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Liu C.,Tongji University | Liu C.,Key Laboratory of Advanced Engineering Surveying of NASMG | Zhou F.,Tongji University | Sun Y.,CAS Institute of Remote Sensing | And 2 more authors.
Journal of Navigation | Year: 2012

Visual navigation is comparatively advanced without a Global Positioning System (GPS). It obtains environmental information via real-time processing of the data gained through visual sensors. Compared with other methods, visual navigation is a passive method that does not launch light or other radiation applications, thus making it easier to hide. The novel navigation system described in this paper uses stereo-matching combined with Inertial Measurement Units (IMU). This system applies photogrammetric theory and a matching algorithm to identify the matching points of two images of the same scene taken from different views and obtains their 3D coordinates. Integrated with the orientation information output by the IMU, the system reduces model-accumulated errors and improves the point accuracy. © 2012 The Royal Institute of Navigation. Source


Shi B.,Tongji University | Shi B.,Shanghai Normal University | Liu C.,Tongji University | Liu C.,Key Laboratory of Advanced Engineering Surveying of NASMG | And 2 more authors.
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2012

Hyper-spectral remote sensing imagery provides a large amount of spectral and structure information. However, these availabilities challenge the traditional spectral segmentation methods which may cause salt and pepper effect and low information extraction accuracy. In order to overcome this disadvantage, texture information is proposed into feature space. A 3D-Gabor filter is used to represent the spectral/spatial properties of hyper-spectral data. Thus multi-scale, multi-oriented texture features are extracted. And feature energy from 3D feature points is projected into subspace with PCA which can represent input data with lower dimensional feature vectors. Then the image segmentation is constructed by k-means clustering. Following these steps, the initial residential areas can be obtained, but with many deficiencies including the existence of holes and useless patches. To resolve these problems, a morphological space based method is used to dissolve these residential patches. The experiment on PHI-3 data demonstrates the utility of the algorithm for residential areas recognition. Source


Wu H.,Tongji University | Wu H.,Key Laboratory of Advanced Engineering Surveying of NASMG | Liu C.,Tongji University | Liu C.,Key Laboratory of Advanced Engineering Surveying of NASMG | And 4 more authors.
International Journal of Remote Sensing | Year: 2013

An innovative model for extracting water regions from aerial images fused with light detection and ranging (lidar) data is proposed in this article. This model extracts water features from coarse to fine levels of accuracy by considering special spectral bands of existing airborne lidar systems and their spectral characteristics. The particular model consists of two parts, namely inexact water region recognition and precise water extraction. (1) A strategy of using a triangulated irregular network (TIN) is introduced to describe point clouds with a particular structure. A TIN coarsely divides the network into water and non-water regions through a threshold, which can be determined through an equation by inputting the minimum width and point density of water regions. The coarsely defined water region can be detected through overlay analysis between the aerial image and the raster surface generated from the TIN. (2) An improved mean-shift algorithm is used to remove most land pixels from the roughly recognized water to obtain precise water edges from coarse water. A new empirical formula to describe distance between multi-dimensional data is adopted. Using the mean-shift algorithm and empirical distance function, accurate water edge features are extracted from inexact water region(s). In addition, the classification field of lidar point clouds is used to remove land pixels from water features.A case study based on a point cloud data set and an aerial image is conducted to evaluate the feasibility and accuracy of the proposed model. Spatial distances between checkpoints and extracted water edges, as well as the confusion matrix of mean-shift classification, are adopted as measurements of accuracy for the extracted water edges in two case regions. Evaluation results show that the proposed model achieved continuous water-edge features, and that spatial accuracy of water edges is 0.3 to 0.4 m, at approximately the 1-2-pixel level, which is more than four times better than the maximum-likelihood classification method. General accuracy of the confusion matrix shows that mean-shift classification in the proposed model is better than 95%, which indicates excellent results. © 2013 Copyright Taylor & Francis. Source

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