Qingwu Hu

A Robust Photogrammetric Processing Method of Low-Altitude UAV Images

Low-altitude Unmanned Aerial Vehicles (UAV) images which include distortion, illumination variance, and large rotation angles are facing multiple challenges of image orientation and image processing. In this paper, a robust and convenient photogrammetric approach is proposed for processing low-altitude UAV images, involving a strip management method to automatically build a standardized regional aerial triangle (AT) network, a parallel inner orientation algorithm, a ground control points (GCPs) predicting method, and an improved Scale Invariant Feature Transform (SIFT) method to produce large number of evenly distributed reliable tie points for bundle adjustment (BA). A multi-view matching approach is improved to produce Digital Surface Models (DSM) and Digital Orthophoto Maps (DOM) for 3D visualization. Experimental results show that the proposed approach is robust and feasible for photogrammetric processing of low-altitude UAV images and 3D visualization of products.

Feature-Based Laser Scan Matching and Its Application for Indoor Mapping

Scan matching, an approach to recover the relative position and orientation of two laser scans, is a very important technique for indoor positioning and indoor modeling. The iterative closest point (ICP) algorithm and its variants are the most well-known techniques for such a problem. However, ICP algorithms rely highly on the initial guess of the relative transformation, which will reduce its power for practical applications. In this paper, an initial-free 2D laser scan matching method based on point and line features is proposed. We carefully design a framework for the detection of point and line feature correspondences. First, distinct feature points are detected based on an extended 1D SIFT, and line features are extracted via a modified Split-and-Merge algorithm. In this stage, we also give an effective strategy for discarding unreliable features. The point and line features are then described by a distance histogram; the pairs achieving best matching scores are accepted as potential correct correspondences. The histogram cluster technique is adapted to filter outliers and provide an accurate initial value of the rigid transformation. We also proposed a new relative pose estimation method that is robust to outliers. We use the lq-norm (0 < q < 1) metric in this approach, in contrast to classic optimization methods whose cost function is based on the l2-norm of residuals. Extensive experiments on real data demonstrate that the proposed method is almost as accurate as ICPs and is initial free. We also show that our scan matching method can be integrated into a simultaneous localization and mapping (SLAM) system for indoor mapping.

Robust Feature Matching for Remote Sensing Image Registration Based on

This letter proposes a robust feature matching algorithm for remote sensing images based on lq -estimator. We start with a set of initial matches provided by a feature matching method such as scale-invariant feature transform and then focus on global transformation estimation from contaminated observations and outliers elimination as well. We use an affine model to describe the global transformation and minimize a new cost function based on lq -norm. We apply an augmented Lagrangian function and an alternating direction method of multipliers to solve such a nonconvex and nonsmooth optimization problem. Extensive experiments on real remote sensing data demonstrate that the proposed method is effective, efficient, and robust. Our method outperforms state-of-the-art methods and can easily handle situations with up to 90% outliers. In addition, the proposed method is much faster than RANSAC.

L_{q}

A multiple terrestrial laser scanner (TLS) integration approach is proposed for the fine surveying and 3D modeling of ancient wooden architecture in an ancient building complex of Wudang Mountains, which is located in very steep surroundings making it difficult to access. Three-level TLS with a scalable measurement distance and accuracy is presented for data collection to compensate for data missed because of mutual sheltering and scanning view limitations. A multi-scale data fusion approach is proposed for data registration and filtering of the different scales and separated 3D data. A point projection algorithm together with point cloud slice tools is designed for fine surveying to generate all types of architecture maps, such as plan drawings, facade drawings, section drawings, and doors and windows drawings. The section drawings together with slicing point cloud are presented for the deformation analysis of the building structure. Along with fine drawings and laser scanning data, the 3D models of the ancient architecture components are built for digital management and visualization. Results show that the proposed approach can achieve fine surveying and 3D documentation of the ancient architecture within 3 mm accuracy. In addition, the defects of scanning view and mutual sheltering can overcome to obtain the complete and exact structure in detail.

-Estimator

Shadows, which are cast by clouds, trees, and buildings, degrade the accuracy of many tasks in remote sensing, such as image classification, change detection, object recognition, etc. In this paper, we address the problem of shadow detection for complex scenes. Unlike traditional methods which only use pixel information, our method joins model and observation cues. Firstly, we improve the bright channel prior (BCP) to model and extract the occlusion map in an image. Then, we combine the model-based result with observation cues (i.e., pixel values, luminance, and chromaticity properties) to refine the shadow mask. Our method is suitable for both natural images and satellite images. We evaluate the proposed approach from both qualitative and quantitative aspects on four datasets. The results demonstrate the power of our method. It shows that the proposed method can achieve almost 85% F-measure accuracy both on natural images and remote sensing images, which is much better than the compared state-of-the-art methods.

Fine Surveying and 3D Modeling Approach for Wooden Ancient Architecture via Multiple Laser Scanner Integration

Crowd sourcing geographic data, which is contributed by lots of non-professionals and provided to the public, is an open source geographic data with characteristics of large data volume, high currency, abundance information and low cost. As a kind of crowd sourcing geographic data, check-in data contains multitudes of social attribute data and becomes a research hotspot of international geographic information science in the recent years. Take check-in data of Jiepang for instance, this paper studies spatiotemporal visualization method of check-in data, proposes a hotspot detection method based on frequency and variation thematic map of check-in data, and does some research on the spatiotemporal mining method of check-in data in the end. The correlation analysis experiment between the resident population and the amount of check-in user in different district shows that the check-in data has a high correlation with urban economy and population, indirectly reflects the distribution situation of urban economy and population, can be used for the analysis of national socioeconomic situation.

Joint Model and Observation Cues for Single-Image Shadow Detection

ABSTRACT Multispectral (MS) and panchromatic (Pan) image fusion, which is used to obtain both high spatial- and spectral-resolution images, plays an important role in many remote-sensing applications such as environmental monitoring, agriculture, and mineral exploration. This article presents an image fusion framework based on the spatial distribution consistency. First, a YUV transform is adopted to separate the luminance component from the colour components of the original MS image. Then, the relationships between the ideal high-resolution multispectral (HRMS) colour components and the Pan band are established based on the spatial distribution consistency, and finally an inverse transform is employed to obtain the fused image. In this article, two types of relationship models are presented. The first model stems from the physical meaning of the assumption and uses a local linear model to describe it. The second model directly uses its algebraic meaning to design the objective cost function and obtains the global optimal solution. The proposed two models are compared with 15 other widely used methods on six real remote-sensing image data sets. Experimental results show that the proposed method outperforms the compared state-of-the-art approaches.

Data mining and visualization research of check-in data

ABSTRACT Automatic road extraction from remotely sensed images is an important and challenging task. This article proposes an unsupervised road detection method based on a Gaussian mixture model and object-based features. Our approach has five major stages, i.e. superpixel segmentation, feature description, homogeneous region merging, clustering via the Gaussian mixture model, and outlier filtering. In the third step, we present a graph-based region merging algorithm, in which the nodes of the graph are superpixels and edges are the similarities of intensity, colour, and texture. We also define two shape features, called deviation of parallelism (DoP) and narrow rate (NR), to automatically recognize road layer and filter outliers in the last step. We evaluated the proposed method on a variety of datasets, in which the Vaihingen dataset from the International Society for Photogrammetry and Remote Sensing Test Project is also included. Results demonstrate the power of our approach compared with some state-of-the-art methods.

Multispectral and panchromatic image fusion based on spatial consistency

As an important part of the public service and educational infrastructures for national culture and heritage culture, a virtual museum presents the user experience of a real museum, with visitors, educators, and tourists interacting with the prepared digital culture contents by a mouse, touch panel, and other augmented reality devices. The goal of virtual museum is to help students and visitors to move around the virtual museum space freely and generate experience and satisfaction from the fruition of cultural heritage anytime, anywhere, and from any device. This study presents a hybrid three-dimensional virtual museum based on panoramic images and three-dimensional models. A technical framework of hybrid three-dimensional virtual museum is proposed on the basis of a typical three-tier architecture, which includes the data layer, technique supporting layer, and application layer. A hybrid three-dimensional data organization approach with geo-referenced sequence panoramic images and three-dimensional models is designed to build the data layer of hybrid three-dimensional virtual museum. A three-dimensional scene of geo-referenced sequence panoramic images and three-dimensional models is created in real time using Unity three-dimensional and web service under the mobile Internet environment for hybrid three-dimensional virtual museum. The different applications of hybrid three-dimensional virtual museum based on the data layer and technique infrastructure are designed to achieve handheld virtual museum guidance and navigation, three-dimensional browsing, and heritage culture information query for visitors with smartphones to access anytime and anywhere. As an example, a hybrid three-dimensional virtual museum application for Jinsha Archaeological Site Museum is developed with the proposed approach. The geo-referenced sequence panoramic images of museum galleries, together with three-dimensional models of cultural relics, can integrate seamlessly to a three-dimensional reality-based museum space where users can move around the space actively and freely with all kinds of personal computer and smartphone clients.

Unsupervised road extraction via a Gaussian mixture model with object-based features

In the past few years, for its lower-cost, safer and high-resolution images, unmanned aerial vehicles (UAVs) demonstrated great potential for photogrammetric measurements in numerous application fields. Nevertheless, these images are often affected by large rotation, big viewpoint change as well as small overlaps, in which case traditional procedure are not able to orientate images or generate reliable Digital Generation Models (DSM). This paper introduces the whole procedure of the DSM generation, which comprehensively utilizes advantage of both computer vision and multi-image matching algorithms in extracting points and generating a dense DSM. Experiment shows that, based on this procedure, it can quickly extract points from the high-resolution images acquired by UAVs with high location accuracy.