PE&RS September 2021 Issue

Gaussian Mixture Model of Ground Filtering Based on Hierarchical Curvature Constraints for Airborne Lidar Point Clouds?

(THIS ARTICLE IS OPEN-ACCESS)

Longjie Ye, Ka Zhang, Wen Xiao, Yehua Sheng, Dong Su, Pengbo Wang, Shan Zhang, Na Zhao, and Hui Chen

This article proposes a Gaussian mixture model of a ground filtering method based on hierarchical curvature constraints.

Detecting Geo-Positional Bias in Imagery Collected Using Small UASs

Jonathan B. Thayn, Aaron M. Paque, and Megan C. Maher

In this article, statistical methods for detecting bias in global positioning system (GPS) errors are presented and applied to imagery collected using three common unmanned aerial systems (UASs).

Double Adaptive Intensity-Threshold Method for Uneven Lidar Data to Extract Road Markings

Chengming Ye, Hongfu Li, Ruilong Wei, Lixuan Wang, Tianbo Sui, Wensen Bai, and Pirasteh Saied

Due to the large volume and high redundancy of point clouds, there are many dilemmas in road-marking extraction algorithms, especially from uneven lidar point clouds. To extract road markings efficiently, this article presents a novel method for handling the uneven density distribution of point clouds and the high reflection intensity of road markings.

Estimating Regional Soil Moisture with Synergistic Use of AMSR2 and MODIS Images

Majid Rahimzadegan, Arash Davari, and Ali Sayadi

Soil moisture content (SMC), a product of Advanced Microwave Scanning Radiometer 2 (AMSR2), is not at an adequate level of accuracy on a regional scale. This article introduces a simple method to estimate SMC while synergistically using AMSR2 and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements with higher accuracy on a regional scale.

Optimal Regularization Method Based on the L-Curve for Solving Rational Function Model Parameters

Guoqing Zhou, Man Yuan, Xiaozhu Li, Hongjun Sha, Jiasheng Xu, Bo Song, and Feng Wang

Rational polynomial coefficients in a rational function model (RFM) have high correlation and redundancy, especially in high-order RFMs, which results in ill-posed problems of the normal equation. For this reason, this article presents an optimal regularization method with the L-curve for solving rational polynomial coefficients.

Scene-Change Detection Based on Multi-Feature-Fusion Latent Dirichlet Allocation Model for High-Spatial-Resolution Remote Sensing Imagery

Xiaoman Li, Yanfei Zhong, Yu Su, and Richen Ye

With the continuous development of high-spatial-resolution ground observation technology, it is now becoming possible to obtain more and more high-resolution images, which provide us with the possibility to understand remote sensing images at the semantic level. Compared with traditional pixel- and object-oriented methods of change detection, scene-change detection can provide us with land-use change information at the semantic level, and can thus provide reliable information for urban land-use change detection, urban planning, and government management. Most of the current scene-change detection methods are based on the visual-words expression of the bag-of-visual-words model and the single-feature-based latent Dirichlet allocation model. In this article, a scene-change detection method for high-spatial-resolution imagery is proposed based on a multi-feature-fusion latent Dirichlet allocation model.