and clipping. Three polarization characteristic parameters of entropy (H), scattering angle (α) and anisotropy (A) were obtained by using Cloude-Pottier polarization decomposition, and three texture features of the mean (MEAN), variance (VAR) and dissimilarity (DIS) were extracted based on the gray-level co-occurrence matrix(GLCM). Combined with the advantages of GF-3 high-resolution quad-polarization and PALSAR L-band, multi-dimensional information including frequency, polarization, temporal and texture features was used synthetically. Then support vector machine (SVM) supervised classifier was used to obtain the four classification results, including coniferous forest, broad-leaved forest, mixed broadleaf-conifer forest and others. The experimental result shows that proposed method achieved a better classification result based on multi-dimensional POLSAR, the overall accuracy of forest type identification is approximately 89.47% and the Kappa coefficient is 0.85.
The alert did not successfully save. Please try again later.
Xiaohu Zhou, Lingjia Gu, Ruizhi Ren, Xintong Fan, "Research of forest type identification based on multi-dimensional POLSAR data in northeast China," Proc. SPIE 10767, Remote Sensing and Modeling of Ecosystems for Sustainability XV, 107670K (18 September 2018); https://doi.org/10.1117/12.2318932