论文信息 - Automatic Land Use and Land Cover Classification Using RapidEye Imagery in Mexico

Automatic Land Use and Land Cover Classification Using RapidEye Imagery in Mexico

Land use and land cover classification (LUCC) maps from remote sensor data are of great interest since they allow to track issues like deforestation/reforestation, water sources reduction or urban growth. The line of work in this project is to model land cover and land use as random textures in order to take advantage of high resolution satellite imagery.

Juan M. Barrios | Raúl Sierra-Alcocer | Enrique-Daniel Zenteno-Jimenez

[1] Pascal Fua,et al. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2] C. Justice,et al. High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[3] Honglak Lee,et al. An Analysis of Single-Layer Networks in Unsupervised Feature Learning , 2011, AISTATS.

[4] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.