Remote sensing image captioning is a multimodal foundation task for fine-grained understanding of remote sensing images. However, remote sensing images contain complex scenes and rich objects, it is very challenging to accurately describe the objects in the scene with their attributes and dependencies. To address these issues, the article proposes a novel scale-aware prompting with optimal transport (SPOT) to learn effective multiscale features under diverse scenes, and to build fine-grained cross-modal alignment between semantic features and linguistic words during caption generation. Specifically, a scale-aware prompt extractor is constructed to explore feature integrations in complex scenes through learning prompts that query multi-scale features, and to enhance the representation of attributes and dependencies for objects by embedding positional relations. Besides, a fine-grained cross-modal alignment is designed to dynamically match image feature representations and textual semantics through optimal transport. Through the above manner, the model can learn effective language-aligned feature representations for caption generation. Finally, a caption Transformer with causal self-attention is introduced to generate accurate captions for remote sensing scenes. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on three public datasets, with the superiority of the proposed method further demonstrated by ablating the role of each component.

Prominent domain discrepancies in Remote Sensing Images (RSIs), such as sensor types, geographical patterns, and land usage, significantly hinder the research and practical applications of cross-scene land classification. Unsupervised Domain Adaptation (UDA) fully exploits domain invariance between labelled source and unlabelled target domain, which alleviates the challenge of inaccurate land classification due to lack of labels in RSIs. However, most existing UDA methods for RSIs semantic segmentation are insufficient in exploring cross-domain features and have difficulty in modelling fine-grained domain-invariant features between inter-class. In this paper, we propose a novel self-training UDA method named Hierarchical Prototype Learning (HPL), which learns the inherent domain-wise consistency and class-wise invariance through progressive exploration of the prototypy, significantly alleviating the ambiguity in uncertain regions. HPL mainly consists of Domain-wise Progressive Prototype Interaction (DPPI) and Class-wise Dynamic Prototype Collaboration (CDPC). DPPI and CDPC specialize in hierarchically building prototype interaction architectures tailored to domain-wise alignment and class-wise calibration, respectively. This design not only mitigates the sensitivity to cross-domain scenarios but also allows for precise correction of uncertain regions. Furthermore, CDPC exhibits the capacity for pixel-level category restoration and promotes the correct and fine-grained updating of pseudo-labels. Extensive Experiments on two public datasets and a private self-build dataset demonstrate the superiority of HPL over other state-of-the-art methods for UDA semantic segmentation of RSIs.

Domain-adaptive Remote Sensing Images (RSIs) semantic segmentation mitigates the overfitting problem that affects the effectiveness of segmentation, which results from the scarcity of high-quality labels and the cross-domain styles of ground objects. The effectiveness of domain adaptive segmentation remains suboptimal in complex scenarios due to inadequate exploitation of latent geographic knowledge. Consequently, inter-class ambiguity and boundary agnostic are further exacerbated under cross-domain transfer scenarios. To address this issue, we first devise a deep geospatio-semantic guided network named DSSAL, which comprehensively investigates the potential spatial relationship and semantic correlation between classes of RSIs by geospatial aware interaction and geosemantic aware interaction, respectively. To mitigate class-wise cognitive deviation in the unlabeled domain, DSSAL-DA is developed to further enhance the segmentation effect with the spatio-semantic domain alignment module in manifold cross domain tasks. Furthermore, a pseudo-labels consistency filter is developed for DSSAL-DA to ensure reliability in self-training through cross-view consistency verification. Extensive experiments on two public datasets and a private dataset demonstrate the superiority of DSSAL and DSSAL-DA over the state-of-the-art methods for UDA semantic segmentation of RSIs.

Benefiting from the high spectral resolution, hyperspectral image (HSI) owns the property of discriminating material. However, the spatial resolution of HSIs is limited by the hardware and, thus, makes HSI super-resolution (SR) a necessary and hot topic. Existed HSI SR methods rarely consider the high-frequency edge information, resulting in unsatisfactory quality of spatial reconstruction. To address the problem mentioned above, we propose a novel method for HSI SR named attention-driven dual feature guidance net (AD-DFGNet), which makes full use of the spatial–spectral information. Specifically, AD-DFGNet mainly consists of three modules, which are shallow feature extraction, DFG blocks, and upsampling fusion module. First, the three adjacent bands of an HSI cube are selected sequentially as one of the inputs to the proposed AD-DFGNet. Their feature dimension is upgraded through shallow feature extraction. Second, middle band of three adjacent bands is used as the other input to DFG block, and it makes the network focus on spatial feature extraction by DFG, which includes feature aggregation guidance (FAG) and gradient–texture attention (GTA) guidance. Then, the output features of DFG blocks are upsampled and fused, in turn, to obtain the single-band SR result. Finally, the SR results of the whole HSI are obtained from the sequential concatenation of each single band. In addition, the proposed method reduces the size of the generated model and makes it possible to apply on different datasets. The efficiency of the AD-DFGNet is validated on three publicly available hyperspectral datasets and yields the state-of-the-art performance.

In this paper, a novel hyperspectral image (HSI) anomaly detection method is proposed. This method is inspired by three ideas. First, the spatial resolution of the HSIs is sacrificed for their spectral information. Structural information of the HSIs tends to be smooth and distorts from that of the real scene. Second, with a loose false alarm rate, it is not difficult to pick out all the anomalies. Third, gradients of these probable anomalies can be transformed to enhance the spatial information of the HSI. Meanwhile, it is desirable that the enhanced HSI could be detected more precisely. Three modules are designed with respect to these three ideas, which are locating the probable pixels, local gradient guidance, and the anomaly detection for the enhanced HSI. Specifically, some probable anomalies are firstly selected. Secondly, the gradients of these selected pixels are transformed and utilized to guide the spatial enhancement for the HSI locally. Finally, the final detection is implemented on the enhanced HSI. Experimental results obtained on four real HSIs demonstrate the effectiveness of the proposed method.