A new supervised classification method for quantitative analysis of remotely-sensed multi-spectral data

Erol, Hamza; Akdeniz, Fikri

doi:10.1080/014311698216008

Cited by 6 publications

(4 citation statements)

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“…To quantitatively describe the similarity of the PDFs for each precipitation scenario, we employ the Hellinger distance statistic, which is used for various applications related to classification techniques [36][37][38][39][40]. This metric H, which is closely related to the Bhattacharyya distance [41,42], is dependent on two bivariate normal distributions, P ∼ N (µ 1 , Σ 1 ) and Q ∼ N (µ 2 , Σ 2 ), where µ and Σ are the mean and covariance matrix of the distributions, respectively.…”

Section: Hellinger Distancesmentioning

confidence: 99%

Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

et al. 2018

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Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.

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Section: Hellinger Distancesmentioning

confidence: 99%

Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

et al. 2018

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“…These classification routines fall into three main categories: distance based, probability based, and angular based decision rules. There is no one ideal classification routine to suit all needs and requirements (Cihlar et al, 1998;Erol & Akdeniz, 1998;Kartikeyan et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Optimal classification methods for mapping agricultural tillage practices

South

Lusch

2004

Remote Sensing of Environment

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“…In order to delineate a feature from its surroundings, the effective and practical techniques are edge detection, cluster shadow method, and entropic approach [10,11,. Apart from the objective filters, the classification techniques, mainly natural-breaks Jenks optimization method and standard deviation, are widely used in remote sensing [86][87][88]. These classifications aid in spatially identifying the characteristics that are crucial or impossible to recognize precisely in multi-spectral remote sensing satellite data [89][90][91].…”

Section: Introductionmentioning

confidence: 99%

“…The summary of filters used to extract the extent of KC from the selected seven satellite-based oceanographic parameters (Table 1) and the sequence in which they were applied (Figure 2). In multi-spectral remote sensing satellite data, the spectral classification methods aid with extracting quite complicated and hidden features [86][87][88]. The classification method used varies depending on the nature of the data.…”

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confidence: 99%

A Combination of Spatial Domain Filters to Detect Surface Ocean Current from Multi-Sensor Remote Sensing Data

Lee

2022

Remote Sensing

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This research investigates the applicability of combining spatial filter’s algorithm to extract surface ocean current. Accordingly, the raster filters were tested on 80–13,505 daily images to detect Kuroshio Current (KC) on weekly, seasonal, and climatological scales. The selected raster filters are convolution, Laplacian, north gradient, sharpening, min/max, histogram equalization, standard deviation, and natural break. In addition, conventional data set of sea surface currents, sea surface temperature (SST), sea surface height (SSH), and non-conventional data such as total heat flux, surface density (SSD), and salinity (SSS) were employed. Moreover, controversial data on ocean color are included because very few studies revealed that chlorophyll-α is a proxy to SST in the summer to extract KC. Interestingly, the performance of filters is uniform and thriving for seasonal and on a climatological scale only by combining the algorithms. In contrast, the typical scenario of identifying Kuroshio signatures using an individual filter and by designating a value spectrum is inapplicable for specific seasons and data set. Furthermore, the KC’s centerlines computed from SST, SSH, total heat flux, SSS, SSD, and chlorophyll-α correlate with sea surface currents. Deviations are observed in the various segments of Kuroshio’s centerline extracted from heat flux, chlorophyll-α, and SSS flowing across Tokara Strait from northeast Taiwan to the south of Japan.

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A new supervised classification method for quantitative analysis of remotely-sensed multi-spectral data

Cited by 6 publications

References 0 publications

Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

Optimal classification methods for mapping agricultural tillage practices

A Combination of Spatial Domain Filters to Detect Surface Ocean Current from Multi-Sensor Remote Sensing Data

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