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.

Abundant spectral information endows unique advantages of hyperspectral remote sensing images in target location and recognition. Target detection techniques locate materials or objects of interest from hyperspectral images with given prior target spectra, and have been widely used in military, mineral exploration, ecological protection, etc. However, hyperspectral target detection is a challenging task due to high-dimension data, spectral changes, spectral mixing, and so on. To this end, many methods based on optimization and machine learning have been proposed in the past decades. In this paper, we review the representatives of hyperspectral image target detection methods and group them into seven categories: hypothesis testing-based methods, spectral angle-based methods, signal decomposition-based methods, constrained energy minimization (CEM)-based methods, kernel-based methods, sparse representation-based methods, and deep learning-based methods. We then comprehensively summarize their basic principles, classical algorithms, advantages, limitations, and connections. Meanwhile, we give critical comparisons of the methods on the summarized datasets and evaluation metrics. Furthermore, the future challenges and directions in the area are analyzed.

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 IEEE grss_dfc_2018 dataset demonstrate that our method can synthesize highly diverse hyperspectral data. In addition, the rationality of the proposed metrics is also verified.

Hyperspectral remote sensing images 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 paper, we propose 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 the state-of-the-art performance in spectral super-resolution. Codes can be found at https://github.com/Mr-Bamboo/SCDM.

Referring Remote Sensing Image Segmentation (RRSIS) facilitates flexible scene analysis by leveraging vision-language collaborative interpretation. However, conventional coupled frameworks typically perform pixel decoding after cross-modal fusion, conflating target localization (“where”) with boundary delineation (“how”). While recent decoupled approaches utilizing foundation models (e.g., SAM) attempt to separate these tasks, they predominantly rely on reductionist “box-to-mask” or “prompt-to-mask” pipelines. Such simple interfaces compress rich referring expressions into simplistic geometric constraints, severing semantic consistency and failing to leverage open-vocabulary visual-semantic alignment, particularly in multi-entity scenarios. To address these limitations, we propose RSRefSeg 2, an framework that logically reformulates the workflow into sequential subtasks of “slack localization” and “refined segmentation.” Central to our approach is a novel cascaded second-order referring prompter, designed to construct a robust semantic bridge between CLIP’s open-world understanding and SAM’s segmentation capabilities. Specifically, we introduce an orthogonal subspace decomposition mechanism that separates text embeddings into complementary components. This enables implicit cascaded reasoning to isolate target attributes from background noise: first performing slack localization via cross-modal interaction to identify potential regions, and subsequently generating refined prompts to guide SAM’s precise delineation. Furthermore, we incorporate parameter-efficient tuning to align natural image priors with remote sensing domains. Extensive experiments on RefSegRS, RRSIS-D, and RISBench demonstrate that RSRefSeg 2 significantly outperforms state-of-the-art methods, achieving an approximate 3% improvement in gIoU while offering superior diagnostic interpretability. The code is available at: https://github.com/KyanChen/RSRefSeg2.