@article{fdi:010064731,
  title = {{D}etection of mesoscale thermal fronts from 4 km data using smoothing techniques : gradient-based fronts classification and basin scale application},
  author = {{R}oa-{P}ascuali, {L}. and {D}emarcq, {H}erv{\'e} and {N}ieblas, {A}. {E}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n order to optimize frontal detection in sea surface temperature fields at 4 km resolution, a combined statistical and expert-based approach is applied to test different spatial smoothing of the data prior to the detection process. {F}ronts are usually detected at 1 km resolution using the histogram-based, single image edge detection ({SIED}) algorithm developed by {C}ayula and {C}ornillon in 1992, with a standard preliminary smoothing using a median filter and a 3 x 3 pixel kernel. {H}ere, detections are performed in three study regions (off {M}orocco, the {M}ozambique {C}hannel and north-western {A}ustralia) and across the {I}ndian {O}cean basin using the combination of multiple windows ({CMW}) method developed by {N}ieto, {D}emarcq and {M}c{C}latchie in 2012 which improves on the original {C}ayula and {C}ornillon algorithm. {D}etections at 4 km and 1 km resolution are compared. {F}ronts are divided into two intensity classes ("weak" and "strong") according to their thermal gradient {A} preliminary smoothing is applied prior to the detection using different convolutions: three type of filters (median, average and {G}aussian) combined with four kernel sizes (3 x 3, 5 x 5, 7 x 7, and 9 x 9 pixels) and three detection window sizes (16 x 16,24 x 24 and 32 x 32 pixels) to test the effect of these smoothing combinations on reducing the background noise of the data and therefore on improving the frontal detection. {T}he performance of the combinations on 4 km data are evaluated using two criteria: detection efficiency and front length. {W}e find that the optimal combination of preliminary smoothing parameters in enhancing detection efficiency and preserving front length includes a median filter, a 16 x 16 pixel window size, and a 5 x 5 pixel kernel for strong fronts and a 7 x 7 pixel kernel for weak fronts. {R}esults show an improvement in detection performance (from largest to smallest window size) of 71% for strong fronts and 120% for weak fronts. {D}espite the small window used (16 x 16 pixels), the length of the fronts has been preserved relative to that found with 1 km data. {T}his optimal preliminary smoothing and the {CMW} detection algorithm on 4 km sea surface temperature data are then used to describe the spatial distribution of the monthly frequencies of occurrence for both strong and weak fronts across the {I}ndian {O}cean basin. {I}n general, strong fronts are observed in coastal areas; whereas weak fronts, with some seasonal exceptions, are mainly located in the open ocean. {T}his study shows that adequate noise reduction achieved by a preliminary smoothing of the data considerably improves the frontal detection efficiency as well as the global quality of the results. {C}onsequently, the use of 4 km data enables frontal detections similar to 1 km data (using a standard median 3 x 3 convolution) in terms of detectability, length and location. {T}his method is easily applicable to large regions or at the global scale, with far less constraints of data manipulation and processing time using 4 km data relative to 1 km data.},
  keywords = {{M}esoscale thermal fronts ; {P}reliminary smoothing ; {S}ea surface temperature ; 4 km resolution ; {G}radient intensity classification ; {E}xpert-based approach ; {D}etection efficiency ; {I}ndian {O}cean ; {OCEAN} {INDIEN}},
  booktitle = {},
  journal = {{R}emote {S}ensing of {E}nvironment},
  volume = {164},
  numero = {},
  pages = {225--237},
  ISSN = {0034-4257},
  year = {2015},
  DOI = {10.1016/j.rse.2015.03.030},
  URL = {https://www.documentation.ird.fr/hor/fdi:010064731},
}