Feng L.,University College London |
Muller J.-P.,University College London |
Li Y.,Chinese Academy of Surveying and Mapping CASM
European Space Agency, (Special Publication) ESA SP | Year: 2015
Baseline estimation is a key parameter to calculate phase. The accuracy of baseline estimation has a direct impact on the final relative and absolute height accuracy in InSAR processing. This paper presents an improved baseline estimation method using an external DEM based FFT, which can be used in different terrain areas and deformation areas to obtain a relative good baseline estimation result.
Wen X.,Changjiang River Scientific Research Institute |
Hu D.,Huazhong University of Science and Technology |
Dong X.,University of Tennessee at Knoxville |
Yu F.,Chinese Academy of Surveying and Mapping CASM |
And 7 more authors.
International Journal of Remote Sensing | Year: 2014
A new approach for land fog detection using daytime imagery from Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data based on the normalized difference fog index (NDFI) is proposed. NDFI is used to discriminate fog from clouds based on simulating and analysing the radiation characteristics of fog and cloud with MODIS data and the Streamer radiative transfer model. In this paper, in addition to the spectral and spatial characteristics of NDFI, the textural characteristics are introduced by using a fractal dimension. The fractal dimension is calculated with a differential box-counting approach to differentiate the texture characteristics of cloud and fog, and then the spectral and texture features are combined using an NDFI weighted fractal dimension algorithm as a new feature to improve the existing daytime fog detection approach. The performance of this approach is evaluated against ground-based measurements over China in winter, and the approach is proved to be effective in detecting land fog accurately based on the three cases. © 2014 Taylor & Francis.
Yu F.,Chinese Academy of Surveying and Mapping CASM |
Li H.,Chinese Academy of Surveying and Mapping CASM |
Zhang C.,Shandong Agricultural University |
Wan Z.,Chinese Academy of Surveying and Mapping CASM |
And 2 more authors.
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2014
This paper proposes an original experiential methodology to retrieve bare surface soil moisture by two-polarized microwave remote sensing data. In the model, we combined the roughness parameters, the root mean square S and correlative length L, and introduced a new synthetic roughness parameter Rs to describe the land surface. So, the unknown parameter in this model reduces to Rs and Fresnel reflection coefficient in normal direction Γ0. Then, Γ0 and Rs can both be retrieved using two-polarized microwave data. In situ measurements from Heihe experiments were used to test the empirical model. Results indicate that there is a strong linear relationship between the estimated soil moisture and the in situ measurements (R2=0.681, RMS=0.043).
Du Q.,Chinese Academy of Surveying and Mapping CASM |
Xie D.,Nanjing Institute of Technology |
Sun Y.,Chinese Academy of Surveying and Mapping CASM
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2015
The integration of digital aerial photogrammetry and Light Detetion And Ranging (LiDAR) is an inevitable trend in Surveying and Mapping field. We calculate the external orientation elements of images which identical with LiDAR coordinate to realize automatic high precision registration between aerial images and LiDAR data. There are two ways to calculate orientation elements. One is single image spatial resection using image matching 3D points that registered to LiDAR. The other one is Position and Orientation System (POS) data supported aerotriangulation. The high precision registration points are selected as Ground Control Points (GCPs) instead of measuring GCPs manually during aerotriangulation. The registration experiments indicate that the method which registering aerial images and LiDAR points has a great advantage in higher automation and precision compare with manual registration.
Yang J.,Chinese Academy of Surveying and Mapping CASM |
Yang J.,Wuhan University |
Zhang J.,Chinese Academy of Surveying and Mapping CASM
Photogrammetric Engineering and Remote Sensing | Year: 2015
Typical algorithms in remote sensing-based mapping, such as geometric correction, image fusion, image mosaic, and automatic DEM extractions, are data- and computation-intensive; processing on multi-core computers can improve their performance. Therefore, parallel computing methods that can fully leverage state-of-the-art hardware platforms and that can be easily adapted to these algorithms are required. In this paper, a method with high parallelism is adopted. The method integrates a recursive procedure with a parallel mechanism that is capable of concurrently processing multiple blocks on multiple cores. The parallel experiments of five categories of typical algorithms on two multi-core computers with Windows and Linux operating systems, respectively, were fulfilled. The experimental results show that although the gains of parallel performance vary for different algorithms, the processing performance achieved on multi-core computers is significantly improved. The best case on a computer with two CPUs is able to perform the DEM extractions up to 13.6 times faster than serial execution. According to these experiments, the factors influencing parallel performance on a multi-core computer are discussed. © 2015 American Society for Photogrammetry and Remote Sensing.