&lt;title&gt;Principal component analysis of remote sensing imagery: effects of additive and multiplicative noise&lt;/title&gt;

Corner, Brian R.; Narayanan, Ram M.; Reichenbach, Stephen E.

doi:10.1117/12.365833

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gabor-based kernel PCA 2.3.1. Kernel PCA PCA (Duda, Hart, & Stork, 2001) has been widely used in many pattern recognition applications, such as face recognition (Belhumeur, Hespanha, & Kriegman, 1997;Turk & Pentland, 1991) and remote sensing (Corner, Narayanan, & Reichenbach, 1999). In this research, PCA was used to define the best subspace such that a set of apple patterns could be sufficiently represented by that subspace.…”

Section: Gabor-wavelet Decompositionmentioning

confidence: 99%

Gabor feature-based apple quality inspection using kernel principal component analysis

Zhu

Jiang

Luo

et al. 2007

Journal of Food Engineering

View full text Add to dashboard Cite

Section: Gabor-wavelet Decompositionmentioning

confidence: 99%

Gabor feature-based apple quality inspection using kernel principal component analysis

Zhu

Jiang

Luo

et al. 2007

Journal of Food Engineering

View full text Add to dashboard Cite

“…As stated above, the RGB orthophotos and DSMs were used to derive six additional data products used to train the SSD models ( Figure 2-P1.1). From the RGB data, two products were derived: a grayscale image and the first component of Principal Components Analysis (PCA) [37], at the same spatial resolution. Four main products were derived from the DSM: slope, slope normalized for frequency distribution, hillshade and Canopy Height Model (CHM).…”

Section: Data Pre-processingmentioning

confidence: 99%

Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model

et al. 2020

View full text Add to dashboard Cite

Traditional methods for individual tree-crown (ITC) detection (image classification, segmentation, template matching, etc.) applied to very high-resolution remote sensing imagery have been shown to struggle in disparate landscape types or image resolutions due to scale problems and information complexity. Deep learning promised to overcome these shortcomings due to its superior performance and versatility, proven with reported detection rates of ~90%. However, such models still find their limits in transferability across study areas, because of different tree conditions (e.g., isolated trees vs. compact forests) and/or resolutions of the input data. This study introduces a highly replicable deep learning ensemble design for ITC detection and species classification based on the established single shot detector (SSD) model. The ensemble model design is based on varying the input data for the SSD models, coupled with a voting strategy for the output predictions. Very high-resolution unmanned aerial vehicles (UAV), aerial remote sensing imagery and elevation data are used in different combinations to test the performance of the ensemble models in three study sites with highly contrasting spatial patterns. The results show that ensemble models perform better than any single SSD model, regardless of the local tree conditions or image resolution. The detection performance and the accuracy rates improved by 3–18% with only as few as two participant single models, regardless of the study site. However, when more than two models were included, the performance of the ensemble models only improved slightly and even dropped.

show abstract

<title>Principal component analysis of remote sensing imagery: effects of additive and multiplicative noise</title>

Cited by 2 publications

References 0 publications

Gabor feature-based apple quality inspection using kernel principal component analysis

Gabor feature-based apple quality inspection using kernel principal component analysis

Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model

Contact Info

Product

Resources

About