429 publications from this institution
Thematic mapping via a classification analysis is one of the most common applications of remote sensing. The accuracy of image classifications is, however, often viewed negatively. Here, it is suggested that the approach to the evaluation of image classification accuracy typically adopted in remote sensing may often be unfair, commonly being rather harsh and misleading. It is stressed that the widely used target accuracy of 85% can be inappropriate and that the approach to accuracy assessment adopted commonly in remote sensing is pessimistically biased. Moreover, the maps produced by other communities, which are often used unquestioningly, may have a low accuracy if evaluated from the standard perspective adopted in remote sensing. A greater awareness of the problems encountered in accuracy assessment may help ensure that perceptions of classification accuracy are realistic and reduce unfair criticism of thematic maps derived from remote sensing.
Neural networks are attractive for the supervised classification of remotely sensed data. There are, however, many problems with their use, restricting the realisation of their full potential. This article focuses on the accommodation of fuzziness in the...
Given the common trade-off between the spatial and temporal resolutions of current satellite sensors, spatial-temporal data fusion methods could be applied to produce fused remotely sensed data with synthetic fine spatial resolution (FR) and high repeat frequency. Such fused data are required to provide a comprehensive understanding of Earth's surface land cover dynamics. In this research, a novel Spatial-Temporal Fraction Map Fusion (STFMF) model is proposed to produce a series of fine-spatial-temporal-resolution land cover fraction maps by fusing coarse-spatial-fine-temporal and fine-spatial-coarse-temporal fraction maps, which may be generated from multi-scale remotely sensed images. The STFMF has two main stages. First, FR fraction change maps are generated using kernel ridge regression. Second, a FR fraction map for the date of prediction is predicted using a temporal-weighted fusion model. In comparison to two established spatial-temporal fusion methods of spatial-temporal super-resolution land cover mapping model and spatial-temporal image reflectance fusion model, STFMF holds the following characteristics and advantages: (1) it takes account of the mixed pixel problem in FR remotely sensed images; (2) it directly uses the fraction maps as input, which could be generated from a range of satellite images or other suitable data sources; (3) it focuses on the estimation of fraction changes happened through time and can predict the land cover change more accurately. Experiments using synthetic multi-scale fraction maps simulated from Google Earth images, as well as synthetic and real MODIS-Landsat images were undertaken to test the performance of the proposed STFMF approach against two benchmark spatial-temporal reflectance fusion methods: the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Flexible Spatiotemporal Data Fusion (FSDAF) model. In both visual and quantitative evaluations, STFMF was able to generate more accurate FR fraction maps and provide more spatial detail than ESTARFM and FSDAF, particularly in areas with substantial land cover changes. STFMF has great potential to produce accurate time-series fraction maps with fine-spatial-temporal-resolution that can support studies of land cover dynamics at the sub-pixel scale.
Stress cause crops to grow at less than their full potential and can cause reduction in yield; this is as a result of threats emanating from the causative factor(s) known as stressors. Field experiments were conducted in 2010 to compare the effects of two stress-inducing agents on the spectral reflectance of barley: (1) High concentration of Carbon dioxide in soil and (2) four different levels of concentration of herbicide application. Carbon dioxide concentrations up to 80% in soil were applied to experimental plots as part of a study of the potential effects of leakage from carbon capture and storage. In a separate set of plots, glyphogan herbicide (Makhteshim Agan, UK) containing 360g l -1 of glyphosate was applied at four different levels of concentration, at the rate of 0.15 , 0.3 , 0.6 and 1.2 l ha -1 in 200 l ha -1 of water. These rates are equivalent to 5, 10, 20 and 40% of the usual lethal dose for barley crop, diluted to give the normal rate of spray coverage. Thus 0.1, 0.2, 0.4, and 0.8 ml Glophogan in 80 ml of water was sprayed evenly over each of the four plots treatment levels. This was designed to provide a range of levels of stress to the barley crop. Plant stress effects were detected by spectral scanning between 350 and 2500 nm with an ASD Fieldspec FR spectroradiometer (ASD, Boulder, USA). Canopy reflectance spectra were used to locate the position and height of the inflection point of the red edge by derivative analysis and to investigate other peaks that may indicate stress in plants. Measurements of soil gas concentration, and chlorophyll content were carried out at various stages of the crop development to determine any variations as the experiments progressed.
No abstract is provided for this article.
Many assumptions are typically made in the course of a supervised digital image classification. The focus of this paper is the commonly made assumption of an exhaustively defined set of classes. This assumption is often unsatisfied, with the imagery containing regions of classes that were not included in training the classification. The failure to satisfy the assumed condition was investigated with reference to hard and soft land cover classifications by a feedforward neural network. The accuracy of these classifications was decreased if a non-exhaustively defined set of classes was used. The exclusion of a class from the training stage resulted in a decrease in the accuracy of a hard classification of agricultural crops of up to 21.2%. Moreover, there were marked differences between the 'real' and 'apparent' accuracies of classifications of up to 15.4%. With soft classifications of urban land cover, the presence of an untrained class also degraded classification accuracy. In the soft classification output, the correlation between the actual and estimated proportional cover of a class declined from r =0.97 to r =0.76 when another class was excluded from the training stage of the classification. Possible means to reduce the negative impacts of untrained classes are considered briefly. Post-classification thresholding of the neural network's output unit activation levels may form the basis of a method to identify and remove cases of an untrained class from a hard classification. Alternatively, supporting information on the typicality of class membership may be used to identify cases representing an area containing an untrained class in both hard and soft classifications and this is illustrated with reference to a soft classification.
Super-resolution mapping (SRM) is a technique for generating a fine spatial resolution land cover map from coarse spatial resolution fraction images estimated by soft classification.The prior model used to describe the fine spatial resolution land cover pattern is a key issue in SRM.Here, a novel learning based SRM algorithm, whose prior model is learned from other available fine spatial resolution land cover maps, is proposed.The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar.The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine and coarse spatial resolution representation for the same area.From the learning database, patch pairs that have similar coarse spatial resolution patches as those in input fraction images are selected.Fine spatial resolution patches in these selected patch pairs are then used to estimate the latent fine spatial resolution land cover map, by solving an optimization problem.The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA's National Land Cover Database.Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes.The proposed SRM algorithm has the highest overall accuracy and Kappa values in all these SRM algorithms, by using the entire maps in the accuracy assessment.
The role of Earth observation (EO) data in addressing societal problems from environmental through to humanitarian should not be understated. Recent innovation in EO means provision of analysis ready data and data cubes, which allows for rapid use of EO data. This in combination with processing technologies, such as Google Earth Engine and open source algorithms/software for EO data integration and analyses, has afforded an explosion of information to answer research questions and/or inform policy making. However, there is still a need for both training and validation data within EO projects – often this can be challenging to obtain. It has been suggested that citizen science can help here to provide these data, yet there is some perceived hesitancy in using citizen science within EO projects. This paper reports on the Citizen Science 4 EO (Citizens4EO) project that aimed to obtain an in-depth understanding of researchers’ and practitioners’ experiences with citizen science data in EO within the UK. Through a mixed methods approach (online and in-depth surveys and a spotlight case study) it was found that although the benefits of using citizen science data in EO projects were many (and highlighted in the spotlighted “Slavery from Space” case study), there were a number of common concerns around using citizen science. These were around the mechanics of deploying citizen science and the unreliability of a potentially misinformed or undertrained citizen base. As such, comparing the results of this study with those of a similar survey undertaken in 2016, it is apparent that progress towards optimizing citizen science use in EO has been incremental but positive with evidence of the realization of the benefits of citizen science for EO (Citizens4EO). As such, we conclude by offering priority action areas to support further use of citizen science by the EO community within the UK, which ultimately should be adopted further afield.
The quality of the training data used in a supervised image classification can impact on the accuracy of the resulting thematic map obtained. Here the effects of mis-labeled training cases on the accuracy of classifications by discriminant analysis and a support vector machine were explored. The accuracy of both classifiers varied with the amount and nature of mis-labeled training cases. In particular, the SVM, which has been claimed to be relatively insensitive to training data error, showed the greatest sensitivity with overall accuracy declining by 8% with the use of a training set containing 20% mis-labeled cases; the difference in accuracy from that obtained without mis-labeled cases was statistically significant at the 95% level of confidence. Training data quality needs consideration when undertaking a supervised classification and should be considered in the selection of a classifier as the effects will be classifier-specific.
Continuous phenomena such as semi‐natural heathland vegetation cannot be adequately represented by conventional image classification routines. Measures of the probability of class membership derived as a by‐product of the widely used maximum likelihood classification were, however, found to be significantly correlated to heathland composition. Furthermore, ecological trends in the vegetation ground data revealed by an ordination were associated with systematic variations in the probabilities of class membership. Mapping the probabilities of class membership will therefore model the continuous character of such vegetation more appropriately than the class ificationfrom which the probabilities were dervied.
No abstract is provided for this article.
Metrics are defined which quantify the 'value' of hyperspectral imagery in the context of military tasks. A design trade-off process for sensor concept evaluation is described which takes account of constraints, such as system cost and technology limitations, on the permitted values of design parameters. The particular issue of band selection is addressed in some detail. Techniques for evaluating sensor design trade-offs are then described, based principally on simulating sensor configurations using measured image data as input. The initial use of these techniques against a limited data set is reported.
Support vector machines (SVMs) have considerable potential as classifiers of remotely sensed data. A constraint on their application in remote sensing has been their binary nature, requiring multiclass classifications to be based upon a large number of binary analyses. Here, an approach for multiclass classification of airborne sensor data by a single SVM analysis is evaluated against a series of classifiers that are widely used in remote sensing, with particular regard to the effect of training set size on classification accuracy. In addition to the SVM, the same datasets were classified using a discriminant analysis, decision tree, and multilayer perceptron neural network. The accuracy statements of the classifications derived from the different classifiers were compared in a statistically rigorous fashion that accommodated for the related nature of the samples used in the analyses. For each classification technique, accuracy was positively related with the size of the training set. In general, the most accurate classifications were derived from the SVM approach, and with the largest training set the SVM classification was significantly (p < 0.05)more accurate (93.75%) than that derived from the discriminant analysis (90.00%) and decision tree algorithms (90.31%). Although each classifier could yield a very accurate classification, > 90% correct, the classifiers differed in the ability to correctly label individual cases and so may be suitable candidates for an ensemble-based approach to classification.
