A Lightweight Interlayer Multiscale Dense Network for Classifying Hyperspectral Remote Sensing Images

Abstract

Multiscale Dense Network (MSDN) is a deep convolutional neural network method proposed to solve the loss of fine features in hyperspectral remote sensing images. Multiscale Dense Network (MSDN), which makes full use of the different scale information in the network, implements feature extraction of HSI in both horizontal and vertical dimensions. Since MSDN maintains all fine scales until the last layer of the network, the computational effort is large. To address this problem, this paper proposes a lightweight Inter layer Short Multiscale Dense Network (IMSDN) method for hyperspectral image classification. HSI classification is performed using network reduction and inert computation to avoid unnecessary computation, the MSDN network is divided into S blocks along the horizontal dimension, keeping the coarsest (S-i+1) scale only in the ith block and only down sampling the final output of each block in the Short Multiscale Dense Network (SMSDN) for HSI classification. On the basis of the SMSDN network, in order to maintain the classification accuracy of the network, the Lightweight Interlayer Multiscale Dense Network (IMSDN) utilizes three scales of convolution kernels in different horizontal directions to extract the image features separately, which are then fused for the classification of HSI. Experiments are conducted on two publicly available datasets and the results are compared with other methods to demonstrate that the IMSDN network has less computation and shortens the experiment time compared to the MSDN network while guaranteeing the classification accuracy.

Keywords

• Hyperspectral images
• interlayer
• multiscale dense networks
• lightweighting