Phase Error Compensation in Airborne Synthetic Aperture Lidar Data Processing

2015 ◽  
Vol 35 (8) ◽  
pp. 0801002
Author(s):  
鲁天安 Lu Tianan ◽  
李洪平 Li Hongping
Optik ◽  
2019 ◽  
Vol 178 ◽  
pp. 830-840
Author(s):  
Shuai Wang ◽  
Maosheng Xiang ◽  
Bingnan Wang ◽  
Fubo Zhang ◽  
Yirong Wu

2013 ◽  
Vol 50 (10) ◽  
pp. 102801
Author(s):  
阮航 Ruan Hang ◽  
吴彦鸿 Wu Yanhong ◽  
叶伟 Ye Wei ◽  
贾鑫 Jia Xin

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Lei Zhang ◽  
Yunkai Deng ◽  
Robert Wang

Multi-input multioutput (MIMO) is a novel technique to achieve high-resolution as well as wide swath in synthetic aperture radar (SAR) systems. Channel imbalance is inevitable in multichannel systems that it declines the imaging quality. Generally, the imbalance cannot be fully compensated by simple internal calibration in a MIMO-SAR system. In this paper, a new algorithm based on raw data is presented to remove the channel phase error. Based on the error source, this approach models the phase error as two parts: the transmit phase error and the receive phase error. The receive phase error is removed using cost function at the azimuth processing stage, whereas the transmit phase error is estimated with correlation. Point target simulations confirm the influence of channel phase error and the validation of the proposed approach. Besides, the performance is also investigated.


2007 ◽  
Vol 46 (22) ◽  
pp. 4879 ◽  
Author(s):  
Valery Shcherbakov

2015 ◽  
Vol 12 (22) ◽  
pp. 6637-6653 ◽  
Author(s):  
M. B. Collins ◽  
E. T. A. Mitchard

Abstract. Forests with high above-ground biomass (AGB), including those growing on peat swamps, have historically not been thought suitable for biomass mapping and change detection using synthetic aperture radar (SAR). However, by integrating L-band (λ = 0.23 m) SAR from the ALOS and lidar from the ICESat Earth-Observing satellites with 56 field plots, we were able to create a forest biomass and change map for a 10.7 Mha section of eastern Sumatra that still contains high AGB peat swamp forest. Using a time series of SAR data we estimated changes in both forest area and AGB. We estimate that there was 274 ± 68 Tg AGB remaining in natural forest (≥ 20 m height) in the study area in 2007, with this stock reducing by approximately 11.4 % over the subsequent 3 years. A total of 137.4 kha of the study area was deforested between 2007 and 2010, an average rate of 3.8 % yr−1. The ability to attribute forest loss to different initial biomass values allows for far more effective monitoring and baseline modelling for avoided deforestation projects than traditional, optical-based remote sensing. Furthermore, given SAR's ability to penetrate the smoke and cloud which normally obscure land cover change in this region, SAR-based forest monitoring can be relied on to provide frequent imagery. This study demonstrates that, even at L-band, which typically saturates at medium biomass levels (ca. 150 Mg ha−1), in conjunction with lidar data, it is possible to make reliable estimates of not just the area but also the carbon emissions resulting from land use change.


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