Polarimetric Synthetic Aperture Radar (PolSAR) image classification remains challenging due to complex scattering mechanisms, speckle noise, and the difficulty of selecting the most informative features from the multitude of derivable polarimetric representations. To address these challenges, we propose a novel two-step methodology for PolSAR image classification that utilizes an advanced feature extraction technique while leveraging the strengths of a vision transformer(ViT) architecture. In the first step, advanced feature extraction techniques are explored to capture multiscale spatial scattering information and preserve crucial structural information of the PolSAR image while effectively mitigating the noise present on it. In the second step, a dual-branch ViT (DB-ViT) is proposed that simultaneously processes both the original polarimetric features and the extracted spatial features, enabling effective information fusion through a local window attention transformer (LWAT). Extensive experiments on the Flevoland AIRSAR and the San-Francisco RADARSAT-2 benchmark datasets demonstrated that our approach consistently outperforms state-of-the-art methods, achieving the highest overall accuracies of 99.50% and 99.51%, respectively.

Spectral-spatial classification of hyperspectral images (HSIs) has been extensively studied. Although the importance of spatial information for classification of HSIs is widely proven in the literature, the definition of effective techniques for the extraction of spatial information is still a challenging and open research issue. In this letter, a semantic edge-aware structure preserving image filtering technique is presented to accurately model spatial information in HSI classification. The experimental results on the three real HSI data sets show the superiority of our model, which provides at least 2% higher classification accuracy than the best among the numerous literature models considered.

Convolutional neural networks (CNNs) have become a popular and powerful tool for polarimetric synthetic aperture radar (PolSAR) image classification. The success of the CNN model is dependent on the features that the networks extract from the polarimetric channels during the learning phase. To extract better discriminative features, we propose a novel two-step method. In the first step, by exploiting singular value decomposition (SVD), a multiscale SVD profile (MSVDP) is constructed that models spatial information of each pixel of the PolSAR image in multiple scales. In the second step, a lightweight and shallow dual-branch CNN is proposed to take the original PolSAR image and the constructed MSVDP as inputs for extracting more discriminative features during the learning of the CNN model. The effectiveness of the proposed model is validated using three real PolSAR datasets. Our proposed technique provides accurate and satisfactory results irrespective of the considered polarimetric feature sets and power descriptors. Source code for the dual-branch CNN is available athttps://github.com/ND-PatternHunter/DB-SVD-CNN

Active learning is one of the popular approaches that can mitigate some of the drawbacks of supervised classification. Although sparse representation classifier has already proven to be a robust classifier and successfully used in many applications, it is seldom used jointly with active learning. In this letter, we propose a novel active learning technique for sparse representation classifiers. In the proposed model, the query function is designed by combining uncertainty and diversity criteria, both of which are defined by using the sparse representation classifier in kernel space. The proposed technique outperforms other state-of-the-art methods in terms of classification performance.

Hyperspectral images are acquired in hundreds of spectral channels that contain rich spectral information of different land‐cover objects but at the same time they have high dimensionality with ample redundancy which rises curse of dimensionality and computational issues for classification. To mitigate such issues, band selection that reduces the dimensionality of hyperspectral data is a well‐known approach widely used in the literature. Neighbourhood rough set, a variant of rough set capable of analysing continuous values, is a robust mathematical tool for handling uncertain and vague data. In this study, the authors have presented an empirical study of four forward greedy hyperspectral band selection algorithms implemented using the neighbourhood rough set, the variable precision neighbourhood rough set, the consistency measure of neighbourhood rough set and the granulation knowledge‐based neighbourhood rough set. The effectiveness of these techniques is compared in terms of average classification accuracy, kappa accuracy and standard deviation obtained by using support vector machine classifier on three real hyperspectral data sets. From the experiments, it is found that the variable precision neighbourhood rough set and the consistency measure of neighbourhood rough set are more robust for selecting informative bands compared to the others. The effectiveness of these techniques is also validated by comparing with some state‐of‐the‐art techniques.

Selection of threshold values to generate nonredundant filtered images in attribute profiles (APs) is an unresolved issue. This paper presents a novel filtering approach to the construction of APs that does not require the definition of any threshold value. The proposed approach creates a max-tree (or min-tree), traverse to the first encountered leaf node using depth first traversal, and defines a leaf attribute function (LAF) to demonstrate the changes in attribute values from leaf to root node. The LAF is analyzed based on a novel criterion to automatically detect the node along the path that has a first significant difference in the attribute value. All its descendant nodes are merged to it and the process is repeated for each unvisited leaf node to create the final filtered tree which is transformed back as a filtered image. The proposed approach can incorporate maximum spatial information by applying a few filtering operations without the need to define any threshold value. This is of great importance in spectral-spatial classification applications. Moreover, since the proposed approach requires one depth first traversal to generate a filtered image, it is very efficient in terms of computation time. To show the effectiveness of the proposed method, three real hyperspectral data sets are considered and the results are compared to the state-of-the-art method considering five different attributes. The results show that the proposed method has several important advantages with respect to the existing threshold-based filtering techniques. Furthermore, the proposed method is also effective when compared with different spectral-spatial classification techniques.

Rough set theory is a paradigm to deal with uncertainty, vagueness, and incompleteness of data. Although it has been applied successfully to feature selection in different application domains, it is seldom used for the analysis of hyperspectral images. In this paper, a rough set based supervised method is proposed to select informative bands in hyperspectral images. The proposed technique exploits rough set theory to define a novel criterion for selecting informative bands. The performances of the proposed approach were compared with those of three state-of-the-art methods on a hyperspectral data set. Experimental results show the effectiveness of the proposed technique.

Band selection is a well-known approach to reduce the dimensionality of hyperspectral imagery. Rough set theory is a paradigm to deal with uncertainty, vagueness, and incompleteness of data. Although it has been applied successfully to feature selection in different application domains, it is seldom used for the analysis of the hyperspectral imagery. In this paper, a rough-set-based supervised method is proposed to select informative bands from hyperspectral imagery. The proposed technique exploits rough set theory to compute the relevance and significance of each spectral band. Then, by defining a novel criterion, it selects the informative bands that have higher relevance and significance values. To assess the effectiveness of the proposed band selection technique, three state-of-the-art methods (one supervised and two unsupervised) used in the remote sensing literature are analyzed for comparison on three hyperspectral data sets. The results of this comparison point to the superiority of the proposed technique, especially when a small number of bands are to be selected.

In this paper, a novel iterative active learning technique based on self-organizing map (SOM) neural network and support vector machine (SVM) classifier is presented. The technique exploits the properties of the SVM classifier and of the SOM neural network to identify uncertain and diverse samples, to include in the training set. It selects uncertain samples from low-density regions of the feature space by exploiting the topological properties of the SOM. This results in a fast convergence also when the available initial training samples are poor. The effectiveness of the proposed method is assessed by comparing it with several methods existing in the literature using a toy data set and a color image as well as real multispectral and hyperspectral remote sensing images.

This paper presents a novel batch mode active learning technique for solving remote sensing image classification problems. The proposed technique incorporates uncertainty, diversity and cluster assumption criteria to design the query function. The uncertainty criterion is implemented by taking into account the properties of the support vector machine classifiers. The diversity and cluster assumption criteria are defined by exploiting the properties of the self-organizing map neural networks. To assess the effectiveness of the proposed method, we compared it with several other active learning methods existing in the remote sensing literature by using both multispectral and hyperspectral remote sensing data sets. Experimental results confirmed the effectiveness of the proposed technique.

In this letter, we present a novel batch-mode active learning technique for solving multiclass classification problems by using the support vector machine classifier with the one-against-all architecture. The uncertainty of each unlabeled sample is measured by defining a criterion which not only considers the smallest distance to the decision hyperplanes but also takes into account the distances to other hyperplanes if the sample is within the margin of their decision boundaries. To select batch of most uncertain samples from all over the decision region, the uncertain regions of the classifiers are partitioned into multiple parts depending on the number of geometrical margins of binary classifiers passing on them. Then, a balanced number of most uncertain samples are selected from each part. To minimize the redundancy and keep the diversity among these samples, the kernelk-means clustering algorithm is applied to the set of uncertain samples, and the representative sample (medoid) from each cluster is selected for labeling. The effectiveness of the proposed method is evaluated by comparing it with other batch-mode active learning techniques existing in the literature. Experimental results on two different remote sensing data sets confirmed the effectiveness of the proposed technique.

In this paper, we propose a simple, fast, and reliable active-learning technique for solving remote sensing image classification problems with support vector machine (SVM) classifiers. The main property of the proposed technique consists in its robustness to biased (poor) initial training sets. The presented method considers the 1-D output space of the classifier to identify the most uncertain samples whose labeling and inclusion in the training set involve a high probability to improve the classification results. A simple histogram-thresholding algorithm is used to find out the low-density (i.e., under the cluster assumption, the most uncertain) region in the 1-D SVM output space. To assess the effectiveness of the proposed method, we compared it with other active-learning techniques proposed in the remote sensing literature using multispectral and hyperspectral data. Experimental results confirmed that the proposed technique provided the best tradeoff among robustness to biased (poor) initial training samples, computational complexity, classification accuracy, and the number of new labeled samples necessary to reach convergence.

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. This technique is based on a modified Hopfield neural network architecture designed to model spatial correlation between neighboring pixels of the difference image produced by comparing images acquired on the same area at different times. Each spatial position in the considered scene is represented by a neuron in the Hopfield network that is connected only to its neighboring units. These connections model the spatial correlation between neighboring pixels and are associated with a context-sensitive energy function that represents the overall status of the network. Change detection maps are obtained by iteratively updating the output status of the neurons until a minimum of the energy function is reached and the network assumes a stable state. A simple heuristic thresholding procedure is presented and adopted for initializing the network. The proposed change detection technique is unsupervised and distribution free. Experimental results carried out on two multispectral and multitemporal remote sensing images confirm the effectiveness of the proposed technique