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.

Hydrogeological Survey for Interpretation of Damaging Process in Ancient Grave of Naqsh-e-Rostam, Iran

Naqsh-e-Rostam is known as an ancient periphery which is located in northwest of Persepolis in Fars Province, lran. The oldest relief at Naqsh-e-Rostam is severely damaged and dates to 1,200 B.C.. There is a rock relief thought to be elimate, originally. Four tombs belonging to Achaemenid kings are carved out of the rock face and seven oversized rock reliefs at Naqsh-e-Rostam depict the monarchs of the Sassanid Period. These works are located in orographic mountains which are exposed to the interaction with their surrounding environment. Therefore, existing deterioration as well as erosion process is mainly observed due to climatically conditions and geo-environmental factors which have caused such challenges. Transverse and micro cracks are most kinds of damages that will result in erosion in whole of these works. The focal sources which cause ground cracks have been studied with hydro geological survey. Actually considering the storage volume changing and the calculation of hydrological budget for aquifer, this conclusion drown that the ground crack in this site is the direct result of land subsidence resulting from ground water extraction.

Density‑Based Road Segmentation Algorithm for Point Cloud Collected by Roadside LiDAR

This paper proposes a novel density-based real-time segmentation algorithm,to extract ground point cloud in real time from point cloud data collected by roadside LiDAR.The algorithm solves the problems such as the large amount of original point cloud data collected by LiDAR,which leads to heavy computational burden in ground point search.First,point cloud data is filtered by straight-through filtering method and rasterized to improve the real-time performance of the algorithm.Then,the density of the point cloud in horizontal plane is calculated,and the threshold of the density is selected to extract the low-density regional point cloud according to the density statistical histogram and 95%loci.Finally,the low-density regional point cloud is used as the initial ground seeds for iterative optimization of ground parameters,and the ground point cloud is extracted by the fitted ground model to realize road point cloud extraction.The experimental results on 1055 frames of continuous data collected on real scenes show that the average time consumption of the proposed method is 0.11 s,and the average segmentation precision is 92.48%.This shows that the density-based road segmentation algorithm can reduce the time of point cloud traversal in the process of ground parameter fitting and improve the real-time performance of the algorithm while maintaining the accuracy of ground extraction.