The Sentinel-2 satellite, launched by the European Space Agency (ESA), offers extensive spatial coverage and has become indispensable in a wide range of remote sensing applications. However, it just has 12 spectral bands, making substances/objects identification less effective, not mentioning the varying spatial resolutions (10/20/60 m) across the 12 bands. If such a multi-resolution 12-band image can be computationally converted into a hyperspectral image with uniformly high resolution (i.e., 10 m), it significantly facilitates remote identification tasks. Though there are some spectral super-resolution methods, they did not address the multi-resolution issue on one hand, and, more seriously, they mostly focused on the CAVE-level hyperspectral image reconstruction (involving only 31 visible bands) on the other hand, greatly limiting their applicability in real-world remote sensing scenarios. We ambitiously aim to convert Sentinel-2 data directly into NASA’s AVIRIS-level hyperspectral image (encompassing up to 172 visible and near-infrared (NIR) bands, after ignoring those absorption/corruption ones). For the first time, this paper solves this specific super-resolution problem (highly ill-posed), allowing all historical Sentinel-2 data to have their corresponding high-standard AVIRIS counterparts. We achieve so by customizing a novel algorithm that introduces deep unfolding regularization andQ-quadratic-norm regularization into the so-called convex/deep (CODE) small-data learning criterion. Based on the derived spectral-spatial duality, the proposed interpretable COS2A algorithm demonstrates superior spectral super-resolution results across diverse land cover types, as validated through extensive experiments. Source codes: https://github.com/IHCLab/COS2A.

Multispectral unmixing (MU) is critical due to the inevitable mixed-pixel phenomenon caused by the limited spatial resolution of typical multispectral images (MSIs) in remote sensing. However, MU mathematically corresponds to the underdetermined unsupervised source separation (USS) problem, thus highly challenging, making it a daunting task for researchers to tackle it. Previous MU works all ignore the underdetermined issue and merely consider scenarios with more bands than sources. This work attempts to resolve the underdetermined issue by further conducting the light-splitting task using a network-inspired virtual prism, and as this task is challenging, we achieve so by incorporating very advanced quantum feature extraction techniques. We emphasize that the prism is virtual (allowing us to fix the spectral response as a simple deterministic matrix), so the virtual hyperspectral image (HSI) it generates does not need to correspond to some real hyperspectral sensor; in other words, it is good enough as long as the virtual HSI satisfies some fundamental properties of light splitting (e.g., nonnegativity and continuity). With the above virtual quantum prism, we know that the virtual HSI is expected to possess some desired simplex structure. This allows us to adopt the convex geometry (CG) to unmix the spectra, followed by downsampling the pure spectra back to the multispectral domain, thereby achieving MU. Experimental evidence shows the great potential of our MU algorithm, termed prism-inspired multispectral endmember extraction (PRIME).

Inland waterbody detection (IWD), which aims at identifying and mapping waterbodies such as rivers, lakes, and reservoirs, is critical for water resources management and agricultural planning. However, the development of high-fidelity IWD mapping technology remains unresolved. We aim to propose a practical solution using only the easily accessible delay-Doppler map (DDM) data provided by NASA’s Cyclone Global Navigation Satellite System (CYGNSS), which facilitates effective estimation of physical parameters on the Earth’s surface with high temporal resolution and wide spatial coverage. Specifically, as quantum deep network (QUEEN) has revealed its strong proficiency in addressing classification-like tasks, we encode the DDM using a customized transformer, followed by feeding the transformer-encoded DDM (tDDM) into a highly entangled QUEEN to distinguish whether the tDDM corresponds to a hydrological region. In recent literature, QUEEN has achieved outstanding performances in numerous challenging remote sensing tasks (e.g., hyperspectral restoration, change detection, and mixed noise removal, etc.), and its high effectiveness stems from the fundamentally different way it adopts to extract features (the so-called quantum unitary-computing features). The meticulously designed IWD-QUEEN retrieves high-precision river textures, such as those in Amazon River Basin in South America, demonstrating its superiority over traditional classification methods and existing global hydrography maps. IWD-QUEEN, together with its parallel quantum multihead scheme, works in a near-real-time manner (i.e., millisecond-level computing per DDM data). To broaden accessibility for users of traditional computers, we also provide the non-quantum counterpart of our method, called IWD-Transformer, thereby increasing the impact of this work. In terms of quantitative evaluation, IWD-QUEEN leads the IWD-Transformer by approximately 7% and 8% in F1-score and Cohen’s kappa, respectively, alluding the promising role of QUEEN in achieving high-performance detection. Source codes: https://github.com/IHCLab/IWD-QUEEN.

Super-resolving remotely acquired hyperspectral images, often with low resolution (LR), is a critical signal processing technique, as it greatly affects the subsequent material classification and identification tasks. An economical approach in the remote sensing area is to fuse the spatial details extracted from the high-resolution (HR) counterpart multispectral image into the LR hyperspectral image, thereby inferring the desired HR hyperspectral image. Convex analysis has been shown to be an effective tool for the fusion mission, but it often relies on sophisticated regularization schemes to tackle this challenging inverse problem. In the existing literature, the deep plug-and-play strategy was proposed for fast implementation of those sophisticated regularizers, but just approximately without convergence guarantees. Thus, we introduce deep learning (in an alternative approach) to tailor a simple convex regularizer for efficient super-resolution. Remarkably, though typical deep fusion methods can tackle non-linear effect presented in real hyperspectral data, they often rely on big data and sophisticated network structures, which are often time-consuming and resource-intensive. Instead, our deep regularizer just needs a small-data-driven simple network architecture that implies better stability and tractability; we achieve so by reconsidering the role of deep learning as simply to guide the convex algorithm to search the fusion solution, rather than directly serving as the final solution. The proposed convex deep image fusion (CODE-IF) algorithm, with all the closed-form algorithmic expressions derived, achieves state-of-the-art hyperspectral super-resolution performance.

Recently, numerous hyperspectral anomaly detection (HAD) methods have been proposed for broad and crucial applications. Among these, robust principal component analysis (RPCA) gains considerable attention in HAD, as it separates the matrix into global low-rank (LR) and sparse components, corresponding to the property of background and anomaly. However, RPCA solves the problem by treating hyperspectral imagery (HSI) as a matrix, but this approach alone cannot well-describe the local spatial texture information. In addition, a pixelwise detection method, collaborative representation detector (CRD), has been proposed, which exploits the vital piece of local information by assuming that background pixels can be composed of their neighbor pixels, while abnormal ones cannot. Although several CRD-based methods achieve promising HAD performances, they generally suffer from high computational costs due to the pixelwise optimization scheme. To overcome the aforementioned two limitations, we propose a novel algorithm, SuperRPCA. First, we improve CRD to superpixelwise calculation and reconstruct the background with a simplex-based algebraic solution. Subsequently, the rebuilt background is tailored to serve as a convex regularizer and integrated into RPCA. Besides, the regularizer inherently possesses an LR property, adeptly substituting the nuclear norm in traditional RPCA and hence significantly reducing computational costs. SuperRPCA demonstrates easily identifiable visual qualities and state-of-the-art quantitative performance with all the closed-form algorithmic expressions explicitly derived.

Hyperspectral anomaly detection (HAD) is a crucial task that aims to classify the given image into abnormal pixels and background pixels. Besides, the classification boundary between the abnormal pixels and the background pixels is implicit, making HAD a challenging problem. An existing method for anomaly detection is proposed based on collaborative representation. Since the method performs the detection on each pixel, it is not computationally efficient. To reduce the computational cost, we develop a new method based on collaborative representation. First, superpixel segmentation is utilized to cluster the image. Then, we perform the collaborative representation on each superpixel to obtain a rough detection result. According to the preliminary result, a threshold is automatically calculated to classify potential abnormal superpixels and background superpixels. At last, the boundaries of abnormal superpixels are refined to yield a more accurate detection result. In the real data experiments, we show that our method has satisfactory visual qualities and state-of-the-art quantitative performance.

Acquiring high-spatial-resolution (HSR) images at high temporal sampling rate is not economical and even not achievable using contemporary multispectral satellite imaging hardware. An alternative is to fuse a set of HSR images acquired at low sampling rate, with another set of low-spatial-resolution images acquired at high sampling rate, and such fusion problem is referred to as spatiotemporal super-resolution (STSR). We mitigate the ill-posedness of the STSR problem by incorporating the image self-similarity prior (S2P), which is the key behind the design of several state-of-the-art imaging inverse problems. Unlike most super-resolution works in the computer vision area, our method does not rely on collecting big data. Instead, we propose a fully unsupervised STSR method by adopting the popular strategy in machine learning, known as plug-and-play optimization, and by carefully refining the required matrix computation/inversion. We term our method as STSRS2P, whose superiority and low computational complexity will be experimentally verified.