In recent years, supervised learning, represented by deep learning, has shown good performance in remote sensing image scene classification with its powerful feature learning ability. However, this method requires large-scale and high-quality handcrafted labeled datasets, which leads to a high cost of obtaining annotated samples. Self-supervised learning can alleviate this problem by using unlabeled data to learn the image’s feature representation and then migrate to the downstream task. In this study, we use an encoder–decoder structure to construct a self-supervised learning architecture. In the encoding stage, the image mask is used to discard some of the image patches randomly, and the image’s feature representation can be learned from the remaining image patches. In the decoding stage, the lightweight decoder is used to recover the pixels of the original image patches according to the features learned in the encoding stage. We constructed a large-scale unlabeled training set using several public scene classification datasets and Gaofen-2 satellite data to train the self-supervised learning model. In the downstream task, we use the encoder structure with the masked image patches that have been removed as the backbone network of the scene classification task. Then, we fine-tune the pre-trained weights of self-supervised learning in the encoding stage on two open datasets with complex scene categories. The datasets include NWPU-RESISC45 and AID. Compared with other mainstream supervised learning methods and self-supervised learning methods, our proposed method has better performance than the most state-of-the-art methods in the task of remote sensing image scene classification.

Scene classification is of great significance to understand the semantics and extract the information of high spatial resolution remote sensing image. The bag-of-visual-words( BOVW) model is an effective method to understand the semantic content of images. It is widely used in scene classification of remote sensing image.The existed BOVW models usually use single feature or multiple features (such as spectrum, texture, and shape) to describe visual words. However, when considering multiple low-level feature fusion strategies, most methods only combine them by simple accumulation or concatenation which can not fully learn the relationship between different features.In this letter, a visual bag-of-words scene classifier based on regional covariance (RCOVBOVW) is proposed. This method can naturally fuse multiple related features, and the covariance calculation itself has the filtering ability, which can also reduce the dimension of the features and have high efficiency. Experiments have been conducted on two public and challenging datasets (UC Merced and NWPU-RESISC45), and the results show that our proposed method outperforms the most state-of-the-art methods of remote sensing image scene classification.

Intensive blooms of floating macroalgae have been widely reported during the past decades. Multi-spectral satellite imagery serves as an important data source for bloom monitoring, but its capacities are often hindered by the difficulties in accurate cloud or cloud shadow masking. Imperfect masking not only leads to a lack of bloom observations but also induces uncertainties in their biomass estimations. To reduce false detections and to retain more valid satellite measurements, the Feedback Attention Network (FANet) was applied to mask cloud and cloud shadow pixels on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Satisfactory performance was achieved on the images taken in the Caribbean Sea, where cloud and sun glint contaminations frequently occur. The overall cloud detection accuracy is ~ 96%, as referred to manually prepared “ground truth”. Cloud shadows are effectively detected while over-masking near sun glint regions is reduced. Compared to the commonly used SeaWiFS Data Analysis System (SeaDAS) cloud products, the FANet-derived cloud products retain 44% more valid MODIS measurements in summer 2021. Consequently, they consistently provide more macroalgal features on each single MODIS image. However, the monthly mean macroalgae biomass statistics derived from these two cloud masking approaches are consistently close despite their large differences in the number of valid measurements, suggesting the mean-value composite strategy can minimize the impact of missing observations and provide reliable long-term trends. The proposed cloud masking method is also applicable to other similar multispectral sensors (i.e., Visible Infrared Imaging Radiometer Suite (VIIRS)) for monitoring floating macroalgae in the global open oceans.

Few-shot remote sensing scene classification aims to accurately recognize unseen scene categories using only a scarce number of labeled samples, which has emerged as a research hotspot in the field of remote sensing image interpretation. However, remote sensing images intrinsically suffer from large intra-class variations, high inter-class similarities, and complex background interferences. Traditional few-shot learning methods typically perform feature metric learning in Euclidean space, making it difficult to capture the non-Euclidean geometric distribution characteristics of remote sensing features, and they often neglect the spatial structural information embedded in feature maps. To address these issues, this paper proposes a novel few-shot remote sensing scene classification method, termed ZSFS-RGBN, which integrates a Zigzag Scanning Feature Sequence with a Riemannian Geometric Barycenter Network. Specifically, ResNet12 is first employed as the backbone to extract deep convolutional feature maps from both the support and query sets. Second, a Zigzag scanning strategy is introduced to reorganize the two-dimensional feature maps into one-dimensional feature sequences, thereby effectively preserving the spatial locality and structural continuity of the features. Third, an autoregressive moving average (ARMA) model is constructed to characterize the spatial dependencies of the feature sequences, and its state parameters are mapped onto a symmetric positive definite (SPD) matrix manifold, enabling the deep semantic representations of remote sensing scenes in a non-Euclidean geometric space. Finally, a Riemannian geometric barycenter network is designed to learn the Riemannian barycenter of each category on the SPD manifold, where a joint loss function is introduced to simultaneously optimize intra-class compactness and inter-class separability. Comprehensive experiments are conducted on three public remote sensing scene datasets: NWPU-RESISC45, UC Merced Land-Use, and WHU-RS19. Experimental results demonstrate that the proposed method consistently outperforms several representative state-of-the-art approaches under both 5-way 1-shot and 5-way 5-shot settings. Furthermore, ablation studies verify the effectiveness of each component within the proposed framework.