Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery
نویسندگان
چکیده
منابع مشابه
Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery
Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HSI) that possesses many homogenous areas. In thi...
متن کاملAn Adaptive Dimensionality Reduction Approach for Hyperspectral Imagery Semantic Interpretation
With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the semantic interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact,...
متن کاملOverlap-based feature weighting: The feature extraction of Hyperspectral remote sensing imagery
Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weigh...
متن کاملDetermining the Dimensionality of Hyperspectral Imagery
There are a number of reasons why reduction of large data sets is necessary, for example the amount of data may be too large for some data mining programs. Something the amount of data may exceed the processing capability of a program, as it is usual in the case of hyperspectral images. The data has generally a large number of variables to analyze, some of which have more input than others. It ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Remote Sensing
سال: 2017
ISSN: 2072-4292
DOI: 10.3390/rs9080790