DHCAE: Deep Hybrid Convolutional Autoencoder Approach for Robust Supervised Hyperspectral Unmixing

Abstract

Hyperspectral unmixing (HSU) is a crucial method to determine the fractional abundance of the material (endmembers) in each pixel. Most spectral unmixing methods are affected by low signal-to-noise ratios because of noisy pixels and bands simultaneously, requiring robust HSU techniques that exploit both 3D (spectral–spatial dimension) and 2D (spatial dimension) domains. In this paper, we present a new method for robust supervised HSU based on a deep hybrid (3D and 2D) convolutional autoencoder (DHCAE) network. Most HSU methods adopt the 2D model for simplicity, whereas the performance of HSU depends on spectral and spatial information. The DHCAE network exploits spectral and spatial information of the remote sensing images for abundance map estimation. In addition, DHCAE uses dropout to regularize the network for smooth learning and to avoid overfitting. Quantitative and qualitative results confirm that our proposed DHCAE network achieved better hyperspectral unmixing performance on synthetic and three real hyperspectral images, i.e., Jasper Ridge, urban and Washington DC Mall datasets.

Spatial Validation of Spectral Unmixing Results: A Systematic Review

“…It is important to emphasize that 97% of these reference maps are available online together with hyperspectral images and/or reference spectral libraries (e.g., [532-535] Figure 8). Therefore, these images were well known: Cuprite (Nevada, USA, e.g., [70,458]), Indian Pines (Indiana, USA, e.g., [78,458]), Jasper Ridge (California, USA, e.g., [68,97]), Salinas Valley (California, USA, e.g., [75,78]) datasets that were acquired with AVIRIS sensors; Pavia (Italy, e.g., [81,85]) datasets that were acquired with the ROSIS sensor; Samson (Florida, USA, e.g., [59,89]) dataset that was acquired with the Samson sensor; University of Houston (Texas, USA, e.g., [59,78],) dataset that was acquired with the CASI-1500 sensor ; Urban (Texas, USA, e.g., [59,68]) and Washington DC Mall (Washington DC, USA, e.g., [81,90]) datasets that were acquired with the HYDICE sensor. As regards the papers that analyzed the multispectral data, most of the authors chose to create the reference maps from the other images, whereas most of the authors that analyzed the hyperspectral data chose to employ the previous reference maps.…”

Multi-scale convolutional autoencoder network-based Implicit Information mining of cultural relics

“…And the simple operation of downsampling to expand the field of perception will lose the detailed information in the image. As the number of layers in a network increases, there is often a problem of missing hidden information components in abundance maps [7][8][9] .…”