scholarly journals Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model

2020 ◽  
Vol 12 (5) ◽  
pp. 894 ◽  
Author(s):  
Peng Liu ◽  
Yongming Wei ◽  
Qinjun Wang ◽  
Yu Chen ◽  
Jingjing Xie
Keyword(s):  
Spatial Information ◽  
Extraction Methods ◽  
Disaster Mitigation ◽  
New Method ◽  
Automatic Extraction ◽  
Training Samples ◽  
Low Degree ◽  
Extraction Algorithm ◽  
Survey Results ◽  
Hierarchical Features

Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides.

scholarly journals A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN

2021 ◽  
Vol 10 (3) ◽  
pp. 168
Author(s):  
Peng Liu ◽  
Yongming Wei ◽  
Qinjun Wang ◽  
Jingjing Xie ◽  
Yu Chen ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Extraction Methods ◽  
New Method ◽  
Detection Accuracy ◽  
Sufficient Information ◽  
Automatic Extraction ◽  
Emergency Rescue ◽  
Feature Pyramid ◽  
High Level ◽  
Extraction Model

Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.

scholarly journals BSIRNet: A Road Extraction Network with Bidirectional Spatial Information Reasoning

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Hai Tan ◽  
Hao Xu ◽  
Jiguang Dai
Keyword(s):  
Spatial Information ◽  
Feature Fusion ◽  
Extraction Methods ◽  
Automatic Extraction ◽  
Processing Unit ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
The Public ◽  
Automatic Navigation ◽  
Neural Network Structure

Automatic extraction of road information from remote sensing images is widely used in many fields, such as urban planning and automatic navigation. However, due to interference from noise and occlusion, the existing road extraction methods can easily lead to road discontinuity. To solve this problem, a road extraction network with bidirectional spatial information reasoning (BSIRNet) is proposed, in which neighbourhood feature fusion is used to capture spatial context dependencies and expand the receptive field, and an information processing unit with a recurrent neural network structure is used to capture channel dependencies. BSIRNet enhances the connectivity of road information through spatial information reasoning. Using the public Massachusetts road dataset and Wuhan University road dataset, the superiority of the proposed method is verified by comparing its results with those of other models.

scholarly journals UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

2015 ◽  
Vol XL-1-W5 ◽  
pp. 411-416
Author(s):  
A. Kianisarkaleh ◽  
H. Ghassemian ◽  
F. Razzazi
Keyword(s):  
Feature Extraction ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Sample Selection ◽  
Extraction Methods ◽  
Hyperspectral Images ◽  
Training Samples ◽  
Samples Selection ◽  
Limited Training Samples

Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples exists. This paper illustrates the importance of selecting appropriate unlabeled samples that used in feature extraction methods. Also proposes a new method for unlabeled samples selection using spectral and spatial information. The proposed method has four parts including: PCA, prior classification, posterior classification and sample selection. As hyperspectral image passes these parts, selected unlabeled samples can be used in arbitrary feature extraction methods. The effectiveness of the proposed unlabeled selected samples in unsupervised and semisupervised feature extraction is demonstrated using two real hyperspectral datasets. Results show that through selecting appropriate unlabeled samples, the proposed method can improve the performance of feature extraction methods and increase classification accuracy.

scholarly journals Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2473
Author(s):  
Qinglie Yuan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Aidi Hizami Alias ◽  
Shaiful Jahari Hashim
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Spatial Information ◽  
Feature Fusion ◽  
Aerial Images ◽  
Semantic Gap ◽  
Superior Performance ◽  
Lidar Data ◽  
Building Extraction ◽  
Hierarchical Features

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.

scholarly journals Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

2018 ◽  
Vol 10 (11) ◽  
pp. 1827 ◽  
Author(s):  
Ahram Song ◽  
Jaewan Choi ◽  
Youkyung Han ◽  
Yongil Kim
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Spatial Information ◽  
Short Term Memory ◽  
Hyperspectral Images ◽  
Convolutional Network ◽  
Ground Truth Data ◽  
Fully Convolutional Network ◽  
Training Samples ◽  
Multi Temporal

Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.

scholarly journals Extended Function Analysis of Urban Planning and Design Based on Automatic Extraction Algorithm of Closed Area Boundary

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Xifeng Mi
Keyword(s):  
Functional Analysis ◽  
Urban Planning ◽  
Function Analysis ◽  
Social Economy ◽  
Automatic Extraction ◽  
Boundary Extraction ◽  
Closed Area ◽  
Planning And Design ◽  
Extraction Algorithm ◽  
Urban Planning And Design

With the continuous development of social economy, the expansion of cities often leads to the disorderly utilization of land resources and even waste. In view of these limitations and requirements, this paper introduces the automatic extraction algorithm of closed area boundary, combs the requirements of urban boundary extraction involved in urban planning and design, and uses the technology of geospatial analysis to carry out spatial analysis practice from three angles, so as to realize the expansion of functional analysis of urban planning and design and improve the efficiency and rationality of urban planning. The simulation results show that the automatic extraction algorithm of closed area boundary is effective and can support the functional analysis of urban planning and design expansion.

scholarly journals Mapping Cumulative Impacts of Human Activities on Marine Ecosystems

2018 ◽  
Author(s):  
SeaPlan
Keyword(s):  
Human Activities ◽  
Spatial Information ◽  
Human Impacts ◽  
Marine Ecosystems ◽  
Management Plan ◽  
Cumulative Impacts ◽  
Potential Conflict ◽  
Planning Decisions ◽  
Ocean Management ◽  
Survey Results

Given the diversity of human uses and natural resources that converge in coastal waters, the potential independent and cumulative impacts of those uses on marine ecosystems are important to consider during ocean planning. This study was designed to support the development and implementation of the 2009 Massachusetts Ocean Management Plan. Its goal was to estimate and visualize the cumulative impacts of human activities on coastal and marine ecosystems in the state and federal waters off of Massachusetts.For this study, regional ecosystem experts were surveyed to gauge the relative vulnerability of marine ecosystems to current and emerging anthropogenic stressors. Survey results were then combined with spatial information on the distribution of marine ecosystems and human stressors to map cumulative impacts in Massachusetts waters.The study resulted in an ecosystem vulnerability matrix and human impacts maps, which together yield insights into which ecosystems and places are most vulnerable and which human uses, alone and in combination, are putting the most stress on marine ecosystems. These products can be used in a number of ways, including to help clarify ocean planning decisions, identify areas of potential conflict among ocean users and areas that may merit conservation, and assess ecological, economic and social values of particular places.

Improved Locality Preserving Projection for Hyperspectral Image Classification in Probabilistic Framework

2021 ◽  
Author(s):  
Reza Seifi Majdar ◽  
Hassan Ghassemian
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Probability Distribution Function ◽  
Spatial Information ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Spectral Features ◽  
Probabilistic Framework ◽  
Locality Preserving ◽  
Training Samples

Unlabeled samples and transformation matrix are two main parts of unsupervised and semi-supervised feature extraction (FE) algorithms. In this manuscript, a semi-supervised FE method, locality preserving projection in the probabilistic framework (LPPPF), to find a sufficient number of reliable and unmixed unlabeled samples from all classes and constructing an optimal projection matrix is proposed. The LPPPF has two main steps. In the first step, a number of reliable unlabeled samples are selected based on the training samples, spectral features, and spatial information in the probabilistic framework. In this way, the spectral and spatial probability distribution function is calculated for each unlabeled sample. Therefore, the spectral features and spatial information are integrated together with a joint probability distribution function. Finally, a sufficient number of unlabeled samples with the highest joint probability distribution are selected. In the second step, the selected unlabeled samples are applied to construct the transformation matrix based on the spectral and spatial information of the unlabeled samples. The adjacency graph is improved by using new weights based on spectral and spatial information. This method is evaluated on three data sets: Indian Pines, Pavia University, and Kennedy Space Center (KSC) and compared with some recent and well-known supervised, semi-supervised, and unsupervised FE methods. Various experiments demonstrate the efficiency of the LPPPF in comparison with the other FE methods. LPPPF has also considerable performance with limited training samples.

A New Method for Detecting Altered Text in Document Images

2021 ◽  
pp. 2160010
Author(s):  
Lokesh Nandanwar ◽  
Palaiahnakote Shivakumara ◽  
Umapada Pal ◽  
Tong Lu ◽  
Daniel Lopresti ◽  
...  
Keyword(s):  
Spatial Information ◽  
Feature Vector ◽  
New Method ◽  
Document Images ◽  
Positive And Negative Coefficients ◽  
Digital Format ◽  
Fused Image ◽  
Fusion Operation ◽  
The Relationship ◽  
Text Images

As more and more office documents are captured, stored, and shared in digital format, and as image editing software are becoming increasingly more powerful, there is a growing concern about document authenticity. To prevent illicit activities, this paper presents a new method for detecting altered text in document images. The proposed method explores the relationship between positive and negative coefficients of DCT to extract the effect of distortions caused by tampering by fusing reconstructed images of respective positive and negative coefficients, which results in Positive-Negative DCT coefficients Fusion (PNDF). To take advantage of spatial information, we propose to fuse R, G, and B color channels of input images, which results in RGBF (RGB Fusion). Next, the same fusion operation is used for fusing PNDF and RGBF, which results in a fused image for the original input one. We compute a histogram to extract features from the fused image, which results in a feature vector. The feature vector is then fed to a deep neural network for classifying altered text images. The proposed method is tested on our own dataset and the standard datasets from the ICPR 2018 Fraud Contest, Altered Handwriting (AH), and faked IMEI number images. The results show that the proposed method is effective and the proposed method outperforms the existing methods irrespective of image type.

Automatic Extraction Algorithm of Seismic Landslide Information Based on After-Calamity High-Resolution Remote Sensing Image

2017 ◽  
Vol 54 (11) ◽  
pp. 112801
Author(s):  
闫 琦 Yan Qi ◽  
李 慧 Li Hui ◽  
荆林海 Jing Linhai ◽  
唐韵玮 Tang Yunwei ◽  
丁海峰 Ding Haifeng
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Automatic Extraction ◽  
Extraction Algorithm ◽  
Seismic Landslide
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