  In this paper, we propose a multi-scale deep feature learning method for
high-resolution satellite image classification. Specifically, we firstly warp
the original satellite image into multiple different scales. The images in each
scale are employed to train a deep convolutional neural network (DCNN).
However, simultaneously training multiple DCNNs is time-consuming. To address
this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since
different SPP-nets have the same number of parameters, which share the
identical initial values, and only fine-tuning the parameters in
fully-connected layers ensures the effectiveness of each network, thereby
greatly accelerating the training process. Then, the multi-scale satellite
images are fed into their corresponding SPP-nets respectively to extract
multi-scale deep features. Finally, a multiple kernel learning method is
developed to automatically learn the optimal combination of such features.
Experiments on two difficult datasets show that the proposed method achieves
favorable performance compared to other state-of-the-art methods.
