All Outputs (9)

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, https://doi.org/10.1016/j.rse.2017.11.024

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 about Supervised methods of image segmentation accuracy assessment in land cover mapping.

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, https://doi.org/10.1016/j.ecolind.2017.09.055

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 about Remotely sensed spatial heterogeneity as an exploratory tool for taxonomic and functional diversity study.

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), Article 1175. https://doi.org/10.3390/rs9111175

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 about Monitoring thermal pollution in rivers downstream of dams with Landsat ETM+ thermal infrared images.

Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model: a case study in Wuhan city using Landsat time-series datasets from 1987 to 2016 (2017)
Journal Article
Shi, L., Ling, F., Ge, Y., Foody, G. M., Li, X., Wang, L., …Du, Y. (2017). Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model: a case study in Wuhan city using Landsat time-series datasets from 1987 to 2016. Remote Sensing, 9(11), Article 1148. https://doi.org/10.3390/rs9111148

Detailed information on the spatial-temporal change of impervious surfaces is important for quantifying the effects of rapid urbanization. Free access of the Landsat archive provides new opportunities for impervious surface mapping with fine spatial... Read More about Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model: a case study in Wuhan city using Landsat time-series datasets from 1987 to 2016.

Mapping and the citizen sensor (2017)
Book
Foody, G., See, L., Fritz, S., Mooney, P., Olteanu-Raimond, A., Costa Fonte, C., & Antoniou, V. (2017). G. Foody, L. See, S. Fritz, P. Mooney, A. Olteanu-Raimond, C. C. Fonte, & V. Antoniou (Eds.). Mapping and the citizen sensor. Ubiquity Press. https://doi.org/10.5334/bbf

Maps are a fundamental resource in a diverse array of applications ranging from everyday activities, such as route planning through the legal demarcation of space to scientific studies, such as those seeking to understand biodiversity and inform the... Read More about Mapping and the citizen sensor.

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), Article 888. https://doi.org/10.3390/app7090888

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 about Impacts of sample design for validation data on the accuracy of feedforward neural network classification.

Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps (2017)
Journal Article
Li, X., Ling, F., Foody, G. M., Ge, Y., Zhang, Y., & Du, Y. (2017). Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps. Remote Sensing of Environment, 196, 293-311. https://doi.org/10.1016/j.rse.2017.05.011

© 2017 Elsevier Inc. Studies of land cover dynamics would benefit greatly from the generation of land cover maps at both fine spatial and temporal resolutions. Fine spatial resolution images are usually acquired relatively infrequently, whereas coars... Read More about Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps.

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. (2017). Improving specific class mapping from remotely sensed data by cost-sensitive learning. International Journal of Remote Sensing, 38(11), 3294-3316. https://doi.org/10.1080/01431161.2017.1292073

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 about Improving specific class mapping from remotely sensed data by cost-sensitive learning.

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, https://doi.org/10.1016/j.rse.2016.12.017

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 about Using mixed objects in the training of object-based image classifications.