scholarly journals A Study on Coastline Extraction and Its Trend Based on Remote Sensing Image Data Mining

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yun Zhang ◽  
Xueming Li ◽  
Jianli Zhang ◽  
Derui Song
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Prediction Method ◽  
Image Data ◽  
Chromatic Aberration ◽  
Grey Prediction ◽  
Remote Sensing Images ◽  
Canny Operator ◽  
Matching Algorithm ◽  
Edge Detection Method

In this paper, data mining theory is applied to carry out the field of the pretreatment of remote sensing images. These results show that it is an effective method for carrying out the pretreatment of low-precision remote sensing images by multisource image matching algorithm with SIFT operator, geometric correction on satellite images at scarce control points, and other techniques; the result of the coastline extracted by the edge detection method based on a chromatic aberration Canny operator has a height coincident with the actual measured result; we found that the coastline length of China is predicted to increase in the future by using the grey prediction method, with the total length reaching up to 19,471,983 m by 2015.

scholarly journals A Public Dataset for Fine-Grained Ship Classification in Optical Remote Sensing Images

2021 ◽  
Vol 13 (4) ◽  
pp. 747
Author(s):  
Yanghua Di ◽  
Zhiguo Jiang ◽  
Haopeng Zhang
Keyword(s):  
Remote Sensing ◽  
Image Data ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Visual Categorization ◽  
Class Differences ◽  
Fine Grained ◽  
Ship Classification

Fine-grained visual categorization (FGVC) is an important and challenging problem due to large intra-class differences and small inter-class differences caused by deformation, illumination, angles, etc. Although major advances have been achieved in natural images in the past few years due to the release of popular datasets such as the CUB-200-2011, Stanford Cars and Aircraft datasets, fine-grained ship classification in remote sensing images has been rarely studied because of relative scarcity of publicly available datasets. In this paper, we investigate a large amount of remote sensing image data of sea ships and determine most common 42 categories for fine-grained visual categorization. Based our previous DSCR dataset, a dataset for ship classification in remote sensing images, we collect more remote sensing images containing warships and civilian ships of various scales from Google Earth and other popular remote sensing image datasets including DOTA, HRSC2016, NWPU VHR-10, We call our dataset FGSCR-42, meaning a dataset for Fine-Grained Ship Classification in Remote sensing images with 42 categories. The whole dataset of FGSCR-42 contains 9320 images of most common types of ships. We evaluate popular object classification algorithms and fine-grained visual categorization algorithms to build a benchmark. Our FGSCR-42 dataset is publicly available at our webpages.

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.

scholarly journals MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 247-255 ◽  
Author(s):  
Y. Di ◽  
G. Jiang ◽  
L. Yan ◽  
H. Liu ◽  
S. Zheng
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Minimum Spanning Tree ◽  
Network Evolution ◽  
Edge Weight ◽  
Remote Sensing Images ◽  
Canny Operator ◽  
Multiple Features ◽  
Multi Scale ◽  
Initial Segmentation

Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA) on the accuracy and slightly inferior to FNEA on the efficiency.

scholarly journals U-net Network for Building Information Extraction of Remote-Sensing Imagery

2018 ◽  
Vol 14 (12) ◽  
pp. 179
Author(s):  
Jingtan Li ◽  
Maolin Xu ◽  
Hongling Xiu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Information Extraction ◽  
Image Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Building Information ◽  
The Face

With the resolution of remote sensing images is getting higher and higher, high-resolution remote sensing images are widely used in many areas. Among them, image information extraction is one of the basic applications of remote sensing images. In the face of massive high-resolution remote sensing image data, the traditional method of target recognition is difficult to cope with. Therefore, this paper proposes a remote sensing image extraction based on U-net network. Firstly, the U-net semantic segmentation network is used to train the training set, and the validation set is used to verify the training set at the same time, and finally the test set is used for testing. The experimental results show that U-net can be applied to the extraction of buildings.

Building Extraction from High-Resolution Remote Sensing Images Based on GrabCut with Automatic Selection of Foreground and Background Samples

2020 ◽  
Vol 86 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Ka Zhang ◽  
Hui Chen ◽  
Wen Xiao ◽  
Yehua Sheng ◽  
Dong Su ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Geodesic Distance ◽  
Extraction Methods ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Canny Operator ◽  
Contour Lines ◽  
Average Accuracy ◽  
Seed Points

This article proposes a new building extraction method from high-resolution remote sensing images, based on GrabCut, which can automatically select foreground and background samples under the constraints of building elevation contour lines. First the image is rotated according to the direction of pixel displacement calculated by the rational function Model. Second, the Canny operator, combined with morphology and the Hough transform, is used to extract the building's elevation contour lines. Third, seed points and interesting points of the building are selected under the constraint of the contour line and the geodesic distance. Then foreground and background samples are obtained according to these points. Fourth, GrabCut and geometric features are used to carry out image segmentation and extract buildings. Finally, WorldView satellite images are used to verify the proposed method. Experimental results show that the average accuracy can reach 86.34%, which is 15.12% higher than other building extraction methods.

scholarly journals Feature Extraction and Content Based Image Retrieval for High Resolution Remote Sensing Images

2019 ◽  
Vol 8 (3) ◽  
pp. 8881-8884
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Image Retrieval ◽  
Satellite Images ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Remote Sensing Images ◽  
Image Collection

These are the days where we are very rich in information and poor in data. This is very true in case of image data. Whether it is the case of normal images or satellite images, the image collection is very huge but utilizing those images is of least concern. Extracting features from big images is a very challenging and compute intensive task but if we realize it, it will be very fruitful. CBIR (Content Based Image Retrieval) when used with HRRS (High Resolution Remote Sensing) images will yield with effective data.

scholarly journals Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

2021 ◽  
Vol 14 (1) ◽  
pp. 103
Author(s):  
Dongchuan Yan ◽  
Hao Zhang ◽  
Guoqing Li ◽  
Xiangqiang Li ◽  
Hua Lei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
Transfer Learning ◽  
Near Infrared ◽  
Image Data ◽  
Remote Sensing Images ◽  
Tailings Pond ◽  
True Color ◽  
Tailings Ponds

The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models.

scholarly journals Remote Sensing Image Semantic Segmentation Based on Edge Information Guidance

2020 ◽  
Vol 12 (9) ◽  
pp. 1501
Author(s):  
Chu He ◽  
Shenglin Li ◽  
Dehui Xiong ◽  
Peizhang Fang ◽  
Mingsheng Liao
Keyword(s):  
Remote Sensing ◽  
Prior Knowledge ◽  
Image Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Segmentation Result ◽  
Rapid Progress ◽  
Remote Sensing Images ◽  
Edge Information ◽  
Important Field

Semantic segmentation is an important field for automatic processing of remote sensing image data. Existing algorithms based on Convolution Neural Network (CNN) have made rapid progress, especially the Fully Convolution Network (FCN). However, problems still exist when directly inputting remote sensing images to FCN because the segmentation result of FCN is not fine enough, and it lacks guidance for prior knowledge. To obtain more accurate segmentation results, this paper introduces edge information as prior knowledge into FCN to revise the segmentation results. Specifically, the Edge-FCN network is proposed in this paper, which uses the edge information detected by Holistically Nested Edge Detection (HED) network to correct the FCN segmentation results. The experiment results on ESAR dataset and GID dataset demonstrate the validity of Edge-FCN.

scholarly journals An Approach for Estimating Solar Photovoltaic Potential Based on Rooftop Retrieval from Remote Sensing Images

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3172 ◽  
Author(s):  
Xiaoyang Song ◽  
Yaohuan Huang ◽  
Chuanpeng Zhao ◽  
Yuxin Liu ◽  
Yanguo Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Solar Radiation ◽  
Solar Energy ◽  
Image Data ◽  
Surface Model ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Remote Sensing Images ◽  
Time Step ◽  
Pv Systems

Solar energy is the most clean renewable energy source and has good prospects for future sustainable development. Installation of solar photovoltaic (PV) systems on building rooftops has been the most widely applied method for using solar energy resources. In this study, we developed an approach to simulate the monthly and annual solar radiation on rooftops at an hourly time step to estimate the solar PV potential, based on rooftop feature retrieval from remote sensing images. The rooftop features included 2D rooftop outlines and 3D rooftop parameters retrieved from high-resolution remote sensing image data (obtained from Google Maps) and digital surface model (DSM, generated from the Pleiades satellite), respectively. We developed the building features calculation method for five rooftop types: flat rooftops, shed rooftops, hipped rooftops, gable rooftops and mansard rooftops. The parameters of the PV modules derived from the building features were then combined with solar radiation data to evaluate solar photovoltaic potential. The proposed method was applied in the Chao Yang District of Beijing, China. The results were that the number of rooftops available for PV systems was 743, the available rooftop area was 678,805 m2, and the annual PV electricity potential was 63.78 GWh/year in the study area, which has great solar PV potential. The method to perform precise calculation of specific rooftop solar PV potential developed in this study will guide the formulation of energy policy for solar PV in the future.

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