Research Repository

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Earth observation and machine learning to meet Sustainable Development Goal 8.7: mapping sites associated with slavery from space (2019)
Journal Article
Foody, G., Ling, F., Boyd, D., Li, X., & Wardlaw, J. (2019). Earth observation and machine learning to meet Sustainable Development Goal 8.7: mapping sites associated with slavery from space. Remote Sensing, 11(3), doi:10.3390/rs11030266

A large proportion of the workforce in the brick kilns of the Brick Belt of Asia are modern-day slaves. Work to liberate slaves and contribute to UN Sustainable Development Goal 8.7 would benefit from maps showing the location of brick kilns. Previou... Read More

Reducing the impacts of intra-class spectral variability on the accuracy of soft classification and super-resolution mapping of shoreline (2018)
Journal Article
Doan, H. T., Foody, G. M., & Bui, D. T. (2018). Reducing the impacts of intra-class spectral variability on the accuracy of soft classification and super-resolution mapping of shoreline. International Journal of Remote Sensing, doi:10.1080/01431161.2018.1545099

The main objective of this research is to assess the impact of intra-class spectral variation on the accuracy of soft classification and super-resolution mapping. The accuracy of both analyses was negatively related to the degree of intra-class spect... Read More

Spatial-temporal fraction map fusion with multi-scale remotely sensed images (2018)
Journal Article
Zhang, Y., Foody, G. M., Ling, F., Li, X., Ge, Y., Du, Y., & Atkinson, P. M. (2018). Spatial-temporal fraction map fusion with multi-scale remotely sensed images. Remote Sensing of Environment, 213, doi:10.1016/j.rse.2018.05.010. ISSN 0034-4257

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 rep... Read More

Supervised methods of image segmentation accuracy assessment in land cover mapping (2017)
Journal Article
Costa, H., Foody, G. M., & Boyd, D. S. (2018). Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sensing of Environment, 205, doi:10.1016/j.rse.2017.11.024. ISSN 0034-4257

Land cover mapping via image classification is sometimes realized through object-based image analysis. Objects are typically constructed by partitioning imagery into spatially contiguous groups of pixels through image segmentation and used as the bas... Read More

Remotely sensed spatial heterogeneity as an exploratory tool for taxonomic and functional diversity study (2017)
Journal Article
Rocchini, D., Bacaro, G., Chirici, G., Da Re, D., Feilhauer, H., Foody, G. M., …Rugani, B. (in press). Remotely sensed spatial heterogeneity as an exploratory tool for taxonomic and functional diversity study. Ecological Indicators, 85, doi:10.1016/j.ecolind.2017.09.055. ISSN 1470-160X

Assessing biodiversity from field-based data is difficult for a number of practical reasons: (i) establishing the total number of sampling units to be investigated and the sampling design (e.g. systematic, random, stratified) can be difficult; (ii) t... Read More

Monitoring thermal pollution in rivers downstream of dams with Landsat ETM+ thermal infrared images (2017)
Journal Article
Ling, F., Foody, G., Du, H., Ban, X., Li, X., Zhang, Y., & Du, Y. (2017). Monitoring thermal pollution in rivers downstream of dams with Landsat ETM+ thermal infrared images. Remote Sensing, 9(11), doi:10.3390/rs9111175. ISSN 2072-4292

Dams play a significant role in altering the spatial pattern of temperature in rivers and contribute to thermal pollution, which greatly affects the river aquatic ecosystems. Understanding the temporal and spatial variation of thermal pollution cause... Read More

Impacts of sample design for validation data on the accuracy of feedforward neural network classification (2017)
Journal Article
Foody, G. (2017). Impacts of sample design for validation data on the accuracy of feedforward neural network classification. Applied Sciences, 7(9), doi:10.3390/app7090888. ISSN 2076-3417

Validation data are often used to evaluate the performance of a trained neural network and used in the selection of a network deemed optimal for the task at-hand. Optimality is commonly assessed with a measure, such as overall classification accuracy... Read More

Improving specific class mapping from remotely sensed data by cost-sensitive learning (2017)
Journal Article
Silva, J., Bacao, F., Dieng, M., Foody, G. M., & Caetano, M. (in press). Improving specific class mapping from remotely sensed data by cost-sensitive learning. International Journal of Remote Sensing, 38(11), doi:10.1080/01431161.2017.1292073. ISSN 0143-1161

In many remote-sensing projects, one is usually interested in a small number of land-cover classes present in a study area and not in all the land-cover classes that make-up the landscape. Previous studies in supervised classification of satellite im... Read More

Using mixed objects in the training of object-based image classifications (2017)
Journal Article
Costa, H., Foody, G. M., & Boyd, D. S. (2017). Using mixed objects in the training of object-based image classifications. Remote Sensing of Environment, 190, doi:10.1016/j.rse.2016.12.017. ISSN 0034-4257

Image classification for thematic mapping is a very common application in remote sensing, which is sometimes realized through object-based image analysis. In these analyses, it is common for some of the objects to be mixed in their class composition... Read More

An iterative interpolation deconvolution algorithm for superresolution land cover mapping (2016)
Journal Article
Ling, F., Foody, G. M., Ge, Y., Li, X., & Du, Y. (2016). An iterative interpolation deconvolution algorithm for superresolution land cover mapping. IEEE Transactions on Geoscience and Remote Sensing, 54(12), doi:10.1109/TGRS.2016.2598534. ISSN 0196-2892

Super-resolution mapping (SRM) is a method to produce a fine spatial resolution land cover map from coarse spatial resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of... Read More

Assessing a temporal change strategy for sub-pixel land cover change mapping from multi-scale remote sensing imagery (2016)
Journal Article
Ling, F., Foody, G., Li, X., Zhang, Y., & Du, Y. (in press). Assessing a temporal change strategy for sub-pixel land cover change mapping from multi-scale remote sensing imagery. Remote Sensing, 8(8), doi:10.3390/rs8080642. ISSN 2072-4292

Remotely sensed imagery is an attractive source of information for mapping and monitoring land cover. Fine spatial resolution imagery is typically acquired infrequently, but fine temporal resolution systems commonly provide coarse spatial resolution... Read More