An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing

With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained.

DE-striping hype spectral Remote Sensing Images using Deep Convolutional Neural Network

Hyperspectral far off detecting records reflectance or emittance information in a huge amount of bordering and tight unearthly groups, and accordingly has numerous data in distinguishing and planning the mineral zones. Then again, the science and natural information gives us some other productive data about the actual qualities of pictures and channels that have been recorded from the surface. In this work, we focus on de-striping the hyperspectral remote sensing images on Hyperion data by applying Deep Convolutional Neural Network (DCNN). What is clear is the high significance of applying the sufficient pre-preparing on Hyperion information as a result of low sign to-commotion proportion. By contrasting the known layers of DCNN model for de-striping hyperspectral pictures. The results obtained by applying the mentioned methods, it is revealed that all the higher stripes in an image as well as black color has been reduced and entirely associated with the Hyperion data alteration, and in contrast, the Hyperion imagery successfully corresponds to the de-striping of hyperspectral image with an accuracy of 91.89% using DCNN model. The proposed DCNN is capable of reaching high accuracy 150s after the start of the evaluation phase and never reaches low accuracy. The pre-trained DCNN model approach would be an adequate solution considering de-striping as its high inference time is lower compared existing available methods which are not as efficient for de-striping.