TY - GEN
T1 - A New Smoothing-Based Farmland Extraction Approach with Vectorization from Raster Remote Sensing Images
AU - Li, Ruoxian
AU - Gao, Kun
AU - Dou, Zeyang
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - With the increasing resolution and application scene of remote sensing images, land and resource investigators begin to consider using these images to investigate crop species, cultivated area and ownership. Instead of manually drawing the boundary of the selected farmland region, an efficient edge-preserving smoothing method for automatically segmenting and extracting the area is proposed, which is performed according to the following three steps: (1) Remove the interference information by image preprocessing. The smoothing algorithm in this process was proposed according to features of the ideal smoothed image and using Maximum a Posteriori estimation model to preserve borderlines of farmland regions; (2) Image segmentation, including threshold and region segmentation using the fixed threshold and the hole removal based on the region growth method respectively after edge and whole image enhancement; (3) Information extraction, including region separation with the Flood Fill method and region vectorization which can reduce the amount of data and make the image to scale arbitrarily by contour tracking after thinning with the Freeman Chain Code. The final results of segmenting and extracting farmland objects with different features from raster remote sensing images demonstrate the correctness and efficiency of the proposed process.
AB - With the increasing resolution and application scene of remote sensing images, land and resource investigators begin to consider using these images to investigate crop species, cultivated area and ownership. Instead of manually drawing the boundary of the selected farmland region, an efficient edge-preserving smoothing method for automatically segmenting and extracting the area is proposed, which is performed according to the following three steps: (1) Remove the interference information by image preprocessing. The smoothing algorithm in this process was proposed according to features of the ideal smoothed image and using Maximum a Posteriori estimation model to preserve borderlines of farmland regions; (2) Image segmentation, including threshold and region segmentation using the fixed threshold and the hole removal based on the region growth method respectively after edge and whole image enhancement; (3) Information extraction, including region separation with the Flood Fill method and region vectorization which can reduce the amount of data and make the image to scale arbitrarily by contour tracking after thinning with the Freeman Chain Code. The final results of segmenting and extracting farmland objects with different features from raster remote sensing images demonstrate the correctness and efficiency of the proposed process.
KW - Edge-preserving smoothing
KW - Farmland region
KW - Image segmentation
KW - Information extraction
KW - Remote sensing image
KW - Vectorization
UR - http://www.scopus.com/inward/record.url?scp=85076839129&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-34113-8_28
DO - 10.1007/978-3-030-34113-8_28
M3 - Conference contribution
AN - SCOPUS:85076839129
SN - 9783030341121
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 334
EP - 346
BT - Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 3
A2 - Zhao, Yao
A2 - Lin, Chunyu
A2 - Barnes, Nick
A2 - Chen, Baoquan
A2 - Westermann, Rüdiger
A2 - Kong, Xiangwei
PB - Springer
T2 - 10th International Conference on Image and Graphics, ICIG 2019
Y2 - 23 August 2019 through 25 August 2019
ER -