Crop Classification Based on Lightened Convolutional Neural Networks in Multispectral Images

Crop classification is a representative problem in multispectral remote sensing image (RSI) classification, and has significance in country food security, ecological security, production estimate, crop growth supervision, and so on. It has attracted increasing attention of many researchers around the world especially after the development of convolutional neural networks (CNN). General CNN-based multispectral RSI classification methods may be not suitable for labeled samples with limited numbers and areas. Other pixel-based classification methods are always affected by noise and ignore spatial information. Focusing on these problems, this paper presents an approach based on lightened CNN for crop classification with a small number of tiny size labeled samples in multispectral images. The contribution of this work is to construct a lightened CNN model for crop classification with small samples in multispectral image. It avoids overfitting of deep CNN and reduces the requirement for the size of training samples. We adopt two-layer fully convolutional network (FCN) to extract features. The first layer uses a convolutional kernel of size 1 and outputs 16-band feature map to obtain spectral band information. Spatial information is extracted in the sequential layer using convolutional kernel of size 3, step 1 and padding 1. Thus the feature map after FCN and the labeled area have the same size. Finally, we use a fully connected layer and a softmax classifier for classification. Our experiment was conducted on 8-band multispectral image of size 50362-by-17810 pixels. There are 5 classes in the multispectral image, namely rice, soy, corn, non-crop, and uncertainty. The experimental result which achieves 86.28% accuracy indicates the good performance of our network for crop classification in multispectral RSIs.