2016
DOI: 10.3390/rs8010031
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Cloud and Snow Discrimination for CCD Images of HJ-1A/B Constellation Based on Spectral Signature and Spatio-Temporal Context

Abstract: It is highly desirable to accurately detect the clouds in satellite images before any kind of applications. However, clouds and snow discrimination in remote sensing images is a challenging task because of their similar spectral signature. The shortwave infrared (SWIR, e.g., Landsat TM 1.55-1.75 µm band) band is widely used for the separation of cloud and snow. However, for some sensors such as the CBERS-2 (China-Brazil Earth Resources Satellite), CBERS-4 and HJ-1A/B (HuanJing (HJ), which means environment in … Show more

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Cited by 28 publications
(26 citation statements)
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“…In order to compare the accuracies of the two classification results, three commonly used evaluation metrics were adopted: accuracy, precision, and recall [27,54,55] …”
Section: Accuracy Assessment and Comparisonmentioning
confidence: 99%
See 2 more Smart Citations
“…In order to compare the accuracies of the two classification results, three commonly used evaluation metrics were adopted: accuracy, precision, and recall [27,54,55] …”
Section: Accuracy Assessment and Comparisonmentioning
confidence: 99%
“…This relationship information, derived from pixels or objects in remote sensing imagery, is called spatial contextual information [21,22]. Spatial contextual information can be used in various data sets-including multispectral imagery [23], synthetic aperture radar (SAR) [24], and LiDAR data [25]-as well as for different extraction purposes, such as forest fire mapping [26], cloud detection [27], and building detection [28]. To minimize omission errors in mapping burned areas viewed in Landsat data, a hybrid contextual algorithm was used as a second phase to improve the delimitation of burned patches based on logistic regression analysis [23].…”
Section: Introductionmentioning
confidence: 99%
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“…Cloud masking for all of the images has been implemented according to [28,37]. The cloud detection algorithm was designed to take full advantage of the temporal and spatial contextual information to overcome the shortcomings of the limited spectral bands.…”
Section: Data Pre-processingmentioning
confidence: 99%
“…Martins et al, demonstrated that a simple spatial analysis, i.e., the standard deviation of VNIR isotropic reflectances in a 3 × 3 pixel window, reliably discriminated clouds from aerosol plumes over ocean scenes [34]. Jin hu Bian et al, proposed a spectral signature and spatiotemporal context method to distinguish snow from clouds [35]. A Markov random field model was developed to segment hyperspectral image.…”
Section: Introductionmentioning
confidence: 99%