Peter M Atkinson

Peter M Atkinson
Lancaster University | LU · Lancaster Environment Centre

PhD

About

555
Publications
168,744
Reads
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21,207
Citations
Citations since 2016
212 Research Items
12657 Citations
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201620172018201920202021202205001,0001,5002,000
201620172018201920202021202205001,0001,5002,000
Additional affiliations
June 2015 - December 2016
Lancaster University
Position
  • Dean of Faculty of Science and Technology
February 1994 - May 2016
University of Southampton
Position
  • Professor (Full)

Publications

Publications (555)
Article
Synthetic aperture radar (SAR) image change detection is a challenging task due to inherent speckle noise, imbalanced class occurrence and the requirement for discriminative feature learning. The traditional handcrafted feature extraction and current convolution-based deep learning techniques have some advantages, but suffer from being limited to n...
Article
Full-text available
Synthetic Aperture Radar (SAR) is an indispensable source of data for mapping and monitoring flood hazards, thanks to its ability to image the Earth’s surface in all weather conditions and at all times. Through cloud computing platforms such as Google Earth Engine (GEE), SAR imagery can be used in near-real time for rapid flood mapping. This has fa...
Article
Full-text available
Human African trypanosomiasis (HAT) is a neglected tropical disease that has not received much attention in Zambia and most of the countries in which it occurs. In this study, we assessed the adequacy of the healthcare delivery system in diagnosis and management of rHAT cases, the environmental factors associated with transmission, the population a...
Conference Paper
Full-text available
In order to extract useful geospatial information, cartographers are constantly challenged to respond to new sources and types of spatial data, such as satellite remote sensing imagery. Many different satellite remote sensing systems monitor the surface of the Earth, but only a small number of them are suitable for mapping or are even specifically...
Conference Paper
Full-text available
Remotely sensed measurements of nocturnal lighting from the Visible Infrared Radiometer Suite (VIIRS) satellite sensor offer a unique opportunity to track anthropogenic activities from space. This has opened the door to a multitude of new applications, such as monitoring urban expansion, estimating population growth and GDP, and tracking disasters...
Article
China implemented a stringent Air Clean Plan (ACP) since 2013 to address environmental and health risks caused by ambient fine particulate matter (PM2.5). However, the policy effectiveness of ACP and co-benefits of carbon mitigation measures to environment and health are still largely unknown. Using satellite-based PM2.5 products produced in our pr...
Article
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Spatio-temporal subpixel mapping (STSPM) has shown great potential for monitoring land surfaces, by generating land cover maps with both fine spatial and temporal resolutions. Selecting cloud-free fine spatial resolution images as ancillary data for STSPM can ensure that the temporal dependence term is measured for all subpixels, as in all current...
Article
Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution (FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classe...
Article
Full-text available
Soil moisture, a crucial property for Earth surface research, has been focused widely in various studies. The Soil Moisture Active Passive (SMAP) global products at 36 km and 9 km (called P36 and AP9 in this research) have been published from April 2015. However, the 9 km AP9 product was retrieved from the active radar and L-band passive radiometer...
Article
Semantic segmentation of remotely sensed urban scene images is required in a wide range of practical applications , such as land cover mapping, urban change detection, environmental protection, and economic assessment. Driven by rapid developments in deep learning technologies, the convolutional neural network (CNN) has dominated semantic segmentat...
Preprint
Full-text available
Soil moisture (SM) plays a significant role in many natural and anthropogenic systems which are essential to supporting life on Earth. Thus, accurate measurement and assessment of changes in soil moisture globally is of great value, including long-term historical assessment. Since the on-board cycle and detailed parameters of disparate sensors are...
Article
Haze contamination is a very common issue in remote sensing images, which inevitably limits data usability and further applications. Several methods have been developed for haze removal, which is an ill-posed problem. However, most of these methods involve various strong assumptions coupled with manually-determined parameters, which limit their gen...
Article
Full-text available
Assessment and quantitative description of river morphology using widely recognized river planview measures (e.g., length, width and sinuosity of channels, bifurcation angles and island shape) for multichannel rivers are regarded as fundamental parts of the toolkit of geomorphologists and river engineers. However, conventional assessment methods in...
Article
Continuous monitoring of water bodies is critical for a range of applications including water resource management, natural hazard assessment and climate change analysis. GOES-16 geostationary satellite Advanced Baseline Imager (ABI) imagery has a very fine (per 10 minutes) temporal resolution, which is essential for frequent monitoring of water bod...
Article
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In recent years, socioeconomic transformation and social modernisation in the Gulf Cooperation Council (GCC) states have led to tremendous changes in lifestyle and, subsequently, expansion of urban settlements. This accelerated growth is pronounced not only across vegetated coasts, plains, and mountains, but also in desert cities. Nevertheless, spa...
Article
Migrants are among the groups most vulnerable to infection with viruses due to the social and economic conditions in which they live. Therefore, spatial modeling of virus transmission among migrants is important for controlling and containing the COVID-19 pandemic. This research focused on modeling spatial associations between COVID-19 incidence ra...
Article
Full-text available
Information extraction is a key activity for remote sensing images. A common distinction exists between knowledge-driven and data-driven methods. Knowledge-driven methods have advanced reasoning ability and interpretability, but have difficulty in handling complicated tasks since prior knowledge is usually limited when facing the highly complex spa...
Article
Remote sensing images play a significant role in global land cover monitoring. However, due to the influence of cloud contamination, optical remote sensing images inevitably contain a large number of missing data, which severely limits their applicability. Existing cloud removal methods generally use only the effective information from a single ban...
Article
This paper summarizes the development and application of spatial statistical models in satellite optical remote sensing. The paper focuses on the development of a conceptual model that includes the measurement and sampling processes inherent in remote sensing. We organized this paper into five main sections: introducing the basis of remote sensing,...
Article
Full-text available
COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention natio...
Article
Full-text available
Mixed pixels are a ubiquitous problem in remote sensing images. Spectral unmixing has been used widely for mixed pixel analysis. However, up to now, most spectral unmixing methods require endmembers and cannot consider fully intraclass spectral variation. The recently proposed spatiotemporal spectral unmixing (STSU) method copes with the aforementi...
Article
Full-text available
Assigning geospatial objects with specific categories at the pixel level is a fundamental task in remote sensing image analysis. Along with the rapid development of sensor technologies, remotely sensed images can be captured at multiple spatial resolutions (MSR) with information content manifested at different scales. Extracting information from th...
Article
Subpixel mapping (SPM) is a technique to tackle the mixed pixel problem and produce land cover and land use (LCLU) maps at a finer spatial resolution than the original coarse data. However, uncertainty exists unavoidably in SPM, which is an ill-posed downscaling problem. Spatio-temporal SPM methods have been proposed to deal with this uncertainty,...
Article
Full-text available
Understanding the spatio-temporal pattern of natural vegetation helps decoding the responses to climate change and interpretation on forest resilience. Satellite remote sensing based data products, by virtue of their synoptic and repetitive coverage, offer to study the correlation and lag effects of rainfall on forest growth in a relatively longer...
Article
Remote sensing scene classification plays a critical role in a wide range of real-world applications. Technically, however, scene classification is an extremely challenging task due to the huge complexity in remotely sensed scenes, and the difficulty in acquiring labelled data for model training such as supervised deep learning. To tackle these iss...
Article
Spatiotemporal fusion is a technique applied to create images with both fine spatial and temporal resolutions by blending images with different spatial and temporal resolutions. Spatial unmixing (SU) is a widely used approach for spatiotemporal fusion, which requires only the minimum number of input images. However, ignorance of spatial variation i...
Article
Full-text available
Optical earth observation satellite sensors often provide a coarse spatial resolution (CR) multispectral (MS) image together with a fine spatial resolution (FR) panchromatic (PAN) image. Pansharpening is a technique applied to such satellite sensor images to generate an FR MS image by injecting spatial detail taken from the FR PAN image while simul...
Article
The agricultural landscape can be interpreted at different semantic levels, such as fine low-level crop (LLC) classes (e.g., Wheat, Almond, and Alfalfa) and broad high-level crop (HLC) classes (e.g., Winter crops, Tree crops, and Forage). The LLC and HLC are hierarchically correlated with each other, but such intrinsically hierarchical relationship...
Article
Sentinel-3 is a newly launched satellite implemented by the European Space Agency (ESA) for global observation. The Ocean and Land Colour Imager (OLCI) sensor onboard Sentinel-3 provides 21 band images with a fine spectral resolution and is of great value for ocean, land and atmospheric monitoring. The two platforms (Sentinel-3A and -3B) can provid...
Article
Full-text available
On May 31, 2003, the scan-line corrector (SLC) of Landsat 7 ETM+ failed permanently. The resulting ETM+ SLC-off images contain 22% un-scanned gap pixels, thus, severely limiting their utility. In this paper, we propose a new scheme to fill gaps in SLC-off images by identifying a new source of auxiliary or known image. Specifically, Sentinel-2 MSI i...
Article
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This paper provides a brief analytical review of the current state-of-the-art in relation to the explainability of artificial intelligence in the context of recent advances in machine learning and deep learning. The paper starts with a brief historical introduction and a taxonomy, and formulates the main challenges in terms of explainability buildi...
Article
Full-text available
Crop production is a major source of food and livelihood for many people in arid and semi-arid (ASA) regions across the world. However, due to irregular climatic events, ASA regions are affected commonly by frequent droughts that can impact food production. In addition, ASA regions in the Middle East and Africa are often characterised by political...
Article
Information on forest disturbance is crucial for tropical forest management and global carbon cycle analysis. The long-term collection of data from the Landsat missions provides some of the most valuable information for understanding the processes of global tropical forest disturbance. However, there are substantial uncertainties in the estimation...
Article
Deep learning methods, especially convolutional neural network (CNN)-based methods, have shown promising performance for hyperspectral image (HSI) change detection (CD). It is acknowledged widely that different spectral channels and spatial locations in input image patches may contribute differently to CD. However, they are treated equally in exist...
Article
Full-text available
Semantic segmentation of remote sensing images plays an important role in a wide range of applications, including land resource management, biosphere monitoring, and urban planning. Although the accuracy of semantic segmentation in remote sensing images has been increased significantly by deep convolutional neural networks, several limitations exis...
Code
This is the Matlab code of the STFMF (spatial-temporal fraction map fusion) algorithm. Please refer to the reference: Yihang Zhang, et al.“Spatial- temporal fraction map fusion with multi-scale remotely sensed images,”Remote Sensing of Environment, 2018, 213:162-181. Only for academic exchanges and scientific research, the code cannot be used...
Article
The highly dynamic nature of agro-ecosystems in space and time usually leads to high intra-class variance and low inter-class separability in the fine spatial resolution (FSR) remotely sensed imagery. This makes traditional classifiers essentially relying on spectral information for crop mapping from FSR imagery an extremely challenging task. To mi...
Article
Full-text available
Chronic kidney disease (CKD), a collective term for many causes of progressive renal failure, is increasing worldwide due to ageing, obesity and diabetes. However, these factors cannot explain the many environmental clusters of renal disease that are known to occur globally. This study uses data from the UK Renal Registry (UKRR) including CKD of un...
Article
Full-text available
Global land cover (LC) changes threaten sustainability and yet we lack a comprehensive understanding of the gains and losses of LC types, including the magnitudes, locations and timings of transitions. We used a novel, fine-resolution and temporally consistent satellite-derived dataset covering the entire Earth annually from 1992 to 2018 to quantif...
Article
Full-text available
Mixed pixels exist widely in remotely sensed images. To obtain more reliable land cover information than traditional hard classification, spectral unmixing methods have been developed to estimate the composition of the mixed pixels, in terms of the proportions of land cover classes. The existing spectral unmixing methods usually require pure spectr...
Chapter
Full-text available
The occurrence of environmental clusters of Chronic Kidney Disease of uncertain aetiology (CKDu), where there is no known cause for the onset of kidney dysfunction, is a concern globally. Waterborne exposure pathways in the environment may result in indirect or direct ingestion of trace elements with potential health risks. This research examines t...
Article
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Objectives The aim of the study was to investigate the spatial and temporal relationships between the prevalence of COVID-19 symptoms in the community-level and area-level social deprivation. Design Spatial mapping, generalised linear models, using time as a factor and spatial-lag models were used to explore the relationship between self-reported...
Chapter
Full-text available
Africa continues to experience the highest infectious disease burden despite an increase in investments. These include investments in malaria, HIV/AIDS, tuberculosis, as well as in communicable diseases. The global targets are to reduce the burden of these diseases through improved surveillance, prevention of outbreaks, effective case management, e...
Article
Full-text available
Mapping surface water distribution and its dynamics over various environments with robust methods is essential for managing water resources and supporting water-related policy design. Thresholding Single Water Index image (TSWI) with threshold is a common way of using water index (WI) for mapping water for it is easy to use and could obtain accepta...
Article
The Terra/Aqua MODerate resolution Imaging Spectroradiometer (MODIS) data have been used widely for global monitoring of the Earth's surface due to their daily fine temporal resolution. The spatial resolution of MODIS time-series (i.e., 500 m), however, is too coarse for local monitoring. A feasible solution to this problem is to downscale the coar...
Article
Full-text available
Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction me...
Article
Full-text available
Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. As the virus spread worldwide causing a global pandemic, China reduced transmission at considerable social and economic cost. Post-lockdown, resuming work safely, that is, while avoiding a second epidemic outbreak, is a major challenge. Exacerbating this c...
Article
Full-text available
Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder-decoder architecture, has been used frequently for image segmentation with high accuracy. In this letter, we incorporate multiscale features generated by different layers of U-Net and...
Article
Full-text available
Impervious surfaces have important effects on the natural environment, including promoting hydrological run-off and impeding evapotranspiration, as well as increasing the urban heat island effect. Obtaining accurate and timely information on the spatial distribution and dynamics of urban surfaces is, thus, of paramount importance for socio-economic...
Code
This is the Matlab code of the OATPRK algorithm. Moreover, the IKONOS multispectral and panchromatic images used in the paper are attached in the code file. Please refer to the references: Yihang Zhang, Peter M. Atkinson, Feng Ling, Giles M. Foody, Qunming Wang, et al. "Object-based Area-to-point Regression Kriging for Pansharpening," IEEE Trans...
Article
Large-scale {(large-area)}, fine spatial resolution satellite sensor images are valuable data sources for Earth observation while not yet fully exploited by research communities for practical applications. Often, such images exhibit highly complex geometrical structures and spatial patterns, and distinctive characteristics of multiple land-use cate...
Article
The scan-line corrector (SLC) of the Landsat 7 ETM+ failed permanently in 2003, resulting in about 22% unscanned gap pixels in the SLC-off images, affecting greatly the utility of the ETM+ data. To address this issue, we propose a spatial-spectral radial basis function (SSRBF)-based interpolation method to fill gaps in SLC-off images. Different fro...
Article
The point spread function (PSF) effect is ubiquitous in remote sensing images and imposes a fundamental uncertainty on subpixel mapping (SPM). The crucial PSF effect has been neglected in existing SPM methods. This paper proposes a general model to reduce the PSF effect in SPM. The model is applicable to any SPM methods treating spectral unmixing a...
Preprint
Small area change detection from synthetic aperture radar (SAR) is a highly challenging task. In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can...
Article
Spatio-temporal fusion is a technique used to produce images with both fine spatial and temporal resolution. Generally, the principle of existing spatio-temporal fusion methods can be characterized by a unified framework of prediction based on two parts: (i) the known fine spatial resolution images (e.g., Landsat images), and (ii) the fine spatial...
Preprint
Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media...
Article
Full-text available
The identification and counting of plant individuals is essential for environmental monitoring. UAV based imagery offer ultra-fine spatial resolution and flexibility in data acquisition, and so provide a great opportunity to enhance current plant and in-situ field surveying. However, accurate mapping of individual plants from UAV imagery remains ch...
Preprint
Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media...