An Efficient Technique for Hyperspectral Endmember Extraction based on SE
نویسنده
چکیده
Hyperspectral Endmember extraction of a set of accurateendmembers is critical for the proper unmixing of Hyperspectral image. Several preprocessing algorithms such as spatial preprocessing (SPP), region based spatial preprocessing (RBSPP), and spatial spectral preprocessing (SSPP) have been developed for the extraction of endmembers. These algorithms require complex operations and huge computational effort. Thus, this paper proposes an efficient preprocessing technique for hyperspectralendmember extraction called SE 2 PP which is based on the integration of spatial and spectral information. The proposed approach can be combined with the existing algorithms for endmember extraction reducing the computational complexity of those algorithms while providing similar figures of accuracy.
منابع مشابه
An image-based endmember bundle extraction algorithm using reconstruction error for hyperspectral imagery
Although many endmember extraction algorithms have been proposed for hyperspectral images in recent years, there are still some problems in endmember extraction which would lead to inaccurate endmember extraction. One important problem is the variation in endmember spectral signatures due to spatial and temporal variability in the condition of scene components and differential illumination cond...
متن کاملUnsupervised classification strategy utilizing an endmember extraction technique for airborne hyperspectral remotely sensed imagery
Remote sensing has become an important source of urban land-use/cover classification, and as a result of their high spatial and spectral resolution, airborne hyperspectral images have been widely used to distinguish different urban classes. However, the previous studies into the classification of urban environments have mainly focused on a supervised scenario, which is limited by the selection ...
متن کاملNew Divide and Conquer Method on Endmember Extraction Techniques
In hyperspectral imagery, endmember extraction (EE) is a main stage in hyperspectral unmixing process where its role lies in extracting distinct spectral signature, endmembers, from hyperspectral image which is considered as the main input for unsupervised hyperspectral unmixing to generate the abundance fractions for every pixel in hyperspectral data. EE process has some difficulties. There ar...
متن کاملSpatial-Spectral Preprocessing for Endmember Extraction on GPU’s
Spectral unmixing is focused in the identification of spectrally pure signatures, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Mainly focused on the spectral information contained in the hyperspectral images, endmember extraction techniques have recently included spatial information to achieve more accurate results. Several algorithms have been d...
متن کاملUrban Vegetation Mapping from Fused Hyperspectral Image and LiDAR Data with Application to Monitor Urban Tree Heights
Urban vegetations have infinite proven benefits for urban inhabitants including providing shade, improving air quality, and enhancing the look and feel of communities. But creating a complete inventory is a time consuming and resource intensive process. The extraction of urban vegetation is a challenging task, especially to monitor the urban tree heights. In this study we present an efficient e...
متن کامل