Statistical analysis on polarimetric study of lung cancer cells

Shrestha, Suman; Giakos, G.C.; Farrahi, Tannaz; Narayan, Chaya; Livanos, George; Zervakis, M.

doi:10.1109/ist.2013.6729675

Cited by 2 publications

(1 citation statement)

References 13 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, it has been utilized as a representative metric for discriminating polarized measurements based on two populations are normally distributed with unequal variances. Basically, Welch's t test is a modification of the Student's t test, which has been demonstrated with the assist of Student's t-test and is more robust when there is a possibility of unequal variances and unequal sample sizes between the two treatments [Shrestha et al, 2013]. Moreover, the choice of these tests decides what can be achieved from the independent populations, based on the different null hypotheses are constructed to test.…”

Section: Welch's T Testmentioning

confidence: 99%

Spectral-Spatial Classification of Hyperspectral Imagery Based on Stacked Sparse Autoencoder and Random Forest

Zhao

Wan

Zhao

et al. 2017

European Journal of Remote Sensing

View full text Add to dashboard Cite

It is of great interest in exploiting spectral-spatial information for hyperspectral image (HSI) classification at different spatial resolutions. This paper proposes a new spectral-spatial deep learning-based classification paradigm. First, pixel-based scale transformation and class separability criteria are employed to measure appropriate spatial resolution HSI, and then we integrate the spectral and spatial information (i.e., both implicit and explicit features) together to construct a joint spectral-spatial feature set. Second, as a deep learning architecture, stacked sparse autoencoder provides strong learning performance and is expected to exploit even more abstract and high-level feature representations from both spectral and spatial domains. Specifically, random forest (RF) classifier is first introduced into stacked sparse autoencoder for HSI classification, based on the fact that it provides better tradeoff among generalization performance, prediction accuracy and operation speed compared to other traditional procedures. Experiments on two real HSIs demonstrate that the proposed framework generates competitive performance.

show abstract

Section: Welch's T Testmentioning

confidence: 99%