Design of Remote Sensing Image Data Analysis and Processing Platform Based on Environmental Monitoring

Abstract In the continuous innovation of modern technology concept, remote sensing technology as an advanced and practical comprehensive detection technology has been widely used in many fields. Especially for environmental monitoring, the rational use of remote sensing image data analysis and processing platform can not only obtain valuable environmental information, but also provide effective management decisions for climate changeable natural disasters and other issues. Therefore, on the basis of understanding the design scheme of remote sensing image data analysis and processing platform system, this paper makes clear the positive role of remote sensing image processing technology in the development of environmental monitoring based on the application of the platform.

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Hadoop-Based Painting Resource Storage and Retrieval Platform Construction and Testing

Complexity ◽

10.1155/2021/9933330 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Chenhua Zu

Keyword(s):

Remote Sensing ◽

Data Analysis ◽

Data Storage ◽

Image Data ◽

Remote Sensing Image ◽

Development Environment ◽

Log Data ◽

Storage And Retrieval ◽

Web Text Mining ◽

Log Data Analysis

This paper adopts Hadoop to build and test the storage and retrieval platform for painting resources. This paper adopts Hadoop as the platform and MapReduce as the computing framework and uses Hadoop Distributed Filesystem (HDFS) distributed file system to store massive log data, which solves the storage problem of massive data. According to the business requirements of the system, this paper designs the system according to the process of web text mining, mainly divided into log data preprocessing module, log data storage module, log data analysis module, and log data visualization module. The core part of the system is the log data analysis module. The analysis of search keywords ranking, Uniform Resource Locator (URL), and user click relationship, URL ranking, and other dimensions are realized through data statistical analysis, and Canopy coarse clustering is performed first according to search keywords, and then K-means clustering is used for the results after Canopy clustering, and the calculation of cosine similarity is adopted to realize the grouping of users and build user portrait. The Hadoop development environment is installed and deployed, and functional and performance tests are conducted on the contents implemented in this system. The constructed private cloud platform for remote sensing image data can realize online retrieval of remote sensing image metadata and fast download of remote sensing image data and solve the problems in storage, data sharing, and management of remote sensing image data to a certain extent.

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Noise estimation for remote sensing image data analysis

10.1117/12.508101 ◽

2004 ◽

Cited By ~ 1

Author(s):

Qian Du

Keyword(s):

Remote Sensing ◽

Data Analysis ◽

Image Data ◽

Remote Sensing Image ◽

Noise Estimation

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Archaeological Surveying of Subarctic and Arctic Landscapes: Comparing the Performance of Airborne Laser Scanning and Remote Sensing Image Data

Sustainability ◽

10.3390/su13041917 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1917

Author(s):

Alma Elizabeth Thuestad ◽

Ole Risbøl ◽

Jan Ingolf Kleppe ◽

Stine Barlindhaug ◽

Elin Rose Myrvoll

Keyword(s):

Remote Sensing ◽

Laser Scanning ◽

Digital Terrain Model ◽

Image Data ◽

Remote Sensing Image ◽

Airborne Laser Scanning ◽

Aerial Images ◽

Detection Rates ◽

Airborne Laser ◽

Pros And Cons

What can remote sensing contribute to archaeological surveying in subarctic and arctic landscapes? The pros and cons of remote sensing data vary as do areas of utilization and methodological approaches. We assessed the applicability of remote sensing for archaeological surveying of northern landscapes using airborne laser scanning (LiDAR) and satellite and aerial images to map archaeological features as a basis for (a) assessing the pros and cons of the different approaches and (b) assessing the potential detection rate of remote sensing. Interpretation of images and a LiDAR-based bare-earth digital terrain model (DTM) was based on visual analyses aided by processing and visualizing techniques. 368 features were identified in the aerial images, 437 in the satellite images and 1186 in the DTM. LiDAR yielded the better result, especially for hunting pits. Image data proved suitable for dwellings and settlement sites. Feature characteristics proved a key factor for detectability, both in LiDAR and image data. This study has shown that LiDAR and remote sensing image data are highly applicable for archaeological surveying in northern landscapes. It showed that a multi-sensor approach contributes to high detection rates. Our results have improved the inventory of archaeological sites in a non-destructive and minimally invasive manner.

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

Remote Sensing ◽

10.3390/rs13040747 ◽

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.

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