A Novel Multi-scale 3D Area Morphological Filtering Method for Airborne LiDAR Building Extraction

2016 ◽  
Vol 10 (6) ◽  
pp. 267-276 ◽  
Author(s):  
Cao Zhimin ◽  
Wu Yun
Keyword(s):  
Airborne Lidar ◽  
Building Extraction ◽  
Filtering Method ◽  
Multi Scale ◽  
Morphological Filtering

scholarly journals A Multi-Task Network with Distance–Mask–Boundary Consistency Constraints for Building Extraction from Aerial Images

2021 ◽  
Vol 13 (14) ◽  
pp. 2656
Author(s):  
Furong Shi ◽  
Tong Zhang
Keyword(s):  
Distance Estimation ◽  
Image Data ◽  
Learning Technologies ◽  
Aerial Images ◽  
Superior Performance ◽  
Aerial Image ◽  
Great Success ◽  
Building Extraction ◽  
Shape Information ◽  
Multi Scale

Deep-learning technologies, especially convolutional neural networks (CNNs), have achieved great success in building extraction from areal images. However, shape details are often lost during the down-sampling process, which results in discontinuous segmentation or inaccurate segmentation boundary. In order to compensate for the loss of shape information, two shape-related auxiliary tasks (i.e., boundary prediction and distance estimation) were jointly learned with building segmentation task in our proposed network. Meanwhile, two consistency constraint losses were designed based on the multi-task network to exploit the duality between the mask prediction and two shape-related information predictions. Specifically, an atrous spatial pyramid pooling (ASPP) module was appended to the top of the encoder of a U-shaped network to obtain multi-scale features. Based on the multi-scale features, one regression loss and two classification losses were used for predicting the distance-transform map, segmentation, and boundary. Two inter-task consistency-loss functions were constructed to ensure the consistency between distance maps and masks, and the consistency between masks and boundary maps. Experimental results on three public aerial image data sets showed that our method achieved superior performance over the recent state-of-the-art models.

scholarly journals Study on Fault Diagnosis Method of Planetary Gearbox Based on Turn Domain Resampling and Variable Multi-Scale Morphological Filtering

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 52
Author(s):  
Tongtong Liu ◽  
Lingli Cui ◽  
Chao Zhang
Keyword(s):  
Frequency Conversion ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Operating Efficiency ◽  
Planetary Gearbox ◽  
Noise Interference ◽  
Multi Scale ◽  
Morphological Filtering ◽  
Diagnosis Method ◽  
Simulation Signal

The turn domain resampling (TDR) method is proposed in the paper on the basis of the existing angle domain resampling for solving the problem of non-fixed fault frequency under variable working conditions. TDR can select the appropriate sampling order according to the influence of frequency conversion, which avoided the error caused by the spline interpolation method. It can provide accurate parameters for the subsequent calculation of the equivalent frequency order. Variable multi-scale morphological filtering (VMSMF) method is proposed for the purpose of further reducing the interference of noise in resampling signal to feature extraction. VMSMF adaptively selects structural elements according to the parameter change of impact signal to make its scale more targeted. It only needs to calculate once using the optimal structural unit for a particular impact, and the filtering accuracy and operating efficiency have been greatly improved. The main steps of this article are as follows. First, the TDR is used to resample the original signal as to get the resampling signal which is still submerged by the strong noise. In the second step, VMSMF is used to filter the resampling signal to obtain the signal with less noise interference. Finally, the fault characteristics of the filtering signal was extracted and compared with the possible fault frequency calculated by the sampling parameters provided by resampling, so as to determine the fault type of the planetary gearbox. By analyzing the simulation signal and the experimental signal respectively, this method can find out the corresponding fault characteristics effectively.

A performance enhanced time-varying morphological filtering method for bearing fault diagnosis

Measurement ◽  
2021 ◽  
Vol 176 ◽  
pp. 109163
Author(s):  
Bingyan Chen ◽  
Dongli Song ◽  
Weihua Zhang ◽  
Yao Cheng ◽  
Zhiwei Wang
Keyword(s):  
Fault Diagnosis ◽  
Time Varying ◽  
Filtering Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Morphological Filtering ◽  
A Performance

A multi-scale registration of urban aerial image with airborne lidar data

2015 ◽  
Author(s):  
Shuo Huang ◽  
Siying Chen ◽  
Yinchao Zhang ◽  
Pan Guo ◽  
He Chen
Keyword(s):  
Airborne Lidar ◽  
Aerial Image ◽  
Lidar Data ◽  
Multi Scale ◽  
Airborne Lidar Data

AN IMPROVED PROGRESSIVE MORPHOLOGICAL FILTERING ALGORITHM BASED ON SPATIALLY-DISTRIBUTED SLOPE VALUE OVER TROPICAL VEGETATED REGIONS

2015 ◽  
Vol 77 (26) ◽  
Author(s):  
Zamri Ismail ◽  
Muhammad Zulkarnain Abdul Rahman ◽  
Mohd Radhie Mohd Salleh ◽  
Ibrahim Busu ◽  
Shahabuddin Amerudin ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Entire Area ◽  
Filtering Algorithm ◽  
Morphological Filtering ◽  
Spatially Distributed ◽  
Elevation Data ◽  
Map Generation ◽  
Ground Point ◽  
Constant Slope ◽  
Airborne Lidar Data

This paper presents a thorough investigation on integrating slope map on the existing progressive morphological filtering algorithm for ground point’s extraction. The existing filtering algorithm employs constant slope value for the entire area. The slope map is either generated from field collected elevation data or ground point obtained from initial filtering of airborne LiDAR data. The filtering process has been performed with recursive mode and it stops after the results of the filtering does not show any improvement and the DTM error larger than the previous iteration. The results show that both data used for slope map generation have decreasing pattern of DTM error with increasing in filtering iteration. The spatially-distributed slope map has significantly improved the quality of the DTM compared to the results of filtering based on a constant slope value

scholarly journals Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image

2020 ◽  
Vol 12 (11) ◽  
pp. 1702 ◽  
Author(s):  
Thanh Huy Nguyen ◽  
Sylvie Daniel ◽  
Didier Guériot ◽  
Christophe Sintès ◽  
Jean-Marc Le Caillec
Keyword(s):  
Large Scale ◽  
Active Contour Model ◽  
Super Resolution ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Force Model ◽  
Building Extraction ◽  
Snake Model ◽  
Object Based

Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such a challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution Light Detection and Ranging (LiDAR)-based elevation images—called z-images—generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%.

scholarly journals A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 482 ◽  
Author(s):  
Qi Bi ◽  
Kun Qin ◽  
Han Zhang ◽  
Ye Zhang ◽  
Zhili Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Common Knowledge ◽  
Remote Sensing Image ◽  
Morphological Operations ◽  
Building Extraction ◽  
Multi Scale ◽  
Training Samples ◽  
Image Building ◽  
Very High

Building extraction plays a significant role in many high-resolution remote sensing image applications. Many current building extraction methods need training samples while it is common knowledge that different samples often lead to different generalization ability. Morphological building index (MBI), representing morphological features of building regions in an index form, can effectively extract building regions especially in Chinese urban regions without any training samples and has drawn much attention. However, some problems like the heavy computation cost of multi-scale and multi-direction morphological operations still exist. In this paper, a multi-scale filtering building index (MFBI) is proposed in the hope of overcoming these drawbacks and dealing with the increasing noise in very high-resolution remote sensing image. The profile of multi-scale average filtering is averaged and normalized to generate this index. Moreover, to fully utilize the relatively little spectral information in very high-resolution remote sensing image, two scenarios to generate the multi-channel multi-scale filtering index (MMFBI) are proposed. While no high-resolution remote sensing image building extraction dataset is open to the public now and the current very high-resolution remote sensing image building extraction datasets usually contain samples from the Northern American or European regions, we offer a very high-resolution remote sensing image building extraction datasets in which the samples contain multiple building styles from multiple Chinese regions. The proposed MFBI and MMFBI outperform MBI and the currently used object based segmentation method on the dataset, with a high recall and F-score. Meanwhile, the computation time of MFBI and MBI is compared on three large-scale very high-resolution satellite image and the sensitivity analysis demonstrates the robustness of the proposed method.

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