Automatic Detection and Association Analysis of Multiple Surface Defects on Shield Subway Tunnels.

The surface defects on a shield subway tunnel can significantly affect the serviceability of the tunnel structure and may compromise operation safety. To effectively detect multiple surface defects, this study uses a tunnel inspection trolley (TIT) based on the mobile laser scanning technique. By conducting an inspection of the shield tunnel on a metro line section, various surface defects are identified with the TIT, including water leakage defects, dislocation, spalling, cross-section deformation, etc. To explore the root causes of the surface defects, association rules between different defects are calculated using an improved Apriori algorithm. The results show that: (i) there are significant differences in different association rules for various surface defects on the shield tunnel; (ii) the average confidence of the association rule "dislocation & spalling → water leakage" is as high as 57.78%, indicating that most of the water leakage defects are caused by dislocation and spalling of the shield tunnel in the sections being inspected; (iii) the weakest rule appears at "water leakage → spalling", with an average confidence of 13%. The association analysis can be used for predicting the critical defects influencing structural reliability and operation safety, such as water leakage, and optimizing the construction and maintenance work for a shield subway tunnel.

Cascade Superpixel Regularized Gabor Feature Fusion for Hyperspectral Image Classification.

A 3-D Gabor wavelet provides an effective way to obtain the spectral-spatial-fused features for hyperspectral image, which has shown advantageous performance for material classification and recognition. In this paper, instead of separately employing the Gabor magnitude and phase features, which, respectively, reflect the intensity and variation of surface materials in local area, a cascade superpixel regularized Gabor feature fusion (CSRGFF) approach has been proposed. First, the Gabor filters with particular orientation are utilized to obtain Gabor features (including magnitude and phase) from the original hyperspectral image. Second, a support vector machine (SVM)-based probability representation strategy is developed to fully exploit the decision information in SVM output, and the achieved confidence score can make the following fusion with Gabor phase more effective. Meanwhile, the quadrant bit coding and Hamming distance metric are applied to encode the Gabor phase features and measure sample similarity in sequence. Third, the carefully defined characteristics of two kinds of features are directly combined together without any weighting operation to describe the weight of samples belonging to each class. Finally, a series of superpixel graphs extracted from the raw hyperspectral image with different numbers of superpixels are employed to successively regularize the weighting cube from over-segmentation to under-segmentation, and the classification performance gradually improves with the decrease in the number of superpixels in the regularization procedure. Four widely used real hyperspectral images have been conducted, and the experimental results constantly demonstrate the superiority of our CSRGFF approach over several state-of-the-art methods.

An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms.

In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function to fit the water column contribution. The proposed method was tested on a simulated dataset and a real dataset, with the focus being mainly on the performance of retrieving bottom response and water depths. We also investigated the influence of the parameter settings on the accuracy of the bathymetry estimates. The results demonstrate that the improved quadrilateral fitting algorithm shows a superior performance in terms of low RMSE and a high detection rate in the water depth and magnitude retrieval. What's more, compared with the use of a triangular function or the existing quadrilateral function to fit the water column contribution, the presented method retrieved the least noise and the least number of unidentified waveforms, showed the best performance in fitting the return waveforms, and had consistent fitting goodness for all different water depths.

A 3-D Gabor Phase-Based Coding and Matching Framework for Hyperspectral Imagery Classification.

As manual labeling is very difficult and time-consuming, the labeled samples used to train a supervised classifier are generally limited, which become one of the biggest challenge for hyperspectral imagery classification. In order to tackle this issue, a recent trend is to exploit the structure information of materials, as which reflects the region homogeneity in the spatial domain and offers an invaluable complement to the spectral information. In this respect, 3-D Gabor wavelets have been introduced to extract joint spectral-spatial features for hyperspectral images. One the one hand, the features extracted by 3-D Gabor wavelets lead to very good performance for classification. On the other hand, its drawbacks, i.e., big number of features and high computational cost, limit its applicability. In this paper, a 3-D Gabor-wavelet-based phase coding and Hamming distance-based matching (3DGPC-HDM) framework is developed for hyperspectral imagery classification. The proposed method, instead of taking into account the large volume of Gabor magnitude features, exploits the Gabor phase features with certain orientations (i.e., the direction parallel to the spectral axis), which are then encoded by a simple quadrant bit coding scheme. After that, a normalized Hamming distance matching (HDM) method is adopted to determine the similarity of two samples, and the nearest neighbor classifier is routinely utilized for pixelwise recognition. Finally, experiments on three real hyperspectral data sets show that the proposed 3DGPC-HDM leads to very good performance. Comparisons with the state-of-the-art methods in the literature, in terms of both classifier complexity and generalization ability from very small training sets, are also included.

Formation of the 2015 Shenzhen landslide as observed by SAR shape-from-shading.

The time-series topography change of a landfill site before its failure has rarely been surveyed in detail. However, this information is important for both landfill management and early warning of landslides. Here, we take the 2015 Shenzhen landslide as an example, and we use the radar shape-from-shading (SFS) technique to retrieve time-series digital elevation models of the landfill. The results suggest that the total filling volume reached 4,074,300 m3 in the one and a half years before the landslide, while 2,817,400 m3 slid down in the accident. Meanwhile, the landfill rate in most areas exceeded 2 m/month, which is the empirical upper threshold in landfill engineering. Using topography captured on December 12, 2015, the slope safety analysis gives a factor of safety of 0.932, suggesting that this slope was already hazardous before the landslide. We conclude that the synthetic aperture radar (SAR) SFS technique has the potential to contribute to landfill failure monitoring.