Iterative Gaussian-Laplacian Pyramid Network for Hyperspectral Image Classification.

Gaussian pyramid (GP) is a commonly used image coding technique which encodes an image as a pyramid which is stacked by a set of images with Gaussian window-reduced sizes and multiple spatial resolutions. Associated with GP a Laplacian pyramid (LP) can be also constructed to represent differential images between images in two consecutive layers of GP. Such resulting Gaussian-Laplacian pyramid (GLP) performs data compression in a lossless and lossy manner. A convolutional neural network (CNN) consists of a series of layers concatenated in a feedforward manner where each layer has a convolutional sublayer (CL) and a pooling sublayer (PL). Interestingly, each layer implemented by CL and PL in a CNN can be realized by a single layer in GP in the sense that CL and PL can be carried out by a low-pass Gaussian filter operated as a Gaussian kernel in a single layer of GP. This paper develops a new approach to hyperspectral image classification (HSIC), called Gaussian-Laplacian pyramid network (GLPN) which uses not only GP to realize CNN, but also LP to capture differential information between two consecutive layers that CNN cannot. Furthermore, by incorporating an iterative process into GLPN we can derive an iterative GLPN (IGLPN) that can be considered as a companion of a recently developed iterative random training sampling CNN (IRTS-CNN) by replacing CNN with GLPN. Since GLPN can realize CNN in a better way, it is expected that IGLPN will perform better than IRTS-CNN and also significantly reduce computational efficiency compared to IRTS-CNN.