| Autor: |
Wickramathilaka, H.M.K.D., Fernando, D., Jayasundara, D., Wickramasinghe, D., Ranasinghe, D.Y.L., Godaliyadda, G.M.R.I., Ekanayake, M.P.B., Herath, H.M.V.R., Ramanayake, L., Senarath, N., Weerasooriya, H.M.H.K. |
| Předmět: |
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| Zdroj: |
European Journal of Remote Sensing; Dec2023, Vol. 56 Issue 1, p1-18, 18p |
| Abstrakt: |
This study introduces GAUSS (Guided encoder-decoder Architecture for hyperspectral Unmixing with Spatial Smoothness), a novel autoencoder-based architecture for hyperspectral unmixing (HU). GAUSS consists of an Approximation Network (AN), Unmixing Network (UN), and a Mixing Network (MN). The AN incorporates spatial context within a hyperspectral pixel's neighborhood, while the UN utilizes a pseudo-ground truth mechanism to enhance abundance estimation. The MN provides estimated endmembers' signatures. By incorporating UN-produced abundances, unlike the conventional AE model, GAUSS overcomes the single-layer constraint of the MN. Thereafter, a secondary training phase improves the accuracy of endmembers and abundance estimation using a reliable Signal Processing (SP) algorithm, resulting in superior HU performance. The results demonstrate the effectiveness of GAUSS on two Standard datasets and a Simulated dataset compared to the state-of-the-art SP and Deep Learning (DL) based methods. This signifies the benefit of integrating an SP algorithm in the training process, contributing to advancements in DL-based HU techniques. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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