Cross-domain hyperspectral image (HSI) classification (HSIC) addresses the challenge of real-time labeling of new regions. To mitigate the performance decline caused by unseen classes, a few-shot learning (FSL) method is used. However, these methods fail to fully consider the problem of sample scarcity and classification imbalance due to FSL methods. In addition, the issue of category confusion stemming from localized spectral fluctuations within the same class is commonly overlooked. To solve these problems, a geographical dual-prior guided few-shot network (Gprior-FSN) is proposed. In Gprior-FSN, combining prior knowledge of the first law of geography, a geographical prior guided bicorrelated (G-B) sample enhancement mechanism is proposed which includes geospatially correlated enhancement (GCE) and spectral feature correlated enhancement (SFCE). GCE uses a hierarchical sampling strategy to tackle the inherent imbalance problem for FSL methods. Subsequently, GCE mitigates sample scarcity via neighborhood sample expansion while identifying candidate pseudosamples with geospatial correlation. To make the acquired pseudosamples of the same category bicorrelated in both geospatial and spectral features, G-B combining spectral feature clustering and probabilistic statistics mechanism is designed. Inspired by the second law of geography, Gprior-FSN uses a spatial constraint mechanism to effectively enhance intraclass similarity by reducing spatial local heterogeneity, while improving global interclass discriminability. Finally, to further capture representative spatial-spectral feature, a weighted dual feature fusion network is designed. Experimental results from three distinct HSI datasets show that Gprior-FSN outperforms advanced HSIC methods in both efficiency and accuracy. In addition, the Gprior-FSN demonstrates strong generalization performance on real GF-5 image.

For large-scale mapping applications, cross-domain hyperspectral image classification (HSIC) has emerged as a highly promising research area. However, the classification accuracy decreased significantly when unseen classes emerged. Few shot learning (FSL) methods are adopted in cross-domain HSIC methods to address this problem. Despite this, existing cross-domain HSIC methods still have three key issues that hamper their classification capabilities: 1) previous works struggle to balance incorporating distinctive intradomain knowledge and managing model complexity in the face of significant domain representation differences; 2) previous works inadequately consider the limited capture capacity of interdomain intrinsic mutually invariant structures; and 3) previous works fail to capture the distinct characteristics of both head categories (e.g., urban buildings) and tail categories (e.g., urban corn) simultaneously when applying FSL to deal with unseen classes problem. In this article, we propose a cycle-resemblance few-shot transformation (CF-Trans) network to effectively handle the aforementioned challenges by integrating intradomain distinctiveness with interdomain invariance. To facilitate efficient intradomain feature aggregation for HSI, a novel lightweight intradomain attentive network is introduced. Different from previous works, to reduce the negative impact caused by inaccurate classifier predictions, from the perspective of interdomain knowledge transformation, a cycle-resemblance adversarial network is designed to capture the intrinsic mutually invariant structures. A dynamic label expansion mechanism is designed to capture the distinctive intradomain features of the head and tail classes. Experimental results on six HSI datasets including agricultural, rural-urban and urban datasets show the remarkably performance of our network.

Recently, high-resolution (HR) remote sensing images have attracted increasing attention in a number of tasks. Super-resolution (SR) is an efficient method to obtain high-resolution remote sensing images. Due to the influence of imaging distances and angles, remote sensing images significantly differ from natural images in terms of land cover element distribution, ground object scale and scene complexity. This poses a challenge for capturing global and local low- and high-frequency and restoring fine image details for remote sensing image SR. In this article, a local-global context-aware generative dual-region adversarial network (LGC-GDAN) is designed for remote sensing image SR. It is composed of dual region-level discriminators and a dual-path generator with a context-aware network and an edge-assisted network. To capture global and local low- and high-frequency information, the global-aware self-attention (GAS) mechanism and local-aware self-attention (LAS) mechanism are introduced into the context-aware network. The GAS mechanism combines high-pass and low-pass filtering for long-range similarity feature, while LAS uses local aggregation for fine-level feature. The LR images and the corresponding edge maps are input to the edge-assisted network to extract the detailed geometric structure. To address small ground object and complex ground scenes, conventional image-level discriminators exhibit limited performance in capturing detailed information. Unlike previous discriminator, a region-level discriminator is designed to obtain the real/fake label of each local region. Moreover, two task-driven loss functions are designed to produce diverse images for further scene classification. The experiments undertaken on several remote sensing datasets demonstrate that LGC-GDAN outperforms the other state-of-the-art methods.

Deep learning techniques have been widely applied to hyperspectral image (HSI) classification and have achieved great success. However, the deep neural network model has a large parameter space and requires a large number of labeled data. Deep learning methods for HSI classification usually follow a patchwise learning framework. Recently, a fast patch-free global learning (FPGA) architecture was proposed for HSI classification according to global spatial context information. However, FPGA has difficulty in extracting the most discriminative features when the sample data are imbalanced. In this article, a spectral-spatial-dependent global learning (SSDGL) framework based on the global convolutional long short-term memory (GCL) and global joint attention mechanism (GJAM) is proposed for insufficient and imbalanced HSI classification. In SSDGL, the hierarchically balanced (H-B) sampling strategy and the weighted softmax loss are proposed to address the imbalanced sample problem. To effectively distinguish similar spectral characteristics of land cover types, the GCL module is introduced to extract the long short-term dependency of spectral features. To learn the most discriminative feature representations, the GJAM module is proposed to extract attention areas. The experimental results obtained with three public HSI datasets show that the SSDGL has powerful performance in insufficient and imbalanced sample problems and is superior to other state-of-the-art methods.

High spatial resolution remote sensing images (HSR-RSIs) are critical to providing fine land cover/land use information for scene classification. The global low spatial resolution remote sensing images (LSR-RSIs) can be easily obtained at present, whereas it is still a challenge to acquire large-scale HSR-RSIs. In this paper, an algorithmic-based architecture is proposed to improve the spatial resolution of RSIs beyond the limits of imaging sensors. The generative adversarial network-based end-to-end multi-task learning architecture (EML-GAN) is proposed for LSR-RSIs super-resolution reconstruction and scene classification simultaneously. In EML-GAN, the generator network is used to recover the fine geometric structures of LSR-RSIs by fusing the deep contextual, structure, and edge information. In addition, the discriminator network is designed to predict the scene label and distinguish the real/fake of the input data. The proposed architecture is evaluated on a public dataset and two self-made dataset. The experimental results show that the proposed architecture improves the visual effect and classification performance of LSR-RSIs.

Change detection is one of the main tasks in remote sensing field, and is essential for the accurate processing and understanding of the available earth observation data. However, most of the works focus on traditional binary change detection, without considering the change classes information. To make use of the semantic information and analyze change classes comprehensively, we proposed a Siamese Global Learning Framework (Siam-GL) for multi-class change detection. In Siam-GL, the global hierarchically (G-H) sampling strategy is designed to address the imbalanced training sample problem. In addition, the change mask was created and designed to distinguish the changed object for bi-temporal remote sensing images simultaneously. The proposed Siam-GL framework was validated on the Guangzhou dataset of two high spatial resolution remote sensing images in 2013 and 2015, and achieved a better performance than the other state-of-the art methods.