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  • D P Roy, M A Wulder, T R Loveland, C E Woodcock, R G Allen, M C Anderson +43 others
  • 2016
a r t i c l e i n f o Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the remarkable 40 year Landsat record and has enhanced capabilities including new spectral bands in the blue and(More)
Three southern USA forestry species, loblolly pine (Pinus taeda), Virginia pine (Pinus virginiana), and shortleaf pine (Pinus echinata), were previously shown to be spectrally separable (83% accuracy) using data from a full-range spectro-radiometer (400–2500 nm) acquired above tree canopies. This study focused on whether these same species are also(More)
—This paper describes a new algorithm used to adaptively filter a remote sensing dataset based on signal-to-noise ratios (SNRs) once the maximum noise fraction (MNF) has been applied. This algorithm uses Hermite splines to calculate the approximate area underneath the SNR curve as a function of band number, and that area is used to place bands into " bins "(More)
—One challenge to implementing spectral change detection algorithms using multitemporal Landsat data is that key dates and periods are often missing from the record due to weather disturbances and lapses in continuous coverage. This paper presents a method that utilizes residuals from harmonic regression over years of Landsat data, in conjunction with(More)
The iterative guided spectral class rejection (IGSCR) classification algorithm uses an underlying clustering method and a decision rule to arrive at final classifications for remotely sensed data. Previous versions of IGSCR have used a hard clustering method such as <i>k</i>-means or ISODATA. In an effort to ultimately create a fuzzy version of IGSCR, this(More)
– In remote sensing and other disciplines, clustering is frequently used in classification to assign labels to data. In particular, the iterative guided spectral class rejection (IGSCR) classification algorithm uses labeled data and a statistical hypothesis test to determine which clusters should be used in classification. Rejected clusters (based on this(More)
—This paper presents a new semiautomated soft classification method that is a hybrid between supervised and unsu-pervised classification algorithms for the classification of remote sensing data. Continuous iterative guided spectral class rejection (IGSCR) (CIGSCR) is based on the IGSCR classification method, a crisp classification method that automatically(More)