Automatic Fast and Robust Technique to Refine Extracted SIFT Key Points for Remote Sensing Images

The scale-invariant feature transform(SIFT)ability to automatic control points(CPs)extraction is very well known on remote sensing images,however,its result inaccurate and sometimes has incorrect matching from generating a small number of false CPs pairs,their matching has high false alarm.This paper presents a method containing a modification to improve the performance of the SIFT CPs matching by applying sum of absolute difference(SAD)in different manner for the new optical satellite generation called near-equatorial orbit satellite(NEqO)and multi-sensor images.The proposed method leads to improving CPs matching with a significantly higher rate of correct matches.The data in this study were obtained from the RazakSAT satellite covering the Kuala Lumpur-Pekan area.The proposed method consists of three parts:(1)applying the SIFT to extract CPs automatically,(2)refining CPs matching by SAD algorithm with empirical threshold,and(3)evaluating the refined CPs scenario by comparing the result of the original SIFT with that of the proposed method.The result indicates an accurate and precise performance of the model,which showed the effectiveness and robustness of the proposed approach.

Impact of GCP distribution on the rectification accuracy of Landsat TM imagery in a coastal zone

The purpose is to explore the effect of the spatial distribution of ground control points （GCPs） on the accuracy of imagery rectification. Both area-distributed and linearly distributed GCPs were used to rectify a Landsat TM image of a coastal zone. Rectification accuracy was checked against 99 independent points over the intertidal mudflats with no ground control. Results indicate that the root-mean-square error of residuals over these areas is several times larger than its GCPs-measured counterpart. If the GCPs are spatially dispersed over an area, residuals fluctuate but increase steadily with distance to the source of control in easting （R^2= 0. 827）. in northing they fluctuate around 150 m until 15 km, beyond which they rise steadily at a small range of fluctuation. These residuals are less predictable from distance to the source of control than in easting （R^2= 0.517 ）. If the GCPs are distributed along a control line, residuals rise with distance to it linearly and predictably （R^2 = 0. 877） in the direction perpendicular to it. In a direction parallel to it, the distance has little impact on rectification residuals.

Hierarchical adaptive stereo matching algorithm for obstacle detection with dynamic programming

An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points （GCPs） is presented. To decrease time complexity without losing matching precision, using a multilevel search scheme, the coarse matching is processed in typical disparity space image, while the fine matching is processed in disparity-offset space image. In the upper level, GCPs are obtained by enhanced volumetric iterative algorithm enforcing the mutual constraint and the threshold constraint. Under the supervision of the highly reliable GCPs, bidirectional dynamic programming framework is employed to solve the inconsistency in the optimization path. In the lower level, to reduce running time, disparity-offset space is proposed to efficiently achieve the dense disparity image. In addition, an adaptive dual support-weight strategy is presented to aggregate matching cost, which considers photometric and geometric information. Further, post-processing algorithm can ameliorate disparity results in areas with depth discontinuities and related by occlusions using dual threshold algorithm, where missing stereo information is substituted from surrounding regions. To demonstrate the effectiveness of the algorithm, we present the two groups of experimental results for four widely used standard stereo data sets, including discussion on performance and comparison with other methods, which show that the algorithm has not only a fast speed, but also significantly improves the efficiency of holistic optimization.

On-orbit Geometric Calibration and Preliminary Accuracy Evaluation of GF-14 Satellite

GF-14 satellite is a new generation of sub-meter stereo surveying and mapping satellite in China,carrying dual-line array stereo mapping cameras to achieve 1∶10000 scale topographic mapping without Ground Control Points(GCPs).In fact,space-based high-precision mapping without GCPs is a challenging task that depends on the close cooperation of several payloads and links,of which on-orbit geometric calibration is one of the most critical links.In this paper,the on-orbit geometric calibration of the dual-line array cameras of GF-14 satellite was performed using the control points collected in the high-precision digital calibration field,and the calibration parameters of the dual-line array cameras were solved as a whole by alternate iterations of forward and backward intersection.On this basis,the location accuracy of the stereo images using the calibration parameters was preliminarily evaluated by using several test fields around the world.The evaluation result shows that the direct forward intersection accuracy of GF-14 satellite images without GCPs after on-orbit geometric calibration reaches 2.34 meters(RMS)in plane and 1.97 meters(RMS)in elevation.

Optimal combination of the correction model and parameters for the precision geometric correction of UAV hyperspectral images

Nowadays,with the rapid development of quantitative remote sensing represented by high-resolution UAV hyperspectral remote sensing observation technology,people have put forward higher requirements for the rapid preprocessing and geometric correction accuracy of hyperspectral images.The optimal geometric correction model and parameter combination of UAV hyperspectral images need to be determined to reduce unnecessary waste of time in the preprocessing and provide high-precision data support for the application of UAV hyperspectral images.In this study,the geometric correction accuracy under various geometric correction models(including affine transformation model,local triangulation model,polynomial model,direct linear transformation model,and rational function model)and resampling methods(including nearest neighbor resampling method,bilinear interpolation resampling method,and cubic convolution resampling method)were analyzed.Furthermore,the distribution,number,and accuracy of control points were analyzed based on the control variable method,and precise ground control points(GCPs)were analyzed.The results showed that the average geometric positioning error of UAV hyperspectral images(at 80 m altitude AGL)without geometric correction was as high as 3.4041 m(about 65 pixels).The optimal geometric correction model and parameter combination of the UAV hyperspectral image(at 80 m altitude AGL)used a local triangulation model,adopted a bilinear interpolation resampling method,and selected 12 edgemiddle distributed GCPs.The correction accuracy could reach 0.0493 m(less than one pixel).This study provides a reference for the geometric correction of UAV hyperspectral images.

On Stereo Model Reconstitution in Aerial Photogrammetry

This paper describes the operational issues and basic technical requirements of modern aerial photogrammetry. The accuracy of photogrammetric point determination and the y-parallax at corresponding model points is analyzed when stereo models are reconstituted by using the exterior orientation elements of aerial images. Real aerial photographs, at image scales from 1:2 500 to 1:6 0000, with DGPS/IMU data taken from various topographies in China were processed by our POS-supported bundle block adjustment program WuCAPS. The empirical results verified that the accuracy of the exterior orientation elements from bundle block adjustment meets the requirements of the specifications of topographic mapping. However, the accuracy of the exterior orientation elements determined by POS fails to meet the requirements of the specifica- tions of topographic mapping.

Automatic selection of permanent scatterers-based GCPs for refinement and reflattening in InSAR DEM generation

In DEM generation using interferometric synthetic aperture radar(InSAR),the ground control points(GCPs)for refinement and reflattening are usually selected by manual selection,field surveying,GPS points and existing basemaps,which may not be completely suitable for consequent processes.We proposed a new method(auto-PS-GCP)of GCP selection based on permanent scatterers,which automatically defines the thresholds for the coherence,amplitude,and amplitude dispersion index to select permanent scatterer as the GCPs.The GCP thinning(auto-PS-GCP-Thin)was further conducted considering the point density,distances among points and terrain conditions.We used a three-stage assessment that includes:(1)phase stability and intensity of the GCPs,(2)RMSEs of the elevations between GCPs and homonymous points in the reference DEM,and(3)generated DEM accuracy.Three areas respectively in the plain,hilly and mountainous regions were selected to verify the proposed methods.The assessment using both SRTM DEM andICESat-2 points shows that the DEM accuracy of auto-PS-GCP-Thin was improved by 20%∼30%for different areas compared to the manual,where the best DEM accuracy of 4.71 m was found in the plain area.It is concluded that the proposed methods are effective and reliable in various areas with different terrain conditions.