Spectral Discrimination Method Based on Information Divergence of Gradient

Xiubao, 张修宝 Zhang; Yan, 袁艳 Yuan; Qian, 王潜 Wang

doi:10.3788/aos201131.0530001

Cited by 1 publication

(1 citation statement)

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“…Euclidean Distance sketch in spectral space, each pixel corresponds to a multidimensional spectral vector, and the angle between the two vectors is defined as spectral angle (Fig. 3) [23].The smaller the spectral angle is, the more similar the two spectra are and the more likely they are to belong to the same kind of features. Similarly, the Spectral Angle Distance makes full use of the information of the pixel in the spectral dimension, but different from the Euclidean Distance, the Spectral Angle Distance is not affected by the light, shadow and other conditions, that is, when the brightness value increases or decreases, its angular direction will remain unchanged.…”

Section: B Spectral Angle Distancementioning

confidence: 99%

Reliability Evaluation and Migration of Wetland Samples

Yan

Niu

2021

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

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As one of the most important steps in classification and mapping research, sample acquisition and updating cost a lot of time and energy of researchers. The high temporal and spatial dynamic characteristics of wetland makes the reliable wetland sample selection more challenging. It is especially important for time series wetland classification to address the problem of how to apply sample sets to images with different periods. This paper evaluated the reliability of the historical samples by using optical and SAR remote sensing data from the perspective of water inundation frequency, and three sample migration methods based on rule set, reclassification and spectral similarity were proposed to carry out wetland classification experiment in the Tibetan Plateau. In addition, the relationship between the number of samples and classification accuracy is analyzed. The migration and reuse of sample sets can quickly obtain accurate wetland sample sets, which lays the foundation for the study of wetland mapping in time series.

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Section: B Spectral Angle Distancementioning

confidence: 99%