Convolutional Neural Networks With Class-Driven Loss for Multiscale VHR Remote Sensing Image Classification

Because land covers always have different scales, multiscale methods are widely used in very-high-resolution (VHR) remote sensing image classification. Traditional multiscale methods usually capture multiscale information by using rectangular windows of different sizes. Each scale contains the same number of training samples and is independently trained. Hence, the training process is time-consuming. In this article, a novel convolutional neural network with a class-driven loss (CNNs-CDL) model is proposed for multiscale VHR remote sensing image classification. First, a multiscale sample construction method is proposed to select a training sample and capture the relationships among different scale samples. The lowest-scale samples are selected on the lowest-resolution image and are mapped to the higher-resolution image without additional label information. Then, a CNN with class-driven loss is trained with the lowest-scale training samples. Class-driven loss can effectively learn the spatial dependence between the nonadjacent samples to promote classification accuracy. Finally, the CNN model is fine-tuned with the higher-scale samples. Although the number of higher-scale training samples increases, the fine-tuning process requires only a small number of iterations to converge. Hence, the proposed model can effectively reduce the training time. The experimental results for three VHR remote sensing images show that the proposed method performs better than several recently proposed methods.