Entropy/anisotropy/alpha based 3DGabor filter bank for PolSAR image classification

Imani, Maryam

doi:10.1080/10106049.2022.2142963

Cited by 6 publications

(3 citation statements)

References 55 publications

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“…Let n = 72. After calculating with Formulas ( 9) and (10), two line charts are shown in the right sub-figure of Figure 1, which uncovers the relationship between 1D direction entropy (H EDH ) and 2D direction entropy (H sobel2 ) with respect to the fog density. It could be found that both methods effectively highlight the differences in fog density, with the range of H sobel2 being 2.6177, and H EDH being 0.9127.…”

Section: Two-dimensional Directional Entropymentioning

confidence: 93%

“…Classical one-dimensional image entropy includes fuzzy entropy [4,5], Kapur entropy [6], cross entropy [7,8], and Shannon entropy [9]. In addition to being applied to image segmentation or classification, image entropy has also been applied to image filtering and denoising [10,11]. There are relatively few studies on two-dimensional or multidimensional entropy.…”

Section: Image Entropymentioning

confidence: 99%

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Fog Density Evaluation by Combining Image Grayscale Entropy and Directional Entropy

Cao

Wang

2023

Atmosphere

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The fog density level, as one of the indicators of weather conditions, will affect the management decisions of transportation management agencies. This paper proposes an image-based method to estimate fog density levels to improve the accuracy and efficiency of analyzing fine meteorological conditions and validating fog density predictions. The method involves two types of image entropy: a two-dimensional directional entropy derived from four-direction Sobel operators, and a combined entropy that integrates the image directional entropy and grayscale entropy. For evaluating the performance of the proposed method, an image test set and an image training set are constructed; and each image is labeled as heavy fog, moderate fog, light fog, or fog-free according to the fog density level of the image based on a user study. Using our method, the average accuracy rates of image fog level estimation were 77.27% and 79.39% on the training set using the five-fold cross-validation and the test set, respectively. Our experimental results demonstrate the effectiveness of the proposed combined entropy for image-based fog density level estimation.

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Section: Two-dimensional Directional Entropymentioning

confidence: 93%

Section: Image Entropymentioning

confidence: 99%

Fog Density Evaluation by Combining Image Grayscale Entropy and Directional Entropy

Cao

Wang

2023

Atmosphere

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“…Therefore, PolSAR image classification has major challenges associated with a small training set. Feature learning methods that obtain better identifiable information through mapping the PolSAR image to a new feature space are one of the beneficial algorithms to increase the classification performance in terms of small training set [40,41]. To date, the improvement of PolSAR image feature learning methods has been expressed in various ways: Polarimetric feature learning methods, spatial-polarimetric feature learning algorithms, and deep information extraction of PolSAR images based on deep neural networks [42].…”

Section: Introductionmentioning

confidence: 99%

A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

Darvishnezhad,

Sebt

2023

IET Radar Sonar & Navi

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Classification with a few samples of training set has been a longstanding issue in the field of polarimetric synthetic aperture radar (PolSAR) image analysis and processing. Aiming at the small number of training samples of the PolSAR image classification task, a novel Self‐Supervised Ensemble Learning Framework (SSELF) is designed. The designed SSELF can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. In addition, it can significantly decrease the dependence of neural network algorithms on large labelled samples of training set. First, utilise the spatial–polarimetric features of PolSAR data perfectly, the EfficientNet‐B0 is presented and utilised as the main section of the Deep Learning (DL) model to extract DL features of PolSAR data. Then, using an optimisation function that constrains the cross‐correlation matrix of various distortions of each sample to the identity matrix, the designed DL model can obtain the effective features of homogeneous samples gathering and heterogeneous samples separating from each other in a self‐supervised manner. Moreover, following the great success of curriculum learning in the area of machine learning, a novel deep curriculum‐learning model is proposed, entitled Deep Curriculum Learning (DCL), to train the DL network in our self‐supervised model. The proposed DCL utilises the entropy‐alpha target decomposition to estimate the degree of complexity of each PolSAR image patch before applying it to the EfficientNet‐B0. Also, an accumulative mini‐batch pacing function is used to introduce more difficult patches to EfficientNet‐B0 in the training process of the designed self‐supervised model. Furthermore, two EL models, feature‐level and view‐level ensemble, are proposed to increase the feature extraction capability and classification result of PolSAR data by jointly using spatial features at different scales and polarimetric information at different bands. In fact, in the proposed feature‐level ensemble strategy, to improve the classification result, the extracted features of the different scales are used as the input of the designed feature fusion algorithm. Therefore, the extracted features are mapped to the new space with lower dimension. In general, in the proposed view‐level ensemble strategy, first, the Mutual Information (MI) between each feature and the other features calculates, and then based‐on the calculated MI, different group of features are selected as various views. Next, the final classification result of the PolSAR image classification is obtained by using the designed majority vote of the result in each view. It should be noted that unlike existing traditional methods, both amplitude and phase information of SAR images to devolve the training process of the proposed model are used. In addition, all of the parameters of the proposed network are developed to a complex domain. In addition, a complex backpropagation algorithm by using the gradient‐based model is used for training the network. Finally, experimental results on three well‐known PolSAR data sets illustrate that the designed SSELF can extract more discriminant features using the designed DL model and can achieve better classification results than the other six novel DL models in terms of small training samples and adequate labelled samples.

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Residual network based on entropy-anisotropy-alpha target decomposition for polarimetric SAR image classification

2023

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Entropy/anisotropy/alpha based 3DGabor filter bank for PolSAR image classification

Cited by 6 publications

References 55 publications

Fog Density Evaluation by Combining Image Grayscale Entropy and Directional Entropy

Fog Density Evaluation by Combining Image Grayscale Entropy and Directional Entropy

A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

Residual network based on entropy-anisotropy-alpha target decomposition for polarimetric SAR image classification

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