AUTOMATIC ASSESSMENT OF ACQUISITION AND TRANSMISSION LOSSES IN INDIAN REMOTE SENSING SATELLITE DATA

The quality of Remote Sensing data is an important parameter that defines the extent of its usability in various applications. The data from Remote Sensing satellites is received as raw data frames at the ground station. This data may be corrupted with data losses due to interferences during data transmission, data acquisition and sensor anomalies. Thus it is important to assess the quality of the raw data before product generation for early anomaly detection, faster corrective actions and product rejection minimization. Manual screening of raw images is a time consuming process and not very accurate. In this paper, an automated process for identification and quantification of losses in raw data like pixel drop out, line loss and data loss due to sensor anomalies is discussed. Quality assessment of raw scenes based on these losses is also explained. This process is introduced in the data pre-processing stage and gives crucial data quality information to users at the time of browsing data for product ordering. It has also improved the product generation workflow by enabling faster and more accurate quality estimation.

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Valuing Radiometric Quality of Remote Sensing Data for Decisions

10.1109/igarss47720.2021.9553916 ◽

2021 ◽

Author(s):

Afreen Siddiqi ◽

Sheila Baber ◽

Olivier De Weck

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Sensing Data

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Lossy Compression of Multichannel Remote Sensing Images with Quality Control

Remote Sensing ◽

10.3390/rs12223840 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3840

Author(s):

Vladimir Lukin ◽

Irina Vasilyeva ◽

Sergey Krivenko ◽

Fangfang Li ◽

Sergey Abramov ◽

...

Keyword(s):

Remote Sensing ◽

Classification Accuracy ◽

Real Life ◽

Remote Sensing Data ◽

Original Data ◽

Lossy Compression ◽

Compressed Images ◽

Training Methodology ◽

Neural Network Classifiers

Lossy compression is widely used to decrease the size of multichannel remote sensing data. Alongside this positive effect, lossy compression may lead to a negative outcome as making worse image classification. Thus, if possible, lossy compression should be carried out carefully, controlling the quality of compressed images. In this paper, a dependence between classification accuracy of maximum likelihood and neural network classifiers applied to three-channel test and real-life images and quality of compressed images characterized by standard and visual quality metrics is studied. The following is demonstrated. First, a classification accuracy starts to decrease faster when image quality due to compression ratio increasing reaches a distortion visibility threshold. Second, the classes with a wider distribution of features start to “take pixels” from classes with narrower distributions of features. Third, a classification accuracy might depend essentially on the training methodology, i.e., whether features are determined from original data or compressed images. Finally, the drawbacks of pixel-wise classification are shown and some recommendations on how to improve classification accuracy are given.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.573-574.271 ◽

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.

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Research on Technology of the Global Geographic Data Sets Generation Based on the Original Remote Sensing Data

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.4229 ◽

2012 ◽

Vol 433-440 ◽

pp. 4229-4234

Author(s):

Wen Quan Feng ◽

Gan Zhou ◽

Yong Fang

Keyword(s):

Remote Sensing ◽

Control System ◽

Remote Sensing Data ◽

Data Sets ◽

Generation Process ◽

Data Simulation ◽

Sensing Data ◽

Ground Station ◽

Geographic Data ◽

Operation Control

To meet the simulation and testing requirements of HY-1 Satellite ground operation control system simulation subsystem, a global geographic data sets based on original remote sensing data was proposed. This paper detailed the function and data processing methods of the data sets, mainly including data structure and the key algorithm of the satellite calibration and data filling required in the data sets generation process. At last, using the data simulation function of ground station, the generated raw data would be sent to real ground operation control system, and the processed images showing the data sets were filled with good results

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Evaluating the quality of remote sensing products for agricultural index insurance

PLoS ONE ◽

10.1371/journal.pone.0258215 ◽

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.

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

Proceedings of the International conference “InterCarto/InterGIS” ◽

10.35595/10.35595/2414-9179-2021-2-27-191-204 ◽

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.

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

Computer Optics ◽

10.18287/2412-6179-co-721 ◽

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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