Spatial–Spectral Involution MLP Network for Hyperspectral Image Classification

Abstract

Recently, more and more multi-layer perceptron (MLP) -like models have been proposed. Among them, CycleMLP is good at dense feature prediction tasks, which is potentially useful for hyperspectral image (HSI) classification. However, the receptive field of CycleMLP tends to be cross-shaped, which will lead to insufficient spatial information extraction. Additionally, most of the HSI classification methods only use information from single HSI data. Lack of diversity in the features of a single modality limits classification performance. To address these issues, a novel spatial-spectral involution MLP network (SSIN) is proposed for HSI classification. SSIN contains two paths for extracting different kinds of information, namely the image path and the coordinate path. In the image path, we combine the MLP structure with the involution operation and propose involution MLP (InvoMLP). It obtains the spatial kernel weights corresponding to each pixel individually, thus improving the spatial interaction capability. At the same time, InvoMLP has the same receptive field range as conventional convolution, i.e., a rectangular receptive field. In the coordinate path, we build a lightweight module for extracting information. Unlike the information of images, the coordinates are intuitive information about the location distribution. Considering that the coordinate information contains the global spatial distribution of HSI, fusing it with the image information could improve long-distance dependencies of feature maps. Experimental results on four HSI datasets illustrate that SSIN can outperform some state-of-the-art methods.

Optical Frequency Domain Reflectometry Based on Multilayer Perceptron

“…MLP is a traditional, fully connected neural network that consists of an input layer, hidden layers, and output layer, in which the number of hidden layers can be set according to research needs. MLP currently plays an important role in dealing with regression and classification problems, and it has been successfully applied to image processing [ 37 ], semantic processing [ 38 ], and financial analysis [ 39 ]. The schematic diagram of MLP used in this manuscript to process OFDR data is shown in Figure 2 .…”

A Hybrid Text Recognition Approach based on Involution Operators and Graph Convolution Neural Networks

“…The DRIN used an expanded involution kernel to not only model long-range spatial interactions well but also to implement feature learning in a fairly lightweight manner. Subsequently, a novel spatial-spectral involution multilayer perceptron (MLP) network (SSIN) combined the MLP structure with the involution operation (Shao et al 2022). It can obtain the spatial kernel weights corresponding to each pixel individually, solving the difficulty of insufficient spatial information extraction.…”