A machine learning approach for agricultural parcel delineation through agglomerative segmentation-Reference-Cited by-同舟云学术

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A machine learning approach for agricultural parcel delineation through agglomerative segmentation

Published:2017-01-31 Issue:7 Volume:38 Page:1809-1819
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

García-Pedrero A.¹²,Gonzalo-Martín C.¹²,Lillo-Saavedra M.³⁴

Affiliation:

1. Center for Biomedical Technology, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain

2. Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Spain

3. Faculty of Agricultural Engineering, University of Concepción, Chillán, Chile

4. Water Research Center for Agriculture and Mining, CRHIAM, University of Concepción, Chillán, Chile

Funder

Fondo de Desarrollo Científico y Tecnológico

Universidad Politécnica de Madrid

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2016.1278312

Reference16 articles.

1. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

2. Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas

3. Delineation of vine parcels by segmentation of high resolution remote sensed images

4. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space

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