A mangrove mapping (MM) algorithm is an essential classification tool for environmental monitoring. The recent literature shows that compared with other index-based MM methods that treat pixels as spatially independent, convolutional neural networks (CNNs) are crucial for leveraging spatial continuity information, leading to improved classification performance. In this work, we go a step further to show that quantum features provide radically new information for CNN to further upgrade the classification results. Simply speaking, CNN computes affine-mapping features, while quantum neural network (QNN) offers unitary-computing features, thereby offering a fresh perspective in the final decision-making (classification). To address the challenging MM problem, we design an entangled spatial-spectral quantum feature extraction module. Notably, to ensure that the quantum features contribute genuinely novel information (unaffected by traditional CNN features), we design a separate network track consisting solely of quantum neurons with built-in interpretability. The extracted pure quantum information is then fused with traditional feature information to jointly make the final decision. The proposed quantum-empowered deep network (QEDNet) is very lightweight, so the improvement does come from the cooperation between CNN and QNN (rather than parameter augmentation). Extensive experiments will be conducted to demonstrate the superiority of QEDNet.

Due to the intensifying impacts of extreme climate changes, drought forecasting (DF), which aims to predict droughts from historical meteorological data, has become increasingly critical for monitoring and managing water resources. Despite the spatial coherence of drought conditions, benchmark deep learning-based DF models predict each region independently while ignoring the neighboring spatial information. Using the Standardized Precipitation Evapotranspiration Index (SPEI), we designed and trained a novel and transformative spatially-aware DF neural network, which effectively captures local interactions among neighboring regions, resulting in enhanced spatial coherence and prediction accuracy. As DF also requires sophisticated temporal analysis, the Mamba network, recognized as the most accurate and efficient existing time-sequence modeling, was adopted to extract temporal features from short-term time frames. We also adopted quantum neural networks (QNN) to entangle the spatial features of different time instances, leading to refined spatiotemporal features of seven different meteorological variables for effectively identifying short-term climate fluctuations. In the last stage of our proposed SPEI-driven quantum spatially-aware Mamba network (SQUARE-Mamba), the extracted spatiotemporal features of seven different meteorological variables were fused to achieve more accurate DF. Validation experiments across El Niño, La Niña, and normal years demonstrated the superiority of the proposed SQUARE-Mamba, remarkably achieving an average improvement of more than 9.8% in the coefficient of determination index (R2) compared to baseline methods, thereby illustrating the promising roles of the temporal quantum entanglement and Mamba temporal analysis to achieve more accurate DF. Notably, the integration of QNN further upgrades the naive Mamba baseline by over 2.7% in R2on average, highlighting the model’s sensitivity to transient climate variations.

Hyperspectral dehazing (HyDHZ) has become a crucial signal processing technology to facilitate the subsequent identification and classification tasks, as the airborne visible/infrared imaging spectrometer (AVIRIS) data portal reports a massive portion of haze-corrupted areas in typical hyperspectral remote sensing images. The idea of inverse problem transform (IPT) has been proposed in recent remote sensing literature in order to reformulate a hardly tractable inverse problem (e.g., HyDHZ) into a relatively simple one. Considering the emerging spectral super-resolution (SSR) technique, which spectrally upsamples multispectral data to hyperspectral data, we aim to solve the challenging HyDHZ problem by reformulating it as an SSR problem. Roughly speaking, the proposed algorithm first automatically selects some uncorrupted/informative spectral bands, from which SSR is applied to spectrally upsample the selected bands in the feature space, thereby obtaining a clean hyperspectral image (HSI). The clean HSI is then further refined by a deep transformer network to obtain the final dehazed HSI, where a global attention mechanism is designed to capture nonlocal information. There are very few HyDHZ works in existing literature, and this article introduces the powerful spatial–spectral transformer into HyDHZ for the first time. Remarkably, the proposed transformer-driven IPT-based HyDHZ (T2HyDHZ) is a blind algorithm without requiring the user to manually select the corrupted region. Extensive experiments demonstrate the superiority of T2HyDHZ with less color distortion.

Mangrove mapping (MM) is a critical satellite remote sensing technology since mangrove forests have a large capacity for carbon storage among the blue carbon ecosystems. However, we surprisingly found that benchmark MM methods are all index-based ones, completely ignoring the spatially neighboring information on the one hand and quite sensitive to the threshold setting on the other hand. Deep learning has been proven to be an effective solution for incorporating the desired spatial information, but the induced big data collection of MM is difficult and time-consuming, especially for ground-truth labeling; this would be the reason why benchmark methods are all index-based ones. To solve the dilemma, we introduce convex analysis into deep learning, thereby achieving small-data learning. The proposed algorithm is hence termed convex deep MM (CODE-MM), mainly developed for the Sentinel-2 satellite, which is the mainstream satellite for the MM mission, as it involves those key green/infrared bands for characterizing mangrove multispectral signatures. We also generalize our CODE-MM to test the hyperspectral satellite data, which should be the trend for various classification missions in the future due to its strong material identifiability. Simply speaking, CODE-MM first infers a rough mangrove signature for designing a Siamese deep regularizer, which is then plugged into a convex criterion customized for the mapping task. We implement the convex criterion by deriving closed-form solutions for all the algorithmic steps, ensuring computational efficiency. Extensive experiments demonstrate that CODE-MM is insensitive to the threshold setting and yields state-of-the-art performance in accurate mangrove forest mapping.

Alternating direction method of multipliers (ADMM) and adaptive moment estimation (ADAM) are two optimizers of paramount importance in convex optimization (CO) and deep learning (DL), respectively. Numerous state-of-the-art algorithms for solving inverse problems are achieved by carefully designing a convex criterion, typically composed of a data-fitting term and a regularizer. Even when the regularizer is convex, its mathematical form is often sophisticated, hence inducing a math-heavy optimization procedure and making the algorithm design a daunting task for software engineers. Probably for this reason, people turn to solve the inverse problems via DL, but this requires big data collection, quite time-consuming if not impossible. Motivated by these facts, we propose a new framework, termed as ADMM-ADAM, for solving inverse problems. As the key contribution, even just with small/single data, the proposed ADMM-ADAM is able to exploit DL to obtain a convex regularizer of very simple math form, followed by solving the regularized criterion using simple CO algorithm. As a side contribution, a state-of-the-art hyperspectral inpainting algorithm is designed under ADMM-ADAM, demonstrating its superiority even without the aid of big data or sophisticated mathematical regularization.

Unlike RGB images available almost everywhere, hyperspectral remote sensing images are not easily obtainable, making big data collection often infeasible for a target task. This would prevent the adoption of the powerful deep learning technology from being applied in solving some challenging problems, e.g., recovering the missing part of a hyperspectral data cube (viewed as a 3-way tensor). This fact induces a series of research works to investigate how to augment the small data, for example, by rotation. We just accept the fact that only small data is available, and propose a radically different view (without augmentation) to address the lacking of big data in hyperspectral remote sensing. Specifically, we show that a deep neural network trained using just small data can still output some useful information to be used in designing regularizer for the ill-posed hyperspectral tensor completion (HTC) problem. Such regularizer is made simple and convex, thereby allowing us to design a fast convex optimization based HTC algorithm, whose superiority is experimentally demonstrated.