Experimental demonstration for the attitude measurement capability of interferometric radar altimeter

Author(s):  
Yunhua Zhang ◽  
Wenshuai Zhai ◽  
Xiang Gu ◽  
Xiaojin Shi ◽  
Xueyan Kang
2020 ◽  
Vol 14 (7) ◽  
pp. 2235-2251 ◽  
Author(s):  
Michael Kern ◽  
Robert Cullen ◽  
Bruno Berruti ◽  
Jerome Bouffard ◽  
Tania Casal ◽  
...  

Abstract. The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) mission is one of six high-priority candidate missions (HPCMs) under consideration by the European Commission to enlarge the Copernicus Space Component. Together, the high-priority candidate missions fill gaps in the measurement capability of the existing Copernicus Space Component to address emerging and urgent user requirements in relation to monitoring anthropogenic CO2 emissions, polar environments, and land surfaces. The ambition is to enlarge the Copernicus Space Component with the high-priority candidate missions in the mid-2020s to provide enhanced continuity of services in synergy with the next generation of the existing Copernicus Sentinel missions. CRISTAL will carry a dual-frequency synthetic-aperture radar altimeter as its primary payload for measuring surface height and a passive microwave radiometer to support atmospheric corrections and surface-type classification. The altimeter will have interferometric capabilities at Ku-band for improved ground resolution and a second (non-interferometric) Ka-band frequency to provide information on snow layer properties. This paper outlines the user consultations that have supported expansion of the Copernicus Space Component to include the high-priority candidate missions, describes the primary and secondary objectives of the CRISTAL mission, identifies the key contributions the CRISTAL mission will make, and presents a concept – as far as it is already defined – for the mission payload.


2008 ◽  
Vol 128 (4) ◽  
pp. 677-682 ◽  
Author(s):  
Taku Takaku ◽  
Noriyuki Iwamuro ◽  
Yoshiyuki Uchida ◽  
Ryuichi Shimada

Author(s):  
Suresha .M ◽  
. Sandeep

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.


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