ECA-MobileNetV3(Large)+SegNet Model for Binary Sugarcane Classification of Remotely Sensed Images

Abstract

In recent years, several models based on fully convolutional neural networks have been proposed. These models mainly focused on improving accuracy but ignored computational efficiency. For this reason, this research proposes an innovative deep learning model, entitled “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECA-MobileNetV3(large)+Seg-Net model</i> ” for simultaneously concerning both. In the encoder, the structure-reduced MobileNetV3(large) is selected as the backbone network, which uses 11 network layers to replace 20 network layers of MobileNetV3(large). The hybrid dilated convolution with dilation rates of 1, 2, and 4 is introduced in the depthwise separable convolution to expand the local receptive field and to enhance the connection of contextual information. Finally, the ECA module is fused with the bneck structure. In the decoder, the 18 network layers of SegNet’s decoder are replaced with 9 layers to achieve a lightweight network with small parameters. When compared with the original SegNet, the overall accuracy (OA) of the model proposed in this research is averagely improved by 5.97%, 5.56%, and 4.13%, and the sugarcane identification accuracy is averagely improved by 7.16%, 4.01%, and 9.13%, respectively in the three tested areas. Additionally, the memory size, the number of parameters, and FLOPs are all reduced by 6/7.