Remote sensing image classification with noisy labels is receiving increasing attention. However, the existing methods ignore the context information of the training sample and judge whether the label is a noise label only by monitoring the loss value of a single sample, which may lead to misjudgment of the sample label. Additionally, these algorithms do not consider constructing pairs of confidence instances to obtain robust potential representations after identifying confidence instances. In this paper, a Multi-view Collaborative Representation Learning (MCRL) approach from noisy labels is proposed to improve the classification performance of very high resolution (VHR) remote sensing images. Specifically, we design a correction strategy based on spatial consistency and confidence-aware mechanisms. This strategy quantitatively measures label reliability by mining the contextual information of labelled samples within the adaptive region. Leveraging the spatial consistency principle and the confidence-aware mechanism to correct and smooth the noisy labels progressively. Moreover, we construct confidence sample pairs by establishing relationships between samples within and between views to obtain robust latent representations, which improves the model's tolerance to noisy labels. Experiments show that the MCRL can significantly reduce the impact of noisy labels on the model and is more competitive than homologous algorithms.

Recently, anchor graph-based multi-view clustering has been proven to be highly efficient for large-scale data processing. However, most existing anchor graph-based clustering methods necessitate post-processing to obtain clustering labels and are unable to effectively utilize the information within anchor graphs. To solve these problems, we propose an Embedded Feature Selection on Graph-Based Multi-View Clustering (EFSGMC) approach to improve the clustering performance. Our method decomposes anchor graphs, taking advantage of memory efficiency, to obtain clustering labels in a single step without the need for post-processing. Furthermore, we introduce the l2,p-norm for graph-based feature selection, which selects the most relevant data for efficient graph factorization. Lastly, we employ the tensor Schatten p-norm as a tensor rank approximation function to capture the complementary information between different views, ensuring similarity between cluster assignment matrices. Experimental results on five real-world datasets demonstrate that our proposed method outperforms state-of-the-art approaches.

CNNs are widely used in remote sensing image classification because of its outstanding feature extraction ability. However, the classification performance is limited by the complexity of remote sensing scenes and the large inter-class similarity. Furthermore, the existing methods usually distinguish multiple classes of complex targets at the same time, which brings great difficulties to the classification model. To alleviate the above problems, we propose a coarse-to-fine cell division (CFCD) approach to improve HRSIs classification. The algorithm divides the limited labeled samples into two subclasses through continuous decomposition, which reduces the similarity between the ground object classes from the data level. We employ the ℓ12-norm to depict the specific distribution of the target for only two subclasses rather than multiple classes of ground objects, so that the exclusive features of targets can be selected more accurately. Moreover, we propose an optimization process of multi-level training, which not only significantly reduces the difficulty of distinguishing multi-class targets, but also improves the utilization of training samples. Experimental results show that the CFCD algorithm outperforms the state-of-the-art methods with limited training samples on three publicly available HRSIs datasets.

Self-supervised learning (SSL) has been successfully applied to remote sensing image classification by designing pretext tasks to extract valuable feature representations of targets. However, existing SSL methodologies overlook the edge information integral to ground objects, culminating in frequent misclassifications at target boundaries. Additionally, the scarcity of training samples often restricts the full utilization of the knowledge encapsulated in the pre-training model. To address these issues, we propose a novel self-supervised edge perception learning framework (SEPLF) to improve the classification performance of high-resolution remote sensing images (HRSI). The framework comprises self-supervised edge perception learning (SEPL) and training sample augmentation (TSA) algorithms. On the one hand, the SEPL approach leverages morphological data enhancement strategies to render the extracted invariant features more robust. It also effectively mines the potential information concealed at target edges, augmenting ground objects’s edge separability. On the other hand, the TSA algorithm not only obtains a large number of training samples but also enhances the intra-class diversity of the samples by considering different spectral features of the same category of ground objects. Experimental results validate that our proposed method outperforms state-of-the-art algorithms, particularly with limited labeled samples.

The underwater environment is complex and the underwater light propagation undergoes absorption, scattering and reflection. This leads to the fact that the underwater light imaging cannot be generalized from land-based. How to use these imaging features to work better with super-resolution tasks for underwater imagery applications is still rarely studied. In this paper, we introduce the medium transmission (MT) maps to advance super-resolution tasks for underwater images. A multi-view network is designed to fuse information from the original underwater images and the MT maps, which provides information on the underlying physical properties of the water, such as the attenuation coefficients in different parts of water. By integrating information from multiple views, the proposed network can capture more of the underlying structure and features of the scene, leading to higher-quality super-resolved images. Besides, a new loss function, namely MT Loss, is developed according to the lack of details in special region of the underwater images. This loss function emphasizes the regions with less influence from the underwater environment during the underwater imaging process and therefore the network outputs a more detailed image. Finally, we compare our algorithm with state-of-the-art methods, and extensive results show that our network achieves better qualitative and quantitative performance.

Training samples are usually required to train a classifier for supervised classification of very high spatial resolution (VHR) remote sensing images. However, labeling samples is often a labor-intensive and time-consuming task. To solve this problem, this study integrates histogram distribution analysis, double-window flexible pace search (DFPS), and box–whisker plot (BP) techniques into an iterative algorithm to enrich training samples. The major steps of the proposed algorithm are given as follows. First, to acquire the feature distribution of a class, a histogram of each class (HOC) based on the raw classification map is generated. Second, to cover the spectral heterogeneity of an intraclass, some pixel points in each bin of HOC are selected as the coarse training sample set (CTS). Third, to further purify the CTS, DFPS, and BP techniques are adopted to exclude outlier samples and select the representative samples to signify the corresponding class. Finally, the refined training samples are used to retrain the classifier, and the preceding steps are constructed as an iterative algorithm. Experiments were performed on three real VHR remote sensing images to demonstrate the superiorities of the proposed approach in improving classification performance with respect to the maps obtained directly by the initial training set. In addition, compared with cognate state-of-the-art methods, the proposed approach achieved an approximately 2%–13% improvement in classification accuracy. Code available here:https://github.com/ImgSciGroup/IEEE-GRSL-GSEA-Code.

Imbalanced training sets are known to produce suboptimal maps for supervised classification. Therefore, one challenge in mapping land cover is acquiring training data that will allow classification with high overall accuracy (OA) in which each class is also mapped onto similar user's accuracy. To solve this problem, we integrated local adaptive region and box-and-whisker plot (BP) techniques into an iterative algorithm to expand the size of the training sample for selected classes in this article. The major steps of the proposed algorithm are as follows. First, a very small initial training sample (ITS) for each class set is labeled manually. Second, potential new training samples are found within an adaptive region by conducting local spectral variation analysis. Lastly, three new training samples are acquired to capture information regarding intraclass variation; these samples lie in the lower, median, and upper quartiles of BP. After adding these new training samples to the ITS, classification is retrained and the process is continued iteratively until termination. The proposed approach was applied to three very high-resolution (VHR) remote-sensing images and compared with a set of cognate methods. The comparison demonstrated that the proposed approach produced the best result in terms of OA and exhibited superiority in balancing user's accuracy. For example, the proposed approach was typically 2%-10% more accurate than the compared methods in terms of OA and it generally yielded the most balanced classification.