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- W4294068045 abstract "Hyperspectral imaging technology is a very useful technique in remote sensing and hyperspectral sensors receive more than hundred spectral bands from the electromagnetic spectrum. Hyperspectral image (HSI) is produced by each pixel of vector which is used to identify the unique materials of objects. Due to becoming large spectral bands, the classification of image is a more arduous task. To confront a problem, filtering techniques have become a hot research topic. The filtering techniques are employed for improving the HSI classification. This work discusses spatial feature extraction by using Domain Transform Interpolated Convolution Filter (DTICF). There are three steps for the proposed techniques: First, the spatial features are obtained by using DTICF. Second, those obtained spatial in the first processing and spectral bands of HSI are provided to design 3-Dimensional-Convolutional Neural Network (3D-CNN) with and Bi-directional-Long Short Term Memory (Bi-LSTM) framework. In the third step, the obtained probabilistic classification map of 3D-CNN- Bi-LSTM is smoothed by Markov Random Field (MRF) efficiently. Based on the experimental results, two different hyperspectral images prove that DTICF-3D-CNN-Bi-LSTM approach is more important and provides good classification results compared to other classification approaches." @default.
- W4294068045 created "2022-09-01" @default.
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- W4294068045 date "2022-09-02" @default.
- W4294068045 modified "2023-09-26" @default.
- W4294068045 title "A Spectral-Spatial Classification of Hyperspectral Image Using Domain Transform Interpolated Convolution Filter" @default.
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- W4294068045 doi "https://doi.org/10.1007/978-981-19-2840-6_33" @default.
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