Despite of various approaches proposed to smooth the hyperspectral images (HSIs) before feature extraction, the efficacy is still affected by the noise, even using the corrected dataset with the noisy and water absorption bands discarded. In this study, a novel spectral-spatial feature mining framework, Multiscale Superpixelwise Prophet Model (MSPM), is proposed for noise-robust feature extraction and effective classification of the HSI. The prophet model is highly noise-robust for deeply digging into the complex structured features thus enlarging interclass diversity and improving intraclass similarity. First, the superpixelwise segmentation is produced from the first three principal components of an HSI to group pixels into regions with adaptively determined sizes and shapes. A multiscale prophet model is utilized to extract the multiscale informative trend components from the average spectrum of each superpixel. Taking the multiscale trend signal as the input feature, the HSI data are classified superpixelwisely, which is further refined by a majority vote based decision fusion. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and robustness of our MSPM model when benchmarked with eleven state-of-the-art algorithms, including six spectral-spatial methods and five deep learning ones. Besides, MSPM also shows superiority under limited training samples, due to the combined strategies of superpixelwise fusion and multiscale fusion. Our model has provided a useful solution for noise-robust feature extraction as it achieves superior HSI classification even from the uncorrected dataset without prefiltering the water absorption and noisy bands.

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective management of the disease and control of its spread. We develop and validate a novel intelligent computational model to predict epidemiological trends of COVID-19, with the model parameters enabling an evaluation of the impact of NPIs. By representing the number of daily confirmed cases (NDCC) as a time-series, we assume that, with or without NPIs, the pattern of the pandemic satisfies a series of Gaussian distributions according to the central limit theorem. The underlying pandemic trend is first extracted using a singular spectral analysis (SSA) technique, which decomposes the NDCC time series into the sum of a small number of independent and interpretable components such as a slow varying trend, oscillatory components and structureless noise. We then use a mixture of Gaussian fitting (GF) to derive a novel predictive model for the SSA extracted NDCC incidence trend, with the overall model termed SSA-GF. Our proposed model is shown to accurately predict the NDCC trend, peak daily cases, the length of the pandemic period, the total confirmed cases and the associated dates of the turning points on the cumulated NDCC curve. Further, the three key model parameters, specifically, the amplitude (alpha), mean (mu), and standard deviation (sigma) are linked to the underlying pandemic patterns, and enable a directly interpretable evaluation of the impact of NPIs, such as strict lockdowns and travel restrictions. The predictive model is validated using available data from China and South Korea, and new predictions are made, partially requiring future validation, for the cases of Italy, Spain, the UK and the USA. Comparative results demonstrate that the introduction of consistent control measures across countries can lead to development of similar parametric models, reflected in particular by relative variations in their underlying sigma, alpha and mu values. The paper concludes with a number of open questions and outlines future research directions.

In hyperspectral images (HSI), most feature extraction and data classification methods rely on corrected dataset, in which the noisy and water absorption bands are removed. This can result in not only extra working burden but also information loss from removed bands. To tackle these issues, in this article, we propose a novel spatial-spectral feature extraction framework, multiscale 2-D singular spectrum analysis (2-D-SSA) with principal component analysis (PCA) (2-D-MSSP), for noise-robust feature extraction and data classification of HSI. First, multiscale 2-D-SSA is applied to exploit the multiscale spatial features in each spectral band of HSI via extracting the varying trends within defined windows. Taking the extracted trend signals at each scale level as the input features, the PCA is employed to the spectral domain for dimensionality reduction and spatial-spectral feature extraction. The derived spatial-spectral features in each scale are separately classified and then fused at decision-level for efficacy. As our 2-D-MSSP method can extract features and simultaneously remove noise in both spatial and spectral domains, which ensures it to be noise-robust for classification of HSI, even the uncorrected dataset. Experiments on three publicly available datasets have fully validated the efficacy and robustness of the proposed approach, when benchmarked with 10 state-of-the-art classifiers, including six spatial-spectral methods and four deep learning classifiers. In addition, both quantitative and qualitative assessment has validated the efficacy of our approach in noise-robust classification of HSI even with limited training samples, especially in classifying uncorrected data without filtering noisy bands.

With the increasing trend of energy transition to low-carbon economies, the rate of offshore structure installation and removal will rapidly accelerate through offshore renewable energy development and oil and gas decommissioning. Knowledge of the location and size of offshore infrastructure is vital in the management of marine ecosystems and also for safe navigation at sea. The availability of multimodal data enables the systematic assessment of offshore infrastructure. In this article, we propose an automatic solution for the geolocation and size evaluation of offshore infrastructure through a data fusion model of Sentinel-1 synthetic aperture radar (SAR) data and Sentinel-2 multispectral instrument imagery. The use of the Sentinel-1 (SAR) data aims to quickly localize candidate offshore energy infrastructure with its all-weather imaging capabilities, while the high-resolution optical data provided by the Sentinel-2 can enable more accurate localization and measurement of the offshore infrastructure. To be specific, a candidate detection model is applied to a time series of Sentinel-1 images to extract the “guided area” of the infrastructure, followed by morphological operation-based precise localization within an individual Sentinel-2 image, as well as estimating the size of each structure. With validation against the ground truth data of the Scottish waters from the baseline and closing bays, to the limit of the exclusive economic zone of Scotland, an area of 371 915 km ² , our method has automatically identified 332 objects with an omission error of 0.3% and a commission rate of 0%. Our method was comprehensively compared with two state-of-the-art offshore energy infrastructure detection algorithms. The results validate that our method achieves the highest overall accuracy of 99.70%, surpassing the compared ones by 3.84%–12.50%. For the size evaluation, the achieved mean errors of the topside area size of oil/gas platforms and diameter length measurement of wind turbines both are 1 pixel in Sentinel-2 images, providing an effective technique for the identification and estimation of offshore infrastructure.

In Hyperspectral Imaging (HSI), the detrimental influence of noise and distortions on data quality is profound, which has severely affected the following‐on analytics and decision‐making such as land mapping. This study presents an innovative framework for assessing HSI band quality and reconstructing the low‐quality bands, based on the Prophet model. By introducing a comprehensive quality metric to start, the authors approach factors in both spatial and spectral characteristics across local and global scales. This metric effectively captures the intricate noise and distortions inherent in the HSI data. Subsequently, the authors employ the Prophet model to forecast information within the low‐quality bands, leveraging insights from neighbouring high‐quality bands. To validate the effectiveness of the authors’ proposed model, extensive experiments on three publicly available uncorrected datasets are conducted. In a head‐to‐head comparison, the framework against six state‐of‐the‐art band reconstruction algorithms including three spectral methods, two spatial‐spectral methods and one deep learning method is benchmarked. The authors’ experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands. In addition, the authors assess the classification accuracy utilising these reconstructed bands. In various experiments, the results consistently affirm the efficacy of the authors’ method in HSI quality assessment and band reconstruction. Notably, the authors’ approach obviates the need for manually prefiltering of noisy bands. This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.