Feature fusion method based on local binary graph for PolSAR image classification

Sebt, Mohammad Ali; Darvishnezhad, Mohsen

doi:10.1049/rsn2.12389

Cited by 3 publications

(4 citation statements)

References 72 publications

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“…Then, the reduced noise PolSAR data is used as the input of the PolSAR feature extraction algorithms. In this paper, traditional PolSAR feature extraction algorithms have been used that are described in our previous work [57,112], as it can be seen in Section 2. By applying the feature extraction algorithms to the reduced noised PolSAR data, the PolSAR features can be obtained.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…In the designed feature-level ensemble strategy, the Local Bainary Graph (LBG) feature fusion method is used to fuse the extracted feature of the SS model that is presented in our previous work [57,112]. To see more about the LBG, you can see refs.…”

Section: Feature Level Ensemble Modelmentioning

confidence: 99%

“…After the vast improvement of deep learning (DL) methods, the spatial-polarimetric information-learning algorithm based on DL models is majorly considered. However, the deployment capability of the spatial-polarimetric algorithms is usually weak [56,57]. A large number of hyperparameters need to be set for different PolSAR images, which mainly requires strong professional science knowledge and a large number of tests, thus constraining the improvement of these algorithms [58].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Proposed Methodsmentioning

confidence: 99%

Section: Feature Level Ensemble Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…[9] proposes that the collaborative representation of a PolSAR cell by its neighborhood provides a fused feature space containing both a polarimetric and spatial feature. Furthermore, Ali Sebt M. Darvishnezhad M. [10] introduced a local graph-based fusion method that combines features extracted by Convolutional Neural Networks (CNNs) with their version of Graph Neural Networks (GNNs), referred to as mini GNN.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Coherent and Non-Coherent Pol-SAR Features for Land Cover Classification

Karachristos,

Koukiou,

Anastassopoulos

2024

Electronics

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Remote Sensing plays a fundamental role in acquiring crucial information about the Earth’s surface from a distance, especially through fully polarimetric data, which offers a rich source of information for diverse applications. However, extracting meaningful insights from this intricate data necessitates sophisticated techniques. In addressing this challenge, one predominant trend that has emerged is known as target decomposition techniques. These techniques can be broadly classified into coherent and non-coherent methods. Each of these methods provides high-quality information using different procedures. In this context, this paper introduces innovative feature fusion techniques, amalgamating coherent and non-coherent information. While coherent techniques excel in detailed exploration and specific feature extraction, non-coherent methods offer a broader perspective. Our feature fusion techniques aim to harness the strengths of both approaches, providing a comprehensive and high-quality fusion of information. In the first approach, features derived from Pauli coherent decomposition, Freeman–Durden non-coherent technique, and the Symmetry criterion from Cameron’s stepwise algorithm are combined to construct a sophisticated feature vector. This fusion is achieved using the well-established Fisher Linear Discriminant Analysis algorithm. In the second approach, the Symmetry criterion serves as the basis for fusing coherent and non-coherent coefficients, resulting in the creation of a new feature vector. Both approaches aim to exploit information simultaneously extracted from coherent and non-coherent methods in feature extraction from Remote Sensing data through fusion at the feature level. To evaluate the effectiveness of the feature generated by the proposed fusion techniques, we employ a land cover classification procedure. This involves utilizing a basic classifier, achieving overall accuracies of approximately 82% and 86% for each of the two proposed techniques. Furthermore, the accuracy in individual classes surpasses 92%. The evaluation aims to gauge the effectiveness of the fusion methods in enhancing feature extraction from fully polarimetric data and opens avenues for further exploration in the integration of coherent and non-coherent features for remote sensing applications.

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A multi-view ensemble model based on semi-supervised feature learning for small sample classification of PolSAR images

Darvishnezhad

2024

International Journal of Remote Sensing

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Feature fusion method based on local binary graph for PolSAR image classification

Cited by 3 publications

References 72 publications

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

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

Fusion of Coherent and Non-Coherent Pol-SAR Features for Land Cover Classification

A multi-view ensemble model based on semi-supervised feature learning for small sample classification of PolSAR images

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