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NNetEn 2D : Two-Dimensional Neural Network Entropy in Remote Sensing Imagery and Geophysical Mapping.
- Source :
- Remote Sensing; May2022, Vol. 14 Issue 9, p2166-2166, 25p
- Publication Year :
- 2022
-
Abstract
- Measuring the predictability and complexity of 2D data (image) series using entropy is an essential tool for evaluation of systems' irregularity and complexity in remote sensing and geophysical mapping. However, the existing methods have some drawbacks related to their strong dependence on method parameters and image rotation. To overcome these difficulties, this study proposes a new method for estimating two-dimensional neural network entropy (NNetEn<subscript>2D</subscript>) for evaluating the regularity or predictability of images using the LogNNet neural network model. The method is based on an algorithm for converting a 2D kernel into a 1D data series followed by NNetEn<subscript>2D</subscript> calculation. An artificial test image was created for the study. We demonstrate the advantage of using circular instead of square kernels through comparison of the invariance of the NNetEn<subscript>2D</subscript> distribution after image rotation. Highest robustness was observed for circular kernels with a radius of R = 5 and R = 6 pixels, with a NNetEn<subscript>2D</subscript> calculation error of no more than 10%, comparable to the distortion of the initial 2D data. The NNetEn<subscript>2D</subscript> entropy calculation method has two main geometric parameters (kernel radius and its displacement step), as well as two neural network hyperparameters (number of training epochs and one of six reservoir filling techniques). We evaluated our method on both remote sensing and geophysical mapping images. Remote sensing imagery (Sentinel-2) shows that brightness of the image does not affect results, which helps keep a rather consistent appearance of entropy maps over time without saturation effects being observed. Surfaces with little texture, such as water bodies, have low NNetEn<subscript>2D</subscript> values, while urban areas have consistently high values. Application to geophysical mapping of rocks to the northwest of southwest Australia is characterized by low to medium entropy and highlights aspects of the geology. These results indicate the success of NNetEn<subscript>2D</subscript> in providing meaningful entropy information for 2D in remote sensing and geophysical applications. [ABSTRACT FROM AUTHOR]
- Subjects :
- REMOTE sensing
ENTROPY
ARTIFICIAL neural networks
SURFACE texture
BODIES of water
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 14
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 156874504
- Full Text :
- https://doi.org/10.3390/rs14092166