Land-use classification via constrained extreme learning classifier based on cascaded deep convolutional neural networks

Accurate land-use classification is essential for the management and supervision of urban development, land resources and environment sustainability. Feature extractor and classifier are the important modules of land-use classification. Deep convolutional neural network has been proved to be able to learn more robust and discriminative features from images. In this paper, we increase the diversity and discriminative of features by fusing features extracted by three deep convolutional neural networks with different architectures, which are obtained by fine-tuning the pre-trained models with land-use image dataset. In order to make the classification faster and have excellent generalization performance, we select constrained extreme learning machine instead of fully connected layer or support vector machine. Experimental results show that the proposed method can achieve a better performance with the overall classification accuracy of 98.35%, compared with other state-of-the-art methods.