DeepNEM: Deep Network Energy-Minimization for Agricultural Field Segmentation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Petia Radeva¹  Margarita Torre²  Fernando Martinez³  Beatriz Remeseiro⁴ 

[1] Department of Computer Science, Universidad de Oviedo, Gij&x00F3;Department of Computer Science, Universitat Aut&x00F2;n, Spain;noma de Barcelona, Bellaterra, Spain;

关键词: Agricultural fields;    image edge analysis;    image segmentation;    region extraction;   

DOI  :  10.1109/JSTARS.2020.2971061

来源: DOAJ

【 摘 要 】

One of the main characteristics of agricultural fields is that the appearance of different crops and their growth status, in an aerial image, is varied, and has a wide range of radiometric values and high level of variability. The extraction of these fields and their monitoring are activities that require a high level of human intervention. In this article, we propose a novel automatic algorithm, named deep network energy-minimization (DeepNEM), to extract agricultural fields in aerial images. The model-guided process selects the most relevant image clues extracted by a deep network, completes them and finally generates regions that represent the agricultural fields under a minimization scheme. DeepNEM has been tested over a broad range of fields in terms of size, shape, and content. Different measures were used to compare the DeepNEM with other methods, and to prove that it represents an improved approach to achieve a high-quality segmentation of agricultural fields. Furthermore, this article also presents a new public dataset composed of 1200 images with their parcels boundaries annotations.

【 授权许可】

Unknown