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.

The recent advancement of generative foundational models has ushered in a new era of image generation in the realm of natural images, revolutionizing art design, entertainment, environment simulation, and beyond. Despite producing high-quality samples, existing methods are constrained to generating images of scenes at a limited scale. In this paper, we present MetaEarth - a generative foundation model that breaks the barrier by scaling image generation to a global level, exploring the creation of worldwide, multi-resolution, unbounded, and virtually limitless remote sensing images. In MetaEarth, we propose a resolution-guided self-cascading generative framework, which enables the generating of images at any region with a wide range of geographical resolutions. To achieve unbounded and arbitrary-sized image generation, we design a novel noise sampling strategy for denoising diffusion models by analyzing the generation conditions and initial noise. To train MetaEarth, we construct a large dataset comprising multi-resolution optical remote sensing images with geographical information. Experiments have demonstrated the powerful capabilities of our method in generating global-scale images. Additionally, the MetaEarth serves as a data engine that can provide high-quality and rich training data for downstream tasks. Our model opens up new possibilities for constructing generative world models by simulating Earthâs visuals from an innovative overhead perspective.

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.