A Survey on Image Processing for Hyperspectral and Remote Sensing Images

2020 ◽  
pp. 27-51
Author(s):  
Alfonso Ramos-Michel ◽  
Marco Pérez-Cisneros ◽  
Erik Cuevas ◽  
Daniel Zaldivar
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Remote Sensing Images

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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.

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 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>

A Review on Segmentation Techniques in Large-Scale Remote Sensing Images

2018 ◽  
Vol 4 (4) ◽  
pp. 7
Author(s):  
Rakesh Tripathi ◽  
Neelesh Gupta
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Image Segmentation ◽  
Large Scale ◽  
Remote Sensing Images ◽  
Processing Step ◽  
Multiple Regions ◽  
Important Processing ◽  
Basic Image ◽  
Computer Vision Applications

Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Various algorithms for automating the segmentation process have been proposed, tested and evaluated to find the most ideal algorithm to be used for different types of images. In this paper a review of basic image segmentation techniques of satellite images is presented.

scholarly journals THE LAND-USE AND LAND-COVER CHANGE ANALYSIS IN BEIJING HUAIROU IN LAST TEN YEARS

2018 ◽  
Vol XLII-3 ◽  
pp. 2391-2394
Author(s):  
Q. Zhao ◽  
G. Liu ◽  
J. Tu ◽  
Z. Wang
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Land Use ◽  
Land Cover Change ◽  
Object Oriented ◽  
Remote Sensing Images ◽  
Change Analysis ◽  
The Past ◽  
Land Use Types ◽  
Land Cover Change Analysis

With eCognition software, the sample-based object-oriented classification method is used. Remote sensing images in Huairou district of Beijing had been classified using remote sensing images of last ten years. According to the results of image processing, the land use types in Huairou district of Beijing were analyzed in the past ten years, and the changes of land use types in Huairou district were obtained, and the reasons for its occurrence were analyzed.

scholarly journals A HADOOP-BASED DISTRIBUTED FRAMEWORK FOR EFFICIENT MANAGING AND PROCESSING BIG REMOTE SENSING IMAGES

2015 ◽  
Vol II-4/W2 ◽  
pp. 63-66 ◽  
Author(s):  
C. Wang ◽  
F. Hu ◽  
X. Hu ◽  
S. Zhao ◽  
W. Wen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Big Data ◽  
Remote Sensing Data ◽  
Distributed File System ◽  
Computing Environment ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Distributed Framework ◽  
Hadoop Distributed File System

Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.

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 [...]

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