The phase difference method (PDM) is presented for the direction of arrival (DOA) estimation of the narrowband source. It estimates the DOA by measuring the reciprocal of the phase range of the sensor output spectra a...The phase difference method (PDM) is presented for the direction of arrival (DOA) estimation of the narrowband source. It estimates the DOA by measuring the reciprocal of the phase range of the sensor output spectra at the interest frequency bin. The peak width and variance of the PDM are presented. The PDM can distinguish closely spaced sources with different and unknown center frequencies as long as they are separated with at least one frequency bin. The simulation results show that the PDM has a better resolution than that of the conventional beamforming.展开更多
Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in moun...Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures （such as shadow）. At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM （Gray Level Co- occurrence Matrix） were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification （MLC）. Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and o.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.展开更多
基金the National Science Foundation under Grant No. 60672136the the Doctorate Foundation of Northwestern Polytechnical University under Grant No.CX200803
文摘The phase difference method (PDM) is presented for the direction of arrival (DOA) estimation of the narrowband source. It estimates the DOA by measuring the reciprocal of the phase range of the sensor output spectra at the interest frequency bin. The peak width and variance of the PDM are presented. The PDM can distinguish closely spaced sources with different and unknown center frequencies as long as they are separated with at least one frequency bin. The simulation results show that the PDM has a better resolution than that of the conventional beamforming.
基金supported jointly by Key Laboratory of Geo-special Information Technology, Ministry of Land and Resources (Grant No. KLGSIT2013-12)Knowledge Innovation Program (Grant No. KSCX1-YW-09-01) of Chinese Academy of Sciences
文摘Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures （such as shadow）. At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM （Gray Level Co- occurrence Matrix） were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification （MLC）. Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and o.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.
