Assessment of Radiometric Resolution Impact on Remote Sensing Data Classification Accuracy

Verde, Natalia; Mallinis, Giorgos; Tsakiri–Strati, Maria; Georgiadis, Charalampos; Πατιάς, Πέτρος

doi:10.3390/rs10081267

Cited by 29 publications

(22 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While this study used an array of reference-based metrics to assess the image quality of various pansharpened images in terms of spectral consistency, spatial consistency, and image synthesis, the information content within the images was not quantified. The use of image information metrics such as Shannon entropy and Boltzmann entropy [46][47][48][49][50] enables the quantification of the average amount of information in the fused images and could be used to effectively assess the efficacy of various pansharpening methods in terms of the ability to retain or enhance both spectral and spatial information.…”

Section: Spectral Synthesismentioning

confidence: 99%

Comparison of Image Fusion Techniques Using Satellite Pour l’Observation de la Terre (SPOT) 6 Satellite Imagery

2020

View full text Add to dashboard Cite

Preservation of spectral and spatial information is an important requirement for most quantitative remote sensing applications. In this study, we use image quality metrics to evaluate the performance of several image fusion techniques to assess the spectral and spatial quality of pansharpened images. We evaluated twelve pansharpening algorithms in this study; the Local Mean and Variance Matching (IMVM) algorithm was the best in terms of spectral consistency and synthesis followed by the ratio component substitution (RCS) algorithm. Whereas the IMVM and RCS image fusion techniques showed better results compared to other pansharpening methods, it is pertinent to highlight that our study also showed the credibility of other pansharpening algorithms in terms of spatial and spectral consistency as shown by the high correlation coefficients achieved in all methods. We noted that the algorithms that ranked higher in terms of spectral consistency and synthesis were outperformed by other competing algorithms in terms of spatial consistency. The study, therefore, concludes that the selection of image fusion techniques is driven by the requirements of remote sensing application and a careful trade-off is necessary to account for the impact of scene radiometry, image sharpness, spatial and spectral consistency, and computational overhead.

show abstract

Section: Spectral Synthesismentioning

confidence: 99%

Comparison of Image Fusion Techniques Using Satellite Pour l’Observation de la Terre (SPOT) 6 Satellite Imagery

2020

View full text Add to dashboard Cite

show abstract

“…Radiometric resolution refers to the number of bit depth divisions, which represent the reflected energy of targets and influence the analysis of targets' spectral features [35,36]. At this time, the greater the spatial resolution, the more the radiometric resolution can lead to additional noise in the image processing results [36].…”

Section: Radiometric Resolution Compressionmentioning

confidence: 99%

“…At this time, the greater the spatial resolution, the more the radiometric resolution can lead to additional noise in the image processing results [36]. Furthermore, a lower radiometric resolution reduces the computational complexity and it is more time efficient, while the difference in the information content in the high and low radiometric data is negligible [35]. Therefore, in this study, the radiometric resolution of the images is compressed while using the linear rescale method, and the range is taken from 8-bit to 14-bit [35,37].…”

Section: Radiometric Resolution Compressionmentioning

confidence: 99%

“…Furthermore, a lower radiometric resolution reduces the computational complexity and it is more time efficient, while the difference in the information content in the high and low radiometric data is negligible [35]. Therefore, in this study, the radiometric resolution of the images is compressed while using the linear rescale method, and the range is taken from 8-bit to 14-bit [35,37]. The optimal radiometric resolution is set according to the results of multiple trials, with the goal to still balance the time cost and accuracy.…”

Section: Radiometric Resolution Compressionmentioning

confidence: 99%

See 1 more Smart Citation

Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery

Seo

2019

Applied Sciences

View full text Add to dashboard Cite

Radiometric normalization is an essential preprocessing step that must be performed to detect changes in multi-temporal satellite images and, in general, relative radiometric normalization is utilized. However, most relative radiometric normalization methods assume a linear relationship and they cannot take into account nonlinear properties, such as the distribution of the earth's surface or phenological differences that are caused by the growth of vegetation. Thus, this paper proposes a novel method that assumes a nonlinear relationship and it uses a representative nonlinear regression model-multilayer perceptron (MLP). The proposed method performs radiometric resolution compression while considering both the complexity and time cost, and radiometric control set samples are extracted based on a no-change set method. Subsequently, the spectral index is selected for each band to compensate for the phenological properties, phenological normalization is performed based on MLP, and the global radiometric properties are adjusted through postprocessing. Finally, a performance evaluation is conducted by comparing the results herein with those from conventional relative radiometric normalization algorithms. The experimental results show that the proposed method outperforms conventional methods in terms of both visual inspection and quantitative evaluation. In other words, the applicability of the proposed method to the normalization of multi-temporal images with nonlinear properties is confirmed. Relative radiometric normalization, on the other hand, is an image-based normalization method that selects one image as a reference and matches the radiometric characteristics of a subject image [13,14]. That is, relative radiometric normalization is generally utilized since the multi-temporal images are normalized into common scales without extra parameters [12,15].Relative radiometric normalization can be classified into two categories: a global statistics-based method and a radiometric control set sample (RCSS)-based method. The former is performed by using statistical values of all pixels in the image, which includes histogram matching, minimum-maximum (MM), mean-standard deviation (MS), and simple regression (SR) [15,16]. In contrast, the latter is based on invariant pixels, known as RCSSs, which include dark set-bright set (DB), pseudo invariant features (PIF), and no-change sets (NC) [17][18][19][20]. At this time, except for histogram matching, normalization is performed through regression equations, while assuming a linear relationship between the pixels at the same position in each band [21]. However, most remote sensing data are nonlinearly distributed, and the surface of the earth is composed of a complex mixture of natural and man-made features that exhibit nonlinear characteristics [22][23][24]. Changes that are caused by vegetation in particular have the most typical characteristics, including nonlinearity, which induces serious disturbances when change detection is performed [25,26]. In addition, since optical sa...

show abstract

“…In recent years, with the rapid development of remote sensing technology and the establishment of global earth observation system, the obtaining ability of various remotely sensed data is increasingly strong (Verde et al, 2018). As the development of open science and the increasing popularity of mobile devices (e.g., smartphones), remote sensing data as an important data sources are widely applied in human life.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Evaluating the Information Content of Remote Sensing Images

Fang¹,

Zhou²

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

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.

show abstract

Assessment of Radiometric Resolution Impact on Remote Sensing Data Classification Accuracy

Cited by 29 publications

References 41 publications

Comparison of Image Fusion Techniques Using Satellite Pour l’Observation de la Terre (SPOT) 6 Satellite Imagery

Comparison of Image Fusion Techniques Using Satellite Pour l’Observation de la Terre (SPOT) 6 Satellite Imagery

Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery

An Approach for Evaluating the Information Content of Remote Sensing Images

Contact Info

Product

Resources

About