A Double Epipolar Resampling Approach to Reliable Conjugate Point Extraction for Accurate Kompsat-3/3A Stereo Data Processing

Kompsat-3/3A provides along-track and across-track stereo data for accurate three-dimensional (3D) topographic mapping. Stereo data preprocessing involves conjugate point extraction and acquisition of ground control points (GCPs), rational polynomial coefficient (RPC) bias compensation, and epipolar image resampling. Applications where absolute positional accuracy is not a top priority do not require GCPs, but require precise conjugate points from stereo images for subsequent RPC bias compensation, i.e., relative orientation. Conjugate points are extracted between the original stereo data using image-matching methods by a proper outlier removal process. Inaccurate matching results and potential outliers produce geometric inconsistency in the stereo data. Hence, the reliability of conjugate point extraction must be improved. For this purpose, we proposed to apply the coarse epipolar resampling using raw RPCs before the conjugate point matching. We expect epipolar images with even inaccurate RPCs to show better stereo similarity than the original images, providing better conjugate point extraction. To this end, we carried out the quantitative analysis of the conjugate point extraction performance by comparing the proposed approach using the coarsely epipolar resampled images to the traditional approach using the original stereo images. We tested along-track Kompsat-3 stereo and across-track Kompsat-3A stereo data with four well-known image-matching methods: phase correlation (PC), mutual information (MI), speeded up robust features (SURF), and Harris detector combined with fast retina keypoint (FREAK) descriptor (i.e., Harris). These matching methods were applied to the original stereo images and coarsely resampled epipolar images, and the conjugate point extraction performance was investigated. Experimental results showed that the coarse epipolar image approach was very helpful for accurate conjugate point extraction, realizing highly accurate RPC refinement and sub-pixel y-parallax through fine epipolar image resampling, which was not achievable through the traditional approach. MI and PC provided the most stable results for both along-track and across-track test data with larger patch sizes of more than 400 pixels.

Integration of ZiYuan-3 Multispectral and Stereo Data for Modeling Aboveground Biomass of Larch Plantations in North China

Data saturation in optical sensor data has long been recognized as a major factor that causes underestimation of aboveground biomass (AGB) for forest sites having high AGB, but there is a lack of suitable approaches to solve this problem. The objective of this research was to understand how incorporation of forest canopy features into high spatial resolution optical sensor data improves forest AGB estimation. Therefore, we explored the use of ZiYuan-3 (ZY-3) satellite imagery, including multispectral and stereo data, for AGB estimation of larch plantations in North China. The relative canopy height (RCH) image was calculated from the difference of digital surface model (DSM) data at leaf-on and leaf-off seasons, which were extracted from the ZY-3 stereo images. Image segmentation was conducted using eCognition on the basis of the fused ZY-3 multispectral and panchromatic data. Spectral bands, vegetation indices, textural images, and RCH-based variables based on this segment image were extracted. Linear regression was used to develop forest AGB estimation models, where the dependent variable was AGB from sample plots, and explanatory variables were from the aforementioned remote-sensing variables. The results indicated that incorporation of RCH-based variables and spectral data considerably improved AGB estimation performance when compared with the use of spectral data alone. The RCH-variable successfully reduced the data saturation problem. This research indicated that the combined use of RCH-variables and spectral data provided more accurate AGB estimation for larch plantations than the use of spectral data alone. Specifically, the root mean squared error (RMSE), relative RMSE, and mean absolute error values were 33.89 Mg/ha, 29.57%, and 30.68 Mg/ha, respectively, when using the spectral-only model, but they become 24.49 Mg/ha, 21.37%, and 20.37 Mg/ha, respectively, when using the combined model with RCH variables and spectral band. This proposed approach provides a new insight in reducing the data saturation problem.

Satellite Stereo Image Processing for 3D Topographic Mapping

<p><strong>Abstract.</strong> The large scale topographic map generation is mostly carried out using the aerial images while the high resolution satellite data are gaining popularity because of its large swath width that enables the efficient and economic mapping even over inaccessible areas. To use the satellite data for the 3D topographic mapping, the data should be acquired in the stereo mode and they requires to be aligned for the epipolar geometry. The accurate epipolar image resampling aligns stereo images to enable a stereo display on the computer monitor where human operators can easily identify and extract 3D features, such as points of interest, contours, building layers, and roads. The pushbroom type camera, which is used by most high-resolution satellites, has quite different epipolar geometry than the frame type of aerial or terrestrial cameras. Therefore, the piecewise approach should be used instead of the fundamental matrix approach that is the standard method for the frame type cameras. Regarding the sensor model, most high-resolution satellite data use RPCs (Rational Polynomial Coefficients) of RFM (Rational Function Model) while the frame type camera use the collinearity equation. But RPCs from the satellite image providers have not been accurate enough such that GCPs (Ground Control Points) are often required for improving the RPCs accuracy. The GCPs acquisition is not an easy task over the inaccessible areas and the positional accuracy of the old geospatial data such as orthoimages and traditional maps is relatively low. Fortunately, the positional accuracy of RPCs increases than the past such as up to several meters. The major problem to achieve high accuracy of stereo geometry is to ensure that the consistency between the stereo data is less than one-pixel level. Therefore, in this study, we first utilized the relative orientation method to improve the precision between the stereo data without using any GCP. The tie points are extracted using the robust stereo matching and they are used to generate quasi-GCPs by the space intersection based on the RPCs. The quasi-GCPs are projected back to the stereo data to model and remove the inconsistency in the image space. The improved RPCs were used to accurately align the stereo data for map production.</p>