4 research outputs found
Spectral-Spatial Graph Reasoning Network for Hyperspectral Image Classification
In this paper, we propose a spectral-spatial graph reasoning network (SSGRN)
for hyperspectral image (HSI) classification. Concretely, this network contains
two parts that separately named spatial graph reasoning subnetwork (SAGRN) and
spectral graph reasoning subnetwork (SEGRN) to capture the spatial and spectral
graph contexts, respectively. Different from the previous approaches
implementing superpixel segmentation on the original image or attempting to
obtain the category features under the guide of label image, we perform the
superpixel segmentation on intermediate features of the network to adaptively
produce the homogeneous regions to get the effective descriptors. Then, we
adopt a similar idea in spectral part that reasonably aggregating the channels
to generate spectral descriptors for spectral graph contexts capturing. All
graph reasoning procedures in SAGRN and SEGRN are achieved through graph
convolution. To guarantee the global perception ability of the proposed
methods, all adjacent matrices in graph reasoning are obtained with the help of
non-local self-attention mechanism. At last, by combining the extracted spatial
and spectral graph contexts, we obtain the SSGRN to achieve a high accuracy
classification. Extensive quantitative and qualitative experiments on three
public HSI benchmarks demonstrate the competitiveness of the proposed methods
compared with other state-of-the-art approaches
Hyperspectral Image Classification with Context-Aware Dynamic Graph Convolutional Network
In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance. However, conventional spatial context-based methods simply assume that spatially neighboring pixels should correspond to the same land-cover class, so they often fail to correctly discover the contextual relations among pixels in complex situations, and thus leading to imperfect classification results on some irregular or inhomogeneous regions such as class boundaries. To address this deficiency, we develop a new HSI classification method based on the recently proposed graph convolutional network (GCN), as it can flexibly encode the relations among arbitrarily structured non-Euclidean data. Different from traditional GCN, there are two novel strategies adopted by our method to further exploit the contextual relations for accurate HSI classification. First, since the receptive field of traditional GCN is often limited to fairly small neighborhood, we proposed to capture long-range contextual relations in HSI by performing successive graph convolutions on a learned region-induced graph which is transformed from the original 2-D image grids. Second, we refine the graph edge weight and the connective relationships among image regions simultaneously by learning the improved similarity measurement and the 'edge filter,' so that the graph can be gradually refined to adapt to the representations generated by each graph convolutional layer. Such updated graph will in turn result in faithful region representations, and vice versa. The experiments carried out on four real-world benchmark data sets demonstrate the effectiveness of the proposed method