scholarly journals Petroleum Geological Analysis Based on Remote Sensing and laser scanning in Karamay Formation of Junggar Basin, China

2020 ◽  
Vol 206 ◽  
pp. 01023
Author(s):  
Qihong Zeng ◽  
Youyan Zhang ◽  
Linghua Kong ◽  
Yong Ye ◽  
Yan Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Precision ◽  
Laser Scanning ◽  
Laser Intensity ◽  
Junggar Basin ◽  
Research Area ◽  
Remote Sensing Images ◽  
Lithology Identification ◽  
Rock Lithology ◽  
Geological Analysis

This paper uses high-precision remote sensing and laser scanning to study petroleum geological analysis methods. The research area is Karamay Formation in Junggar Basin, China. Firstly, the outcrop lithologies are identified according to our clastic rock lithology identification pattern based on laser intensity, and the regional lithologies are identified based on high-precision remote sensing images. Furthermore, we analyze the horizontal and vertical distribution characteristics of the sandbodies. At last, we analyze the area sandbody connectivity and sandbody structure characteristics. These data can provide basic information for the analysis of underground reservoirs in Karamay Formation.

scholarly journals Research on Cultivated Land Change Based on GIS and High Resolution Remote Sensing Images

2018 ◽  
Vol 228 ◽  
pp. 02013
Author(s):  
Haibo Yu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Automatic Monitoring ◽  
Research Area ◽  
Decision Fusion ◽  
Land Change ◽  
Fuzzy Decision ◽  
Remote Sensing Images ◽  
Monitoring Method ◽  
Gis Data

This paper study an automatic monitoring method for land change based on high resolution remote sensing images and GIS data, and we use three classification methods to classify and fuse the research area. Secondly, the paper calculates the corresponding map class components and compares them with their historical attributes; it can automatically monitor land use change. The experimental results show that the fuzzy decision fusion classification can significantly improve the classification effect, and it can accurately determine the change area accurately and automatically. However, there are some partial errors in the region.

On Scale Correction Model of Coastline and its Application for Coastline Remote Sensing Monitoring

2013 ◽  
Vol 303-306 ◽  
pp. 734-739
Author(s):  
Hua Guo Zhang ◽  
Dong Ling Li ◽  
Ai Qin Shi
Keyword(s):  
Remote Sensing ◽  
High Precision ◽  
Coastal Zone ◽  
Absolute Error ◽  
Correction Method ◽  
Remote Sensing Images ◽  
Correction Model ◽  
Remote Sensing Monitoring ◽  
Multi Scale ◽  
Map Scale

This paper focuses on the scale correction of coastline extracted from remote sensing images. Measurement of coastline is one of the basic and core work of coastal zone remote sensing monitoring projects. Based on analysis of coastline scale effect and multi-scale simulation of coastline, a scale correction method is presented for remote sensing coastline. This method can be used to correct remote sensing coastline to specified map scale, in order to obtain high-precision remote sensing monitoring results of coastline. The results of application example showed that the absolute error of coastline length is reduced to about one third of the original error after correction using the presented method. So the presented method can increase the accuracy of remote sensing coastline for specified scale substantially.

scholarly journals An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (11) ◽  
pp. 2052
Author(s):  
Dongchuan Yan ◽  
Guoqing Li ◽  
Xiangqiang Li ◽  
Hao Zhang ◽  
Hua Lei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
High Precision ◽  
Large Scale ◽  
Dam Failure ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Tailings Pond ◽  
Tailings Ponds

Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management.

scholarly journals Application of satellite images and VNREDSAT-1 images in study on marine environment in Truong Sa region

2019 ◽  
Vol 19 (3B) ◽  
pp. 149-162
Author(s):  
Do Huy Cuong ◽  
Bui Thi Bao Anh ◽  
Nguyen Xuan Tung ◽  
Nguyen The Luan ◽  
Le Dinh Nam ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Marine Environment ◽  
Surface Current ◽  
Environmental Parameters ◽  
Research Area ◽  
Real Field ◽  
Remote Sensing Images ◽  
Field Surveys ◽  
Different Depths

The remote sensing images, including images of MODIS, VNREDSAT-1 and altimeter, are applied for researching marine environment with the different resolutions. On the basis of different time remote sensing images, we concentrated on the assessment of several characteristics including the SST, chlorophyll-a concentration and sea surface current at the different depths in different monsoons as well. With the large areas, we used the images of MODIS and altimeter. The detailed research area focuses on the Nam Yet island, and the images of VNREDSAT-1 are used. The analysis method of environmental parameters of SST and chlorophyll-a used the regression functions based on the single and combined bands to enhance the accuracy of the analysis result. The marine parameters collected at different depths in the latest field surveys on Truong Sa archipelago in the years of 2015 and 2018 are presented in this paper. On the basis of these parameters, we can analyse the relationships and compare the real field survey data and corresponding results interpreted from remote sensing images.

scholarly journals An Integrated Field and Hyperspectral Remote Sensing Method for the Estimation of Pigments Content of Stipa Purpurea in Shenzha, Tibet

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Bo Kong ◽  
Bing He ◽  
Huan Yu ◽  
Yu Liu
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Content ◽  
Photosynthetic Pigment ◽  
Research Area ◽  
Water Area ◽  
Remote Sensing Images ◽  
Estimation Model ◽  
Stipa Purpurea ◽  
Pigment Contents ◽  
The Mean

Stipa purpurea is the representative type of alpine grassland in Tibet and the surviving and development material for herdsmen. This paper takes Shenzha County as the research area. Based on the analysis of typical hyperspectral variables sensitive to chlorophyll content of Stipa purpurea, 10 spectral variables with significant correlation with chlorophyll were extracted. The estimation model of chlorophyll was established. The photosynthetic pigment contents in the Shenzha area were calculated by using HJ-1A remote sensing images. The results show that (1) there are significant correlations between chlorophyll content and spectral variables; in particular, the coefficient of Chlb in Stipa purpurea with RVI is the largest (0.728); (2) 10 variables are correlated with chlorophyll, and the order of correlation is Chlb > Chla > Chls; (3) for the estimation of Chla, the EVI is the best variable. RVI, NDVI, and VI2 are suitable for Chlb; RVI and NDVI are also suitable for the estimation of Chls; (4) the mean estimated content of Chla in Stipa bungeana is about 4.88 times that of Chlb, while Cars is slightly more than Chlb; (5) the distribution of Chla is opposite to Chlb and Chls content in water area.

scholarly journals Mapping of groundwater potentiality index parameters using remote sensing and GIS techniques in the southern mountain, Yogyakarta Special Region

2021 ◽  
Vol 930 (1) ◽  
pp. 012064
Author(s):  
H Hasibuan ◽  
A H Rafsanjani ◽  
D P E Putra ◽  
S S Surjono
Keyword(s):  
Remote Sensing ◽  
Research Area ◽  
Groundwater Potential ◽  
Remote Sensing Images ◽  
Digital Elevation ◽  
Distribution Parameters ◽  
Hydrogeological Map ◽  
Groundwater Potentiality ◽  
Elevation Model ◽  
Special Region

Abstract In the hydrogeological map sheet of the Special Region of Yogyakarta, the Mountain Zone is categorized as an area of scarce groundwater. This research is intended to determine the parameters of groundwater potential in the area of scarce groundwater according to the Groundwater Potentiality Index (GPI) methods, including; fractures, lithology, slope, topography, and rainfall. Fracture parameters, distribution, and topography were collected from the Indonesia Geospatial Portal and the Digital Elevation Model (DEM). The lithological parameters were obtained from data from the Geological Agency due to the Interpretation of Remote Sensing Images. Rainfall data for the last ten years was obtained from reports. Results show that most of the research area is a fairly massive rock area, and there are some local faults. The lithological parameters indicate that the research area is composed of breccias, sandstones, and tuffs. Distribution parameters obtained information that most distribution is notated river orders 1, 2, and 3 with several river orders notation 4, 5, and 6. The slope varies from <3% to> 65%, and the intensity of rainfall almost evenly ranges from 1600-2100 mm/year.

scholarly journals Hierarchical Multi-Label Object Detection Framework for Remote Sensing Images

2020 ◽  
Vol 12 (17) ◽  
pp. 2734
Author(s):  
Su-Jin Shin ◽  
Seyeob Kim ◽  
Youngjung Kim ◽  
Sungho Kim
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Complex Structure ◽  
Hierarchical Classification ◽  
Research Area ◽  
Visual Features ◽  
Remote Sensing Images ◽  
Efficient Detection ◽  
Fundamental Research ◽  
Label Object

Detecting objects such as aircraft and ships is a fundamental research area in remote sensing analytics. Owing to the prosperity and development of CNNs, many previous methodologies have been proposed for object detection within remote sensing images. Despite the advance, using the object detection datasets with a more complex structure, i.e., datasets with hierarchically multi-labeled objects, is limited to the existing detection models. Especially in remote sensing images, since objects are obtained from bird’s-eye view, the objects are captured with restricted visual features and not always guaranteed to be labeled up to fine categories. We propose a hierarchical multi-label object detection framework applicable to hierarchically partial-annotated datasets. In the framework, an object detection pipeline called Decoupled Hierarchical Classification Refinement (DHCR) fuses the results of two networks: (1) an object detection network with multiple classifiers, and (2) a hierarchical sibling classification network for supporting hierarchical multi-label classification. Our framework additionally introduces a region proposal method for efficient detection on vain areas of the remote sensing images, called clustering-guided cropping strategy. Thorough experiments validate the effectiveness of our framework on our own object detection datasets constructed with remote sensing images from WorldView-3 and SkySat satellites. Under our proposed framework, DHCR-based detections significantly improve the performance of respective baseline models and we achieve state-of-the-art results on the datasets.

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