scholarly journals The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

2021 ◽  
Author(s):  
Leijin Long ◽  
Feng He ◽  
Hongjiang Liu
Keyword(s):  
Remote Sensing ◽  
Southwest China ◽  
Influence Factors ◽  
Classification Error ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Jinsha River ◽  
Detection Model ◽  
High Level

AbstractIn order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in mountainous areas, the data of satellite remote sensing images are combined with various factors inducing landslides and transformed into landslide influence factors, which provides data basis for the establishment of landslide detection model. Then, based on the deep belief networks (DBN) and convolutional neural network (CNN) algorithm, two landslide detection models DBN and convolutional neural-deep belief network (CDN) are established to monitor the high-level landslide in Jinsha River. The influence of the model parameters on the landslide detection results is analyzed, and the accuracy of DBN and CDN models in dealing with actual landslide problems is compared. The results show that when the number of neurons in the DBN is 100, the overall error is the minimum, and when the number of learning layers is 3, the classification error is the minimum. The detection accuracy of DBN and CDN is 97.56% and 97.63%, respectively, which indicates that both DBN and CDN models are feasible in dealing with landslides from remote sensing images. This exploration provides a reference for the study of high-level landslide disasters in Jinsha River.

scholarly journals A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China

2020 ◽  
Vol 12 (14) ◽  
pp. 2334
Author(s):  
Lu Zhao ◽  
Hongyan Ren ◽  
Cheng Cui ◽  
Yaohuan Huang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Uv Detection ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Detection Model ◽  
Model Object ◽  
Object Based ◽  
Similar Area ◽  
Urban Villages

High-resolution remotely sensed imageries have been widely employed to detect urban villages (UVs) in highly urbanized regions, especially in developing countries. However, the understanding of the potential impacts of spatially and temporally differentiated urban internal development on UV detection is still limited. In this study, a partition-strategy-based framework integrating the random forest (RF) model, object-based image analysis (OBIA) method, and high-resolution remote sensing images was proposed for the UV-detection model. In the core regions of Guangzhou, four original districts were re-divided into five new zones for the subsequent object-based RF-detection of UVs with a series features, according to the different proportion of construction lands. The results show that the proposed framework has a good performance on UV detection with an average overall accuracy of 90.23% and a kappa coefficient of 0.8. It also shows the possibility of transferring samples and models into a similar area. In summary, the partition strategy is a potential solution for the improvement of the UV-detection accuracy through high-resolution remote sensing images in Guangzhou. We suggest that the spatiotemporal process of urban construction land expansion should be comprehensively understood so as to ensure an efficient UV-detection in highly urbanized regions. This study can provide some meaningful clues for city managers identifying the UVs efficiently before devising and implementing their urban planning in the future.

scholarly journals Cascade Convolutional Neural Network Based on Transfer-Learning for Aircraft Detection on High-Resolution Remote Sensing Images

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Bin Pan ◽  
Jianhao Tai ◽  
Qi Zheng ◽  
Shanshan Zhao
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Transfer Learning ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Large Area ◽  
Detection Model ◽  
Aircraft Detection

Aircraft detection from high-resolution remote sensing images is important for civil and military applications. Recently, detection methods based on deep learning have rapidly advanced. However, they require numerous samples to train the detection model and cannot be directly used to efficiently handle large-area remote sensing images. A weakly supervised learning method (WSLM) can detect a target with few samples. However, it cannot extract an adequate number of features, and the detection accuracy requires improvement. We propose a cascade convolutional neural network (CCNN) framework based on transfer-learning and geometric feature constraints (GFC) for aircraft detection. It achieves high accuracy and efficient detection with relatively few samples. A high-accuracy detection model is first obtained using transfer-learning to fine-tune pretrained models with few samples. Then, a GFC region proposal filtering method improves detection efficiency. The CCNN framework completes the aircraft detection for large-area remote sensing images. The framework first-level network is an image classifier, which filters the entire image, excluding most areas with no aircraft. The second-level network is an object detector, which rapidly detects aircraft from the first-level network output. Compared with WSLM, detection accuracy increased by 3.66%, false detection decreased by 64%, and missed detection decreased by 23.1%.

scholarly journals Earthquake-Damaged Buildings Detection in Very High-Resolution Remote Sensing Images Based on Object Context and Boundary Enhanced Loss

2021 ◽  
Vol 13 (16) ◽  
pp. 3119
Author(s):  
Chao Wang ◽  
Xing Qiu ◽  
Hai Huan ◽  
Shuai Wang ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Feature Representation ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Benchmark Datasets ◽  
High Level ◽  
Very High

Fully convolutional networks (FCN) such as UNet and DeepLabv3+ are highly competitive when being applied in the detection of earthquake-damaged buildings in very high-resolution (VHR) remote sensing images. However, existing methods show some drawbacks, including incomplete extraction of different sizes of buildings and inaccurate boundary prediction. It is attributed to a deficiency in the global context-aware and inaccurate correlation mining in the spatial context as well as failure to consider the relative positional relationship between pixels and boundaries. Hence, a detection method for earthquake-damaged buildings based on the object contextual representations (OCR) and boundary enhanced loss (BE loss) was proposed. At first, the OCR module was separately embedded into high-level feature extractions of the two networks DeepLabv3+ and UNet in order to enhance the feature representation; in addition, a novel loss function, that is, BE loss, was designed according to the distance between the pixels and boundaries to force the networks to pay more attention to the learning of the boundary pixels. Finally, two improved networks (including OB-DeepLabv3+ and OB-UNet) were established according to the two strategies. To verify the performance of the proposed method, two benchmark datasets (including YSH and HTI) for detecting earthquake-damaged buildings were constructed according to the post-earthquake images in China and Haiti in 2010, respectively. The experimental results show that both the embedment of the OCR module and application of BE loss contribute to significantly increasing the detection accuracy of earthquake-damaged buildings and the two proposed networks are feasible and effective.

Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

2021 ◽  
Vol 10 (7) ◽  
pp. 488
Author(s):  
Peng Li ◽  
Dezheng Zhang ◽  
Aziguli Wulamu ◽  
Xin Liu ◽  
Peng Chen
Keyword(s):  
Remote Sensing ◽  
Scene Understanding ◽  
Deep Understanding ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Scene Graph ◽  
Multi Scale ◽  
Relationship Extraction ◽  
High Level ◽  
Graph Generation

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.

scholarly journals Non-linear Sorenson–Dice Exemplar Image Inpainting Based Bayes Probability for Occlusion Removal in Remote Traffic Control

2021 ◽  
Author(s):  
P. L. Arun ◽  
R Mathusoothana S Kumar
Keyword(s):  
Remote Sensing ◽  
Conditional Probability ◽  
Traffic Control ◽  
Road Safety ◽  
Image Inpainting ◽  
Computational Time ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Non Linear ◽  
Occlusion Removal

AbstractOcclusion removal is a significant problem to be resolved in a remote traffic control system to enhance road safety. However, the conventional techniques do not recognize traffic signs well due to the vehicles are occluded. Besides occlusion removal was not performed in existing techniques with a less amount of time. In order to overcome such limitations, Non-linear Gaussian Bilateral Filtered Sorenson–Dice Exemplar Image Inpainting Based Bayes Conditional Probability (NGBFSEII-BCP) Method is proposed. Initially, a number of remote sensing images are taken as input from Highway Traffic Dataset. Then, the NGBFSEII-BCP method applies the Non-Linear Gaussian Bilateral Filtering (NGBF) algorithm for removing the noise pixels in input images. After preprocessing, the NGBFSEII-BCP method is used to remove the occlusion in the input images. Finally, NGBFSEII-BCP Method applies Bayes conditional probability to find operation status and thereby gets higher road safety using remote sensing images. The technique conducts the simulation evaluation using metrics such as peak signal to noise ratio, computational time, and detection accuracy. The simulation result illustrates that the NGBFSEII-BCP Method increases the detection accuracy by 20% and reduces the computation time by 32% as compared to state-of-the-art works.

Unsupervised labelling of remote sensing images based on force field clustering

2021 ◽  
pp. 1-14
Author(s):  
Zhenggang Wang ◽  
Jin Jin
Keyword(s):  
Remote Sensing ◽  
Force Field ◽  
Image Data ◽  
Model Parameters ◽  
Remote Sensing Images ◽  
Initial Cluster ◽  
Data Points ◽  
Global Optimal ◽  
Density Force

Remote sensing image segmentation provides technical support for decision making in many areas of environmental resource management. But, the quality of the remote sensing images obtained from different channels can vary considerably, and manually labeling a mass amount of image data is too expensive and Inefficiently. In this paper, we propose a point density force field clustering (PDFC) process. According to the spectral information from different ground objects, remote sensing superpixel points are divided into core and edge data points. The differences in the densities of core data points are used to form the local peak. The center of the initial cluster can be determined by the weighted density and position of the local peak. An iterative nebular clustering process is used to obtain the result, and a proposed new objective function is used to optimize the model parameters automatically to obtain the global optimal clustering solution. The proposed algorithm can cluster the area of different ground objects in remote sensing images automatically, and these categories are then labeled by humans simply.

Texture Image Segmentation Based on MRMRF in Contourlet Domain

2012 ◽  
Vol 532-533 ◽  
pp. 732-737
Author(s):  
Xi Jie Wang ◽  
Xiao Fan Zhao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Field Model ◽  
Model Parameters ◽  
Texture Image ◽  
Remote Sensing Images ◽  
Unsupervised Segmentation ◽  
Markov Random Field Model ◽  
Markov Random ◽  
Texture Image Segmentation

This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.

scholarly journals Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhaoli Wu ◽  
Xin Wang ◽  
Chao Chen
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Pedestrian Detection ◽  
Infrared Image ◽  
Far Infrared ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Detection Model ◽  
Embedded Platform

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

scholarly journals Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

2020 ◽  
Vol 9 (4) ◽  
pp. 256 ◽  
Author(s):  
Liguo Weng ◽  
Yiming Xu ◽  
Min Xia ◽  
Yonghong Zhang ◽  
Jia Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Spatial Information ◽  
Global Climate ◽  
Semantic Segmentation ◽  
Water Area ◽  
Remote Sensing Images ◽  
The One ◽  
High Level ◽  
Almost All

Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet.

scholarly journals Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Liang Huang ◽  
Qiuzhi Peng ◽  
Xueqin Yu
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Detection Method ◽  
Saliency Detection ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Saliency Maps ◽  
Multitemporal Remote Sensing

In order to improve the change detection accuracy of multitemporal high spatial resolution remote-sensing (HSRRS) images, a change detection method of multitemporal remote-sensing images based on saliency detection and spatial intuitionistic fuzzy C-means (SIFCM) clustering is proposed. Firstly, the cluster-based saliency cue method is used to obtain the saliency maps of two temporal remote-sensing images; then, the saliency difference is obtained by subtracting the saliency maps of two temporal remote-sensing images; finally, the SIFCM clustering algorithm is used to classify the saliency difference image to obtain the change regions and unchange regions. Two data sets of multitemporal high spatial resolution remote-sensing images are selected as the experimental data. The detection accuracy of the proposed method is 96.17% and 97.89%. The results show that the proposed method is a feasible and better performance multitemporal remote-sensing image change detection method.

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