An investigation into the effects of speckle filters on classification

Alexander, Lee; Inggs, M.R.

doi:10.1109/igarss.1999.774586

Cited by 1 publication

(4 citation statements)

References 2 publications

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“…The JM distance of a pair of probabilistic distributions indicates the average distance between two classes density functions and it features a saturating behavior with increasing separation [71]. This fact makes it more suitable for dealing with a multi-class problem, because it avoids bias when taking averages due to an easily separable class.…”

Section: Separabilitymentioning

confidence: 99%

“…The JM distance ranges between 0 and ? 2 with highest values indicating greater class separability [71].…”

Section: Separabilitymentioning

confidence: 99%

“…It is therefore suitable for the problem at hand, which has four features and four classes. The JM distance (Equation (3)) is derived from the Bhattacharyya distance (BD) (Equation (4)) [71]. The JM distance ranges between 0 and ?…”

Section: Separabilitymentioning

confidence: 99%

“…2. The normalised average JM distance was used as a parameter to indicate overall class separability [71]. Upper and lower bounds of the probability of the classification error P e pi, jq are given by [71] (Equation (5)):…”

Section: Separabilitymentioning

confidence: 99%

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Wavelet Based Analysis of TanDEM-X and LiDAR DEMs across a Tropical Vegetation Heterogeneity Gradient Driven by Fire Disturbance in Indonesia

2016

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Three-dimensional information provided by TanDEM-X interferometric phase and airborne Light Detection and Ranging (LiDAR) Digital Elevation Models (DEMs) were used to detect differences in vegetation heterogeneity through a disturbance gradient in Indonesia. The range of vegetation types developed as a consequence of fires during the 1997-1998 El Niño. Two-point statistic (wavelet variance and co-variance) was used to assess the dominant spatial frequencies associated with either topographic features or canopy structure. DEMs wavelet spectra were found to be sensitive to canopy structure at short scales (up to 8 m) but increasingly influenced by topographic structures at longer scales. Analysis also indicates that, at short scale, canopy texture is driven by the distribution of heights. Thematic class separation using the Jeffries-Matusita distance (JM) was greater when using the full wavelet signature (LiDAR: 1.29 ď JM ď 1.39; TanDEM-X: 1.18 ď JM ď 1.39) compared to using each decomposition scale individually (LiDAR: 0.1 ď JM ď 1.26; TanDEM-X: 0.1 ď JM ď 1.1). In some cases, separability with TanDEM-X was similar to the higher resolution LiDAR. The study highlights the potential of 3D information from TanDEM-X and LiDAR DEMs to explore vegetation disturbance history when analyzed using two-point statistics.

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Section: Separabilitymentioning

confidence: 99%

“…The JM distance ranges between 0 and ? 2 with highest values indicating greater class separability [71].…”

Section: Separabilitymentioning

confidence: 99%

Section: Separabilitymentioning

confidence: 99%

Section: Separabilitymentioning

confidence: 99%

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