@InProceedings{ZorteaSalbTrie:2012:ObClCl,
author = "Zortea, Maciel and Salberg, Arnt-Børre and Trier, Øivind Due",
title = "Object-based cloud and cloud shadow detection in Landsat images
for tropical forest monitoring",
booktitle = "Proceedings...",
year = "2012",
editor = "Feitosa, Raul Queiroz and Costa, Gilson Alexandre Ostwald Pedro da
and Almeida, Cl{\'a}udia Maria de and Fonseca, Leila Maria Garcia
and Kux, Hermann Johann Heinrich",
pages = "326--331",
organization = "International Conference on Geographic Object-Based Image
Analysis, 4. (GEOBIA).",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
keywords = "Cloud detection, shadows, classification, segmentation, Landsat.",
abstract = "Clouds and cloud shadows often obscure parts of images acquired by
optical space-borne sensors. The clouds and cloud shadows need to
be detected and labeled as missing data. This enables subsequent
methods to make their own decisions about how the missing data
should be handled. Here we propose an automatic method to detect
daytime cloud and cloud shadows in the context of tropical forest
monitoring. In particular, we focus on Landsat 5 TM and Landsat 7
ETM+ images. In addition to the original bands, we investigate the
use of additional spectral-derived features, based on pixel-wise
differences, ratios, and maximum values derived for all
combinations of pairs of top-of-the atmosphere reflectance bands.
The subset of features retained for classification, and the
boundaries of the classes in the feature space, were identified by
optimizing the accuracy of the proposed method using samples
collected from spatially disjoint scenes, acquired in different
time periods, in an attempt to increase the generalization
capability of the proposed approach when applied to unseen scenes.
When a new image is to be classified, the idea is to first segment
it locally using the Statistical Region Merging algorithm (Nock
and Nielsen, 2004). Cloud and cloud shadow masks are then obtained
by classifying the averaged pixel values, inside each segment,
instead of individual pixels. Finally a simple cloud shape
matching algorithm is used to reduce false detection of cloud
shadow areas. We found that the proposed object-based technique
reduces the spatial noise of the final classified map when
compared to traditional single pixel classification. The accuracy
of the proposed method appears to be comparable to two alternative
algorithms selected for benchmark purposes.",
conference-location = "Rio de Janeiro",
conference-year = "May 7-9, 2012",
isbn = "978-85-17-00059-1",
language = "en",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP8W/3BT7ED5",
url = "http://urlib.net/ibi/8JMKD3MGP8W/3BT7ED5",
targetfile = "094.pdf",
type = "Forest Analysis",
urlaccessdate = "03 jun. 2024"
}