Caio Da Silva
MIAS and INbreast are mammographic datasets for the detection and diagnosis of breast cancer. With the dawn of digital mammograms, one important preprocessing step for the… Read More »Deep Semantic Segmentation of Mammographic Images
The dataset is composed of 1,311 multi-spectral scenes extracted from images acquired by the RapidEye satellite sensors over the Serra do Cipó region, a mountainous and highly biodiverse and heterogenous landscape in southern-central Brazil mainly constituted of Cerrado-Savanna Vegetation.
From the 5 bands (blue, green, red, red edge and near infrared) that the images acquired by the RapidEye satellite sensors have, we have selected three (near-infrared, green, and red bands), which are the most useful and representative ones for discriminating vegetation areas.
It is a very challenging dataset given its high intraclass variance, caused by different spatial configurations and densities of the same vegetation type, as well as its high interclass similarity, given similar appearance of different types of vegetation species.
This dataset is a composition of scenes taken by SPOT sensor in 2005 over four counties in the State of Minas Gerais, Brazil: Arceburgo, Guaranesia, Guaxupé and Monte Santo. It has many intraclass variance caused by different crop management techniques. Also, coffee is an evergreen culture and the South of Minas Gerais is a mountainous region, which means that this dataset includes scenes with different plant ages and/or with spectral distortions caused by shadows.
Land-cover maps are one of the main sources of information for studies that support the creation of public policies in areas like urban planning and… Read More »Contextual descriptors for superpixel-based segmentation