@article{fdi:010075742,
  title = {{A}cross date species detection using airborne imaging spectroscopy},
  author = {{L}aybros, {A}. and {S}chlapfer, {D}. and {F}eret, {J}. {B}. and {D}escroix, {L}. and {B}edeau, {C}. and {L}efevre, {M}. {J}. and {V}incent, {G}r{\'e}goire},
  editor = {},
  language = {{ENG}},
  abstract = {{I}maging spectroscopy is a promising tool for airborne tree species recognition in hyper-diverse tropical canopies. {H}owever, its widespread application is limited by the signal sensitivity to acquisition parameters, which may require new training data in every new area of application. {T}his study explores how various pre-processing steps may improve species discrimination and species recognition under different operational settings. {I}n the first experiment, a classifier was trained and applied on imaging spectroscopy data acquired on a single date, while in a second experiment, the classifier was trained on data from one date and applied to species identification on data from a different date. {A} radiative transfer model based on atmospheric compensation was applied with special focus on the automatic retrieval of aerosol amounts. {T}he impact of spatial or spectral filtering and normalisation was explored as an alternative to atmospheric correction. {A} pixel-wise classification was performed with a linear discriminant analysis trained on individual tree crowns identified at the species level. {T}ree species were then identified at the crown scale based on a majority vote rule. {A}tmospheric corrections did not outperform simple statistical processing (i.e., filtering and normalisation) when training and testing sets were taken from the same flight date. {H}owever, atmospheric corrections became necessary for reliable species recognition when different dates were considered. {S}hadow masking improved species classification results in all cases. {S}ingle date classification rate was 83.9% for 1297 crowns of 20 tropical species. {T}he loss of mean accuracy observed when using training data from one date to identify species at another date in the same area was limited to 10% when atmospheric correction was applied.},
  keywords = {tropical forest ; atmospheric correction ; hyperspectral ; linear ; discriminant analysis ; {GUYANE} {FRANCAISE} ; {ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {11},
  numero = {7},
  pages = {art. 789 [24 ]},
  ISSN = {2072-4292},
  year = {2019},
  DOI = {10.3390/rs11070789},
  URL = {https://www.documentation.ird.fr/hor/fdi:010075742},
}