scholarly journals AN APPROACH FOR EVALUATING THE INFORMATION CONTENT OF REMOTE SENSING IMAGES

2019 ◽  
Vol XLII-4/W20 ◽  
pp. 21-26
Author(s):  
S. M. Fang ◽  
X. G. Zhou
Keyword(s):  
Remote Sensing ◽  
Information Content ◽  
Evaluation Method ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Image Data ◽  
Structural Features ◽  
Utilization Efficiency ◽  
Sensing Data

Abstract. Due to being affected by the rapid development of open science and the increasing popularity of mobile devices (e.g., smartphones), remote sensing data as frequently used data sources are broadly applied to our daily life. At the same time, remote sensing data collection also presents a trend of popularization. To improve the utilization efficiency and availability of the obtained diversified remote sensing data, we propose a novel evaluation method based on information theory and scatterplot mapping model, i.e., geometrical mapping entropy (GME). The goal is to construct a unified model of measurement to be much more effectively and accurately evaluate the information content and quality of remotely sensed imagery. Different experimental data are used to verify the performance of the proposed method, i.e., a group of the dataset that contains different four types of images; the other group of image data contains the images with different modalities and different imaging times (2016–05, 2017–08, 2018–04, and 2018–06). Experimental results indicate that the proposed approach can better characterize the spectrum features and spatial structural features contained in images and visual perception information. Additionally, it can also reflect the difference in the quality of different modality images, especially the effect for the images that contain clouds or poor lighting conditions, is better.

scholarly journals A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB

2019 ◽  
Vol 8 (12) ◽  
pp. 533 ◽  
Author(s):  
Shuang Wang ◽  
Guoqing Li ◽  
Xiaochuang Yao ◽  
Yi Zeng ◽  
Lushen Pang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Relational Databases ◽  
Large Scale ◽  
Distributed Storage ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Image Data ◽  
Sensing Data ◽  
Cluster Architecture ◽  
Distributed Cluster

With the rapid development of earth-observation technology, the amount of remote sensing data has increased exponentially, and traditional relational databases cannot satisfy the requirements of managing large-scale remote sensing data. To address this problem, this paper undertakes intensive research of the NoSQL (Not Only SQL) data management model, especially the MongoDB database, and proposes a new approach to managing large-scale remote sensing data. Firstly, based on the sharding technology of MongoDB, a distributed cluster architecture was designed and established for massive remote sensing data. Secondly, for the convenience in the unified management of remote sensing data, an archiving model was constructed, and remote sensing data, including structured metadata and unstructured image data, were stored in the above cluster separately, with the metadata stored in the form of a document, and image data stored with the GridFS mechanism. Finally, by designing different shard strategies and comparing MongoDB cluster with a typical relational database, several groups of experiments were conducted to verify the storage performance and access performance of the cluster. The experimental results show that the proposed method can overcome the deficiencies of traditional methods, as well as scale out the database, which is more suitable for managing massive remote sensing data and can provide technical support for the management of massive remote sensing data.

Valuing Radiometric Quality of Remote Sensing Data for Decisions

2021 ◽  
Author(s):  
Afreen Siddiqi ◽  
Sheila Baber ◽  
Olivier De Weck
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Toward a Standardized Encoding of Remote Sensing Geo-Positioning Sensor Models

2020 ◽  
Vol 12 (9) ◽  
pp. 1530
Author(s):  
Meng Jin ◽  
Yuqi Bai ◽  
Emmanuel Devys ◽  
Liping Di
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Image Data ◽  
Xml Schema ◽  
Data Interoperability ◽  
Sensing Data ◽  
Sensor Model ◽  
Application Systems ◽  
Extensible Markup ◽  
Sensor Models

Geolocation information is an important feature of remote sensing image data that is captured through a variety of passive or active observation sensors, such as push-broom electro-optical sensor, synthetic aperture radar (SAR), light detection and ranging (LIDAR) and sound navigation and ranging (SONAR). As a fundamental processing step to locate an image, geo-positioning is used to determine the ground coordinates of an object from image coordinates. A variety of sensor models have been created to describe geo-positioning process. In particular, Open Geospatial Consortium (OGC) has defined the Sensor Model Language (SensorML) specification in its Sensor Web Enablement (SWE) initiative to describe sensors including the geo-positioning process. It has been realized using syntax from the extensible markup language (XML). Besides, two standards defined by the International Organization for Standardization (ISO), ISO 19130-1 and ISO 19130-2, introduced a physical sensor model, a true replacement model, and a correspondence model for the geo-positioning process. However, a standardized encoding for geo-positioning sensor models is still missing for the remote sensing community. Thus, the interoperability of remote sensing data between application systems cannot be ensured. In this paper, a standardized encoding of remote sensing geo-positioning sensor models is introduced. It is semantically based on ISO 19130-1 and ISO 19130-2, and syntactically based on OGC SensorML. It defines a cross mapping of the sensor models defined in ISO 19130-1 and ISO 19130-2 to the SensorML, and then proposes a detailed encoding method to finalize the XML schema (an XML schema here is the structure to define an XML document), which will become a profile of OGC SensorML. It seamlessly unifies the sensor models defined in ISO 19130-1, ISO 19130-2, and OGC SensorML. By enabling a standardized description of sensor models used to produce remote sensing data, this standard is very promising in promoting data interoperability, mobility, and integration in the remote sensing domain.

The Evaluation and Application Study of Remote Sensing Data Quality of Environment and Disaster Monitoring

2012 ◽  
Vol 573-574 ◽  
pp. 271-276
Author(s):  
Ping Ren ◽  
Jie Ming Zhou
Keyword(s):  
Remote Sensing ◽  
Data Quality ◽  
Remote Sensing Data ◽  
Resolution Time ◽  
Application Study ◽  
Space Resolution ◽  
Sensing Data ◽  
The Earth ◽  
Disaster Monitoring

The existing Fengyun (FY) satellites, resource satellites and ocean satellites all can observe the earth muti-funtionally and work well in monitoring environment and disasters. However, all these satellites are insufficient for space resolution, time resolution, spectral resolution and all-weather requirements when facing complicated environmental problems and natural disasters. This paper evaluates the multi-spectral remote sensing data quality of the Environment and Disasters Monitoring Micro-satellite Constellation (HJ-1A/B)A/B satellite and extracts data characteristics to offer references for promotion and application this data.

scholarly journals Evaluating the quality of remote sensing products for agricultural index insurance

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258215
Author(s):  
Benson K. Kenduiywo ◽  
Michael R. Carter ◽  
Aniruddha Ghosh ◽  
Robert J. Hijmans
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Well Being ◽  
Insurance Contract ◽  
Index Insurance ◽  
Sensing Data ◽  
Insurance Contracts ◽  
The Impact ◽  
Economic Well Being

Agricultural index insurance contracts increasingly use remote sensing data to estimate losses and determine indemnity payouts. Index insurance contracts inevitably make errors, failing to detect losses that occur and issuing payments when no losses occur. The quality of these contracts and the indices on which they are based, need to be evaluated to assess their fitness as insurance, and to provide a guide to choosing the index that best protects the insured. In the remote sensing literature, indices are often evaluated with generic model evaluation statistics such as R2 or Root Mean Square Error that do not directly consider the effect of errors on the quality of the insurance contract. Economic analysis suggests using measures that capture the impact of insurance on the expected economic well-being of the insured. To bridge the gap between the remote sensing and economic perspectives, we adopt a standard economic measure of expected well-being and transform it into a Relative Insurance Benefit (RIB) metric. RIB expresses the welfare benefits derived from an index insurance contract relative to a hypothetical contract that perfectly measures losses. RIB takes on its maximal value of one when the index contract offers the same economic benefits as the perfect contract. When it achieves none of the benefits of insurance it takes on a value of zero, and becomes negative if the contract leaves the insured worse off than having no insurance. Part of our contribution is to decompose this economic well-being measure into an asymmetric loss function. We also argue that the expected well-being measure we use has advantages over other economic measures for the normative purpose of insurance quality ascertainment. Finally, we illustrate the use of the RIB measure with a case study of potential livestock insurance contracts in Northern Kenya. We compared 24 indices that were made with 4 different statistical models and 3 remote sensing data sources. RIB for these indices ranged from 0.09 to 0.5, and R2 ranged from 0.2 to 0.51. While RIB and R2 were correlated, the model with the highest RIB did not have the highest R2. Our findings suggest that, when designing and evaluating an index insurance program, it is useful to separately consider the quality of a remote sensing-based index with a metric like the RIB instead of a generic goodness-of-fit metric.

scholarly journals Visual characteristics of landscapes and methods for their assessment on GIS (Belogradchik rocks (Bulgaria) as an example)

2021 ◽  
Vol 27 (2) ◽  
pp. 191-204
Author(s):  
Elina Sheremet ◽  
Natalia Kalutskova ◽  
Vladimir Dekhnich
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Visual Quality ◽  
Aerial Photographs ◽  
Main Task ◽  
Field Observations ◽  
North West ◽  
Sensing Data ◽  
Visual Characteristics

Visual characteristics of landscapes are important factors for the assessment of tourist and recreational potential of territories. At present, a number of methodological approaches are applied to assess the visual characteristics of landscapes. They can be divided into traditional, associated exclusively with field research, and innovative, which is based on remote sensing data (RSD) of high spatial resolution and GIS technologies. Field assessment of the visual quality of landscapes utilizes a system of numerous elementary indicators to minimize subjectivity of assessment. They are conducted within separate areas or touristic routes. In its turn, modern GIS and high quality of remote sensing data allow assessing of most indicators of the visual quality of landscapes for any observation point on the entire territory. The main task of our research is to verify the results of automated processing of ultra-high resolution aerial photographs obtained from unmanned aerial vehicles (UAV) by field observations on a touristic route. The research was carried out on the territory of the “Belogradchik Rocks” Geopark (North-West Bulgaria). In our study, we estimated 4 out of 28 aesthetic indicators—the amount of mountain peaks visible from a site, the amount of mountain peaks on the skyline, the percentage of the forest-covered area, and the amount of open spaces in the wooded landscape. The obtained results confirmed that our approach allows calculating these aesthetic indicators at an accuracy level comparable to field observations.

scholarly journals A Precision Evaluation Method for Remote Sensing Data Sampling Based on Hexagon Discrete Grid

2020 ◽  
Author(s):  
Yue Ma ◽  
Guoqing Li ◽  
Xiaochuang Yao ◽  
Jin Ben ◽  
Qianqian Cao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Evaluation System ◽  
Evaluation Method ◽  
Remote Sensing Data ◽  
Accuracy Evaluation ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Hexagonal Grids ◽  
Discrete Grid

With the rapid development of earth observation, satellite navigation, mobile communication and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. Under this circumstance, a new form of spatial data organization emerged-the global discrete grid. This form of data management can be used for the efficient storage and application of large-scale global spatial data, which is a digital multi-resolution the geo-reference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing and application of current spatial data. There are different types of grid system according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, and establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves that the availability hexagonal grid-based remote sensing data of remote sensing images. And among the three sampling methods, the image obtained by the nearest interpolation sampling method has the highest correlation with the original image.

scholarly journals Remote sensing data retouching based on image inpainting algorithms in the forgery generation problem

2020 ◽  
Vol 44 (5) ◽  
pp. 763-771
Author(s):  
A.V. Kuznetsov ◽  
M.V. Gashnikov
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Inpainting ◽  
Remote Sensing Data ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
Natural Data ◽  
Generation Problem

We investigate image retouching algorithms for generating forgery Earth remote sensing data. We provide an overview of existing neural network solutions in the field of generation and inpainting of remote sensing images. To retouch Earth remote sensing data, we use imageinpainting algorithms based on convolutional neural networks and generative-adversarial neural networks. We pay special attention to a generative neural network with a separate contour prediction block that includes two series-connected generative-adversarial subnets. The first subnet inpaints contours of the image within the retouched area. The second subnet uses the inpainted contours to generate the resulting retouch area. As a basis for comparison, we use exemplar-based algorithms of image inpainting. We carry out computational experiments to study the effectiveness of these algorithms when retouching natural data of remote sensing of various types. We perform a comparative analysis of the quality of the algorithms considered, depending on the type, shape and size of the retouched objects and areas. We give qualitative and quantitative characteristics of the efficiency of the studied image inpainting algorithms when retouching Earth remote sensing data. We experimentally prove the advantage of generative-competitive neural networks in the construction of forgery remote sensing data.

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