Object-based crop identification using multiple vegetation indices, textural features and crop phenology

By José Manuel Peñá-Barragán, Moffatt K. Ngugi, Richard E. Plant, Johan Six. 2011

625 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/1aef6f3c940c5f2ca4a23b75b9152803fba32da3

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In Ecology

In Global and Planetary Change

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Citations in the corpus

Correcting MODIS 16-day composite NDVI time-series with actual acquisition dates (2014, background): “Since mixed pixels’ signal is not pure and the accuracy of land-cover monitoring is reduced [Pena-Barragan et al., 2011; Wang et al., 2012], mixed pixels were not considered for this study.”
Object-based urban vegetation mapping with high-resolution aerial photography as a single data source (2013, methodology): “However, the presence of mixed pixels located at the edges of different objects (De Wit and Clevers 2004) has been mostly overcome by the techniques of object-based image analysis (OBIA) (Peña-Barragán et al. 2011).”
Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery (2014, methodology): “Indices used in the classification included EVI as well as normalized differential senescent vegetation index (NDSVI), which relates to cropspecific responses to water content, and normalized difference tillage index (NDTI), which relates to residue cover (Pena-Barragan et al., 2011; Van Deventer, W”
A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform (2018, positive): “Object-based approaches (Peña-Barragán et al., 2011; Peña et al., 2014) include Hierarchical Image Segmentation software or HSeG (Tilton et al., 2012).”
Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks (2016, methodology): “As an example, the filter sizes for the CNN with context area m = 45 are set to n = [16, 10, 7, 4, 4] in each layer and the pooling dimension p = [1, 1, 1, 1, 3].”
Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine (2017, background): “Object-based approaches of crop identification have also been explored [35].”
Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders (2018, methodology): “Pixel-based [9,12] and object-based [10, 11,13] approaches have been proposed.”