Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

Hoffbeck, Joseph P.; Landgrebe, D. A.

doi:10.1109/igarss.1994.399791

Cited by 12 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, in a study by Song et al [67] it was concluded that atmospheric correction was necessary only when models were applied across spatiotemporal dimensions. Similar conclusions were made in the studies of Kim et al [68] and Hoffbeck and Landgrebe [69]. Thus, as our study area is small, the normalization could in fact be more useful than to use the atmospheric correction.…”

Section: Discussionsupporting

confidence: 87%

Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data

et al. 2019

View full text Add to dashboard Cite

In forest management, site index information is essential for planning silvicultural operations and forecasting forest development. Site index is most commonly expressed as the average height of the dominant trees at a certain index age, and can be determined either by photo interpretation, field measurements, or projection of age combined with height estimates from remote sensing. However, recently it has been shown that site index can be accurately predicted from bi-temporal airborne laser scanner (ALS) data. Furthermore, single-time hyperspectral data have also been shown to be correlated to site index. The aim of the current study was to compare the accuracy of modelling site index using (1) data from bi-temporal ALS; (2) single-time hyperspectral data with different types of preprocessing; and (3) combined bi-temporal ALS and single-time hyperspectral data. The period between the ALS acquisitions was 11 years. The preprocessing of the hyperspectral data included an atmospheric correction and/or a normalization of the reflectance. Furthermore, a selection of pixels was carried out based on NDVI and compared to using all pixels. The results showed that bi-temporal ALS data explained about 70% (R2) of the variation in the site index, and the RMSE values from a cross-validation were 3.0 m and 2.2 m for spruce- and pine-dominated plots, respectively. Corresponding values for the different single-time hyperspectral datasets were 54%, 3.9 m, and 2.5 m. With bi-temporal ALS data and hyperspectral data used in combination, the results indicated that the contribution from the hyperspectral data was marginal compared to just using bi-temporal ALS. We also found that models constructed with normalized hyperspectral data produced lower RMSE values compared to those constructed with atmospherically corrected data, and that a selection of pixels based on NDVI did not improve the results compared to using all pixels.

show abstract

Section: Discussionsupporting

confidence: 87%

Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The atmospheric correction is known not to influence the result of classifications obtained using within-scene training and validation ROIs (e.g. Hoffbeck & Landgrebe, 1994), but was here performed to produce a homogeneous dataset for future direct comparisons or analyses, e.g. based on vegetation indexes.…”

Section: Preliminary Data Processingmentioning

confidence: 99%

Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing

Belluco

Camuffo

Ferrari

et al. 2006

Remote Sensing of Environment

294

161

View full text Add to dashboard Cite

“…Atmospheric correction was not applied to the hyperspectral imagery. Although adjacency effects can influence the classification accuracy and atmospheric correction can reduce this problem, a number of studies have reported that applying an atmospheric correction has little effect on species classification [26], [35], [44], [45]. A bi-directional distribution function (BRDF) correction was not applied since a single flight line was used.…”

Section: B Airborne Surveymentioning

confidence: 99%

Individual Tree Species Classification From Airborne Multisensor Imagery Using Robust PCA

Lee

Cai

Lellmann

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Remote sensing of individual tree species has many applications in resource management, biodiversity assessment and conservation. Airborne remote sensing using LiDAR and hyperspectral sensors has been used extensively to extract biophysical traits of vegetation and to detect species. However, its application for individual tree mapping remains limited due to the technical challenges of precise co-alignment of images acquired from different sensors and accurately delineating individual tree crowns (ITCs). In this study we developed a generic workflow to map tree species at ITC-level from hyperspectral imagery and LiDAR data using a combination of well-established and recently developed techniques. The workflow uses a non-parametric image registration approach to co-align images, a multi-class normalised graph cut method for ITC delineation, robust principal component analysis for feature extraction, and support vector machine for species classification. This workflow allows us to automatically map tree species at both pixel-and ITC-level. Experimental tests of the technique were conducted using ground data collected from a fully-mapped temperate woodland in the UK. The overall accuracy of pixel-level classification was 91%, while that of ITC-level classification was 61%. The test results demonstrate the effectiveness of the approach, and in particular the use of robust PCA to prune the hyperspectral dataset and reveal subtle difference among species.

show abstract

Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

Cited by 12 publications

References 4 publications

Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data

Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data

Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing

Individual Tree Species Classification From Airborne Multisensor Imagery Using Robust PCA

Contact Info

Product

Resources

About