Remote sensing image super-resolution (SR) aims to reconstruct high-resolution remote sensing images from low-resolution inputs, thereby addressing limitations imposed by sensors and imaging conditions. However, the inherent characteristics of remote sensing images, including diverse ground object types and complex details, pose significant challenges to achieving high-quality reconstruction. Existing methods typically employ a uniform structure to process various types of ground objects without distinction, making it difficult to adapt to the complex characteristics of remote sensing images. To address this issue, we introduce a Mixture of Experts (MoE) model and design a set of heterogeneous experts. These experts are organized into multiple expert groups, where experts within each group are homogeneous while being heterogeneous across groups. This design ensures that specialized activation parameters can be employed to handle the diverse and intricate details of ground objects effectively. To better accommodate the heterogeneous experts, we propose a multi-level feature aggregation strategy to guide the routing process. Additionally, we develop a dual-routing mechanism to adaptively select the optimal expert for each pixel. Experiments conducted on the UCMerced and AID datasets demonstrate that our proposed method achieves superior SR reconstruction accuracy compared to state-of-the-art methods. The code will be available at https://github.com/Mr-Bamboo/MFG-HMoE.

High-resolution (HR) remote sensing imagery plays a vital role in a wide range of applications, including urban planning and environmental monitoring. However, due to limitations in sensors and data transmission links, the images acquired in practice often suffer from resolution degradation. Remote Sensing Image Super-Resolution (RSISR) aims to reconstruct HR images from low-resolution (LR) inputs, providing a cost-effective and efficient alternative to direct HR image acquisition. Existing RSISR methods primarily focus on low-level characteristics in pixel space, while neglecting the high-level understanding of remote sensing scenes. This may lead to semantically inconsistent artifacts in the reconstructed results. Motivated by this observation, our work aims to explore the role of high-level semantic knowledge in improving RSISR performance. We propose a Semantic-Guided Super-Resolution framework, SeG-SR, which leverages Vision-Language Models (VLMs) to extract semantic knowledge from input images and uses it to guide the super resolution (SR) process. Specifically, we first design a Semantic Feature Extraction Module (SFEM) that utilizes a pretrained VLM to extract semantic knowledge from remote sensing images. Next, we propose a Semantic Localization Module (SLM), which derives a series of semantic guidance from the extracted semantic knowledge. Finally, we develop a Learnable Modulation Module (LMM) that uses semantic guidance to modulate the features extracted by the SR network, effectively incorporating high-level scene understanding into the SR pipeline. We validate the effectiveness and generalizability of SeG-SR through extensive experiments: SeG-SR achieves state-of-the-art performance on three datasets, and consistently improves performance across various SR architectures. Notably, for the ×4 SR task on the UCMerced dataset, it attained a PSNR of 29.3042 dB and an SSIM of 0.7961. Codes can be found at https://github.com/Mr-Bamboo/SeG-SR.

Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation. The recent advancements of Convolutional Neural Networks (CNNs) and Transformers have markedly enhanced classification accuracy. Nonetheless, remote sensing scene classification remains a significant challenge, especially given the complexity and diversity of remote sensing scenarios and the variability of spatiotemporal resolutions. The capacity for whole-image understanding can provide more precise semantic cues for scene discrimination. In this paper, we introduce RSMamba, a novel architecture for remote sensing image classification. RSMamba is based on the State Space Model (SSM) and incorporates an efficient, hardware-aware design known as the Mamba. It integrates the advantages of both a global receptive field and linear modeling complexity. To overcome the limitation of the vanilla Mamba, which can only model causal sequences and is not adaptable to two-dimensional image data, we propose a dynamic multi-path activation mechanism to augment Mamba’s capacity to model non-causal data. Notably, RSMamba maintains the inherent modeling mechanism of the vanilla Mamba, yet exhibits superior performance across multiple remote sensing image classification datasets,e.g., F1 scores of 95.25, 92.63, and 95.18 on the UC Merced, AID, and RESISC45 classification datasets respectively, exceeding those of concurrent Vim and VMamba. This indicates that RSMamba holds significant potential to function as the backbone of future visual foundation models. The code is available at https://github.com/KyanChen/RSMamba.

Remote Sensing Image Change Captioning (RSICC) aims to describe surface changes between multi-temporal remote sensing images in language, including the changed object categories, locations, and dynamics of changing objects (e.g., added or disappeared). This poses challenges to spatial and temporal modeling of bi-temporal features. Despite previous methods progressing in the spatial change perception, there are still weaknesses in joint spatial-temporal modeling. To address this, in this paper, we propose a novel RSCaMa model, which achieves efficient joint spatial-temporal modeling through multiple CaMa layers, enabling iterative refinement of bi-temporal features. To achieve efficient spatial modeling, we introduce the recently popular Mamba (a state space model) with a global receptive field and linear complexity into the RSICC task and propose the Spatial Difference-aware SSM (SD-SSM), overcoming limitations of previous CNN- and Transformer-based methods in the receptive field and computational complexity. SD-SSM enhances the model’s ability to capture spatial changes sharply. In terms of efficient temporal modeling, considering the potential correlation between the temporal scanning characteristics of Mamba and the temporality of the RSICC, we propose the Temporal-Traversing SSM (TT-SSM), which scans bi-temporal features in a temporal cross-wise manner, enhancing the model’s temporal understanding and information interaction. Experiments validate the effectiveness of the efficient joint spatial-temporal modeling and demonstrate the outstanding performance of RSCaMa and the potential of the Mamba in the RSICC task. Additionally, we systematically compare three different language decoders, including Mamba, GPT-style decoder, and Transformer decoder, providing valuable insights for future RSICC research. The code will be available at https://github.com/Chen-Yang-Liu/RSCaMa.

Hyperspectral remote sensing images (HSIs) have unique advantages in urban planning, precision agriculture, and ecology monitoring since they provide rich spectral information. However, hyperspectral imaging usually suffers from low spatial resolution and high cost, which limits the wide application of hyperspectral data. Spectral super-resolution provides a promising solution to acquire hyperspectral images with high spatial resolution and low cost, taking RGB images as input. Existing spectral super-resolution methods utilize neural networks following a single-shot framework, i.e., final results are obtained by one-stage spectral super-resolution, which struggles to capture and model the complex relationships between spectral bands. In this article, we propose a spectral-cascaded diffusion model (SCDM), a coarse-to-fine spectral super-resolution method based on the diffusion model. The diffusion model fits the real data distribution through stepwise denoising, which is naturally suitable for modeling rich spectral information. We cascade the diffusion model in the spectral dimension to gradually refine the spectral trends and enrich spectral information of the pixels. The cascade solves the highly ill-posed problem of spectral super-resolution step-by-step, mitigating the inaccuracies of previous single-shot approaches. To better utilize the potential of the diffusion model for spectral super-resolution, we design image condition mixture guidance (ICMG) to enhance the guidance of image conditions and progressive dynamic truncation (PDT) to limit cumulative errors in the sampling process. Experimental results demonstrate that our method achieves state-of-the-art performance in spectral super-resolution. Codes can be found athttps://github.com/Mr-Bamboo/SCDM.

Hyperspectral image synthesis overcomes the limitations of imaging sensors and enables low-cost acquisition of hyperspectral images with high spatial resolution. Using RGB as a conditional input for hyperspectral generation is promising and valuable, as it can leverage abundant existing multispectral/RGB images without the intervention of hyperspectral sensors. However, most existing generation methods follow one-to-one mapping frameworks and ignore generation diversity. In addition, the current evaluation metrics of hyperspectral generation are based on the similarity with the reference image, which cannot reflect the diversity of the generated spectra. In this paper, we propose a novel method for diverse hyperspectral remote sensing image generation based on the diffusion model. The diffusion model uses a denoising model to gradually remove noise from the normal distribution and generates the hyperspectral data step-by-step with the conditional RGB image as input. To address the high-dimensional noise prediction problem caused by a large number of bands in the hyperspectral image, we introduce a conditional VQGAN that maps the high-dimension hyperspectral data into a low-dimension latent space and conduct the diffusion process in the latent space. The latent-diffusion process makes the diffusion process faster and more stable. The conditional VQGAN decodes hyperspectral images from the latent code generated by diffusion, with the conditional RGB image as input, which restricts the diversity to a specific object distribution. We also design two new metrics to evaluate the generation spectral diversity. Experiments on the IEEEgrss_dfc_2018dataset demonstrate that our method can synthesize highly diverse hyperspectral data. In addition, the rationality of the proposed metrics is also verified.

Generating photo-realistic remote sensing images conditioned on semantic masks has many practical applications like image editing, detecting deep fake geography, and data augmentation. Although previous methods achieved high-quality synthesis results for natural images like faces and everyday objects, they still underperform in remote sensing scenarios in terms of both visual fidelity and diversity. The high data imbalance and high semantic similarity of remote sensing object categories make the semantic synthesis of remote sensing images more challenging than natural images. To tackle these challenges, we propose a novel method named Conducted Semantic EmBedding GAN (CSEBGAN) for semantic-controllable remote sensing image synthesis. The proposed method decouples different semantic classes into independent Semantic Embeddings, which explores the regularities between classes to improve visual fidelity and naturally supports semantic-level. We further introduce a novel tripartite cooperation adversarial training scheme that involves a conductor network to provide fine-grained semantic feedback for the generator. We also show that the proposed semantic image synthesis method can be utilized as an effective data augmentation approach on improving the performance of the downstream remote sensing image segmentation tasks. Extensive experiments show the superiority of our method compared with the state-of-the-art image synthesis methods.