Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis

Vegetation indices time series analysis is increasingly improved for characterizing agricultural land processes. However, this is challenging because of the multeity of factors affecting vegetation growth. In semiarid regions the rainfall, the soil properties and climate are strongly correlated with crop growth. These relationships are commonly analyzed using the normalized difference vegetation index (NDVI). NDVI series from two sites, belonging to different agroclimatic zones, were examined, decomposing them into the overall average pattern, residuals, and anomalies series. All of them were studied by applying the concept of the generalized Hurst exponent. This is derived from the generalized structure function, which characterizes the series’ scaling properties. The cycle pattern of NDVI series from both zones presented differences that could be explained by the differences in the climatic precipitation pattern and soil characteristics. The significant differences found in the soil reflectance bands confirm the differences in both sites. The scaling properties of NDVI original series were confirmed with Hurst exponents higher than 0.5 showing a persistent structure. The opposite was found when analyzing the residual and the anomaly series with a stronger anti-persistent character. These findings reveal the influences of soil–climate interactions in the dynamic of NDVI series of rainfed cereals in the semiarid.

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“…This multifractal analysis in NDVI have been wider used in the spatial scaling context [10,[71][72][73][74].…”

Section: Scaling Characteristics Of Ndvi Original Seriesmentioning

confidence: 99%

Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

et al. 2021

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“…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%

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

Seo

2019

Applied Sciences

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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...

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“…Three different experiments were designed in order to assess linkages between classification accuracy and radiometric resolution under different classification schemes, spatial resolution, units of classification and image variables ( Figure 2): (i) assessment of the radiometric resolution impact on the classification accuracy and change identification under a binary classification scheme and different spatial resolution, using two images acquired over the same area over a three-year period (experimental setting 1); (ii) assessment of the radiometric resolution impact in a multiclass classification problem employing spectral and texture features (experimental setting 2); (iii) assessment of the radiometric resolution impact on multiseasonal original and synthetic (spectral indices) band classification using a per-field classification approach (experimental setting 3). In all three experiments, images were rescaled from 16-bit to 8-bit radiometric resolution, using the linear rescale method [8,16,37], in order to artificially create the new images to study radiometric resolution effect on classification accuracy. Finally, in each experimental setting the same reference data samples were used for both high and low radiometric resolution datasets while the processing time was also measured.…”

Section: Design Of the Experimentsmentioning

confidence: 99%

“…Similar studies, such as those of Masek et al [14], and Karnieli et al [15], which, compared the amount of information between images with the same radiometry using entropy, indicated that the Landsat-7 ETM+ sensor data contained more information than the Landsat-5 TM sensor data, despite having the same 8-bit radiometric resolution. The effect of radiometry was examined through multifractal analysis and entropy in a study by Alonso et al [16]. The results demonstrated a greater influence of radiometric resolution on blue and green bands than on red and near infrared ones.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Radiometric Resolution Impact on Remote Sensing Data Classification Accuracy

et al. 2018

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Improved sensor characteristics are generally assumed to increase the potential accuracy of image classification and information extraction from remote sensing imagery. However, the increase in data volume caused by these improvements raise challenges associated with the selection, storage, and processing of this data, and with the cost-effective and timely analysis of the remote sensing datasets. Previous research has extensively assessed the relevance and impact of spatial, spectral and temporal resolution of satellite data on classification accuracy, but little attention has been given to the impact of radiometric resolution. This study focuses on the role of radiometric resolution on classification accuracy of remote sensing data through different classification experiments over three different sites. The experiments were carried out using fine and low scale radiometric resolution images classified through a bagging classification tree. The classification experiments addressed different aspects of the classification road map, including among others, binary and multiclass classification schemes, spectrally and spatially enhanced images, as well as pixel and objects as units of the classification. In addition, the impact of image radiometric resolution on computational time and the information content in fine- and low-resolution images was also explored. While in certain cases, higher radiometric resolution has led to up to 8% higher classification accuracies compared to lower resolution radiometric data, other results indicate that higher radiometric resolution does not necessarily imply improved classification accuracy. Also, classification accuracy of spectral indices and texture bands is not related so much to the radiometric resolution of the original remote sensing images but rather to their own radiometric resolution. Overall, the results of this study suggest that data selection and classification need not always adhere to the highest possible radiometric resolution.

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Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis

Cited by 9 publications

References 40 publications

Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

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

Assessment of Radiometric Resolution Impact on Remote Sensing Data Classification Accuracy

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