Study on Remote Sensing Faults Information Extraction in Eastern Kunlun, NW China

2013 ◽  
Vol 380-384 ◽  
pp. 3958-3961
Author(s):  
Xiao Hu Zhou
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Structural Information ◽  
Shear Zones ◽  
Remote Sensing Image ◽  
Extraction Methods ◽  
Orogenic Belt ◽  
Remote Sensing Images ◽  
Structure Information ◽  
Geological Body

Choosing the junction of Altun-Kunlun orogenic belt as the anatomical area of extracting complex texture and structure information from remote sensing images, make full use of multi-band remote sensing images to reflect the characteristics of the properties, to extract hidden information through image processing. Analyzing the structure elements by geological body, rock combination, linear and banded structure, penetrative and non-penetrative planar structure, folds, to carry out the surficial composition and structure research of the the junction of Altun-Kunlun orogenic belt, identifying different geological bodies, the fault zones, ductile shear zones, superimposed folds and different strain zones, the different types of foliation, clarifying the characteristics of multi-source remote sensing image from the angle of the image processing methods, proposing new remote sensing image extraction methods and recognition of structural information technology and new understanding of the regional geology.

A Fast Algorithm for Road Recognition in Remote Sensing Image

2010 ◽  
Vol 108-111 ◽  
pp. 1344-1347
Author(s):  
Li Li Li ◽  
Yong Xin Liu
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Fast Algorithm ◽  
Remote Sensing Image ◽  
Extraction Methods ◽  
Feature Integration ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
The Road ◽  
Simulation Results

In general, the road extraction methods in remote sensing images mainly are edge detection, feature integration, and so on. A fast road recognition arithmetic is presented in this paper. First using adaptive binarization arithmetic, the path on remote sensing images is extracted. Then morphological method is used to process image. Finally, the extracted image superimposed with the original and get clear road. Simulation results shows that this algorithm is efficiency, the anti-noise ability is enhance, and more precision.

scholarly journals Batch Processing of Remote Sensing Image Mosaic based on Python

2018 ◽  
Vol 14 (09) ◽  
pp. 208
Author(s):  
Hongling Xiu ◽  
Fengyun Yang
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Image Analysis ◽  
Remote Sensing Image ◽  
Image Mosaic ◽  
Remote Sensing Images ◽  
Complicated Process ◽  
Processing Software ◽  
Image Processing Software ◽  
Remote Sensing Image Processing

In the process of remote sensing image processing, analysis and interpretation, it is usually necessary to combine several local images into a complete image. Aiming at the shortcoming of long and complicated process of conventional semi-automatic video stitching. In this paper, using the splicing method of pixels, based on the Python interface of ArcGIS 10.1 platform, the idea of programming language is introduced and batch mosaic of remote sensing images is realized. Through the comparison with the image processing software, it is found that this method can shorten the time of image mosaic and improve the efficiency of splicing, which is convenient for later image analysis and other work under the premise of ensuring the accuracy.

scholarly journals Smooth Stitching Method for the Texture Seams of Remote Sensing Images Based on Gradient Structure Information

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1689
Author(s):  
Danjun Deng
Keyword(s):  
Remote Sensing ◽  
Design Method ◽  
Initial Point ◽  
Remote Sensing Image ◽  
Mean Value ◽  
Gradient Structure ◽  
Feature Points ◽  
Remote Sensing Images ◽  
Shape Distortion ◽  
Structure Information

Traditional smooth stitching method for the texture seams of remote sensing images is affected by gradient structure information, leading to poor stitching effect. Therefore, a smooth stitching method for the texture seams of remote sensing images based on gradient structure information is proposed in this research. By matching the feature points of remote sensing images and introducing a block link constraint and shape distortion constraint, the modified stitching image is obtained. By using remote sensing image fusion, the smooth stitching image of texture seams is obtained, and the local overlapping area of the texture is optimized. The main direction of texture seams is determined by calculating the gradient structure information of texture seams in horizontal and vertical directions. By selecting the initial point, the optimal stitching line is extracted by using the minimum mean value of the cumulative error of the smooth stitching line. By using the method of boundary correlation constraints, matching the feature points of the texture seams of remote sensing images and selecting the best matching pair, a smooth stitching algorithm for the texture seams of remote sensing image is designed, which realizes the smooth stitching of the texture seams of remote sensing images. Experimental results show that the design method has good performance in stitching accuracy and efficiency in the smooth stitching of the texture seams of remote sensing images. Specifically, the Liu et al. and the Zhang et al. methods that are the benchmark studies in the literature are introduced as a comparison, and the stitching experiment is carried out. The test is carried out according to accuracy and time and the proposed method achieves better results by almost 25%.

scholarly journals EXTRACTION OF RIVERS AND LAKES ON TIBETAN PLATEAU BASED ON GOOGLE EARTH ENGINE

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1797-1801
Author(s):  
R. G. Xu ◽  
G. Qiao ◽  
Y. J. Wu ◽  
Y. J. Cao
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Remote Sensing Images ◽  
Yarlung Zangbo River ◽  
Rivers And Lakes ◽  
Google Earth Engine ◽  
Remote Sensing Image Processing ◽  
Hydrological Station

<p><strong>Abstract.</strong> Tibetan Plateau (TP) is the most abundant area of water resources and water energy resources in China. It is also the birthplace of the main rivers in Southeast Asia and plays an important strategic role. However, due to its remote location and complex topography, the observation of surface hydrometeorological elements is extremely scarce, which seriously restricts the understanding of the water cycle in this area. Using remote sensing images to extract rivers and lakes on TP can obtain a lot of valuable water resources information. However, the downloading and processing of remote sensing images is very time-consuming, especially the processing of remote sensing images with large-scale and long time series often involves hundreds of gigabytes of data, which requires a high level of personal computers and is inefficient. As a cloud platform dedicated to data processing and analysis of geoscience, Google Earth Engine(GEE) integrates many excellent remote sensing image processing algorithms. It does not need to download images and supports online remote sensing image processing, which greatly improves the output efficiency. Based on GEE, the monthly data of Yarlung Zangbo River at Nuxia Hydrological Station and the annual data of typical lakes were extracted and vectorized from the pre-processed Landsat series images. It was found that the area of Yarlung Zangbo River at Nuxia Hydrological Station varies periodically. The changing trend of typical lakes is also revealed.</p>

scholarly journals Editorial for the Special Issue “New Advances on Sub-Pixel Processing: Unmixing and Mapping Methods”

2021 ◽  
Vol 13 (19) ◽  
pp. 3807
Author(s):  
Addisson Salazar ◽  
Luis Vergara ◽  
Gonzalo Safont
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Data Analysis ◽  
Remote Sensing Image ◽  
Computational Power ◽  
Special Issue ◽  
Remote Sensing Images ◽  
Image Processing Techniques ◽  
Remote Sensing Image Processing ◽  
Processing Techniques

Innovative remote sensing image processing techniques have been progressively studied due to the increasing availability of remote sensing images, powerful techniques of data analysis, and computational power [...]

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

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