Monitoring land cover information is fundamental for environmental change studies

land resource management

and sustainable development；it plays an important role in the global resource monitoring and change detection.Improving the land cover classification accuracy on moderate resolution remote sensing images is considerably significant.For land cover classification

some researchers have combined the texture features with spectral values.The classification accuracy has improved；however

the ability of using texture features to improve the classification accuracy is still extremely limited.Extracting expressive features is the key to remote sensing image classification.In recent years

the deep convolutional neural network (CNN) has made a great breakthrough in the fields of image classification

object detection

and image semantic segmentation；it has a remarkable capability for feature learning and representation compared with the traditional machine learning methods.This feature extraction capability of CNN in improving the accuracy of land cover classification should be investigated. Given the superior characteristics of CNNs

this paper conducts an exploratory research on the classification of moderate resolution remote sensing images using features extracted by deep CNN.In detail

the GF-1 multispectral remote sensing imagery with 16 m resolution is used as experimental data

and the pretrained AlexNet is used for feature extraction using the support vector machine (SVM) as the classifier.The proposed method has three steps：1) Preprocessing：The image that is inputted to AlexNet must include three bands

because the pretrained AlexNet is trained on ImageNet.Thus

the principal component analysis is applied to the GF-1 four-band multispectral image to derive the first three principal components.2) Deep feature extraction：For each pixel

the surrounding image patch with a fixed window size is initially upsampled to 224×224 by the nearest neighbor interpolation method to conform to the required input size of the AlexNet model；the mean image of train set is then subtracted for normalization and is finally fed into the pretrained AlexNet to extract the deep features of convolutional layers or full-connected layers.3) Classification：The extracted deep features are fed into the SVM classifier with a linear kernel.The capability of feature representation for various layers of AlexNet and the effectiveness of different window sizes are evaluated and analyzed.Furthermore

the comparisons with classification results

which are obtained by spectral values and spectral-texture features

are conducted to assess the potential of the proposed method. Experimental results include the following：1) Features of full-connected layers are more effective than those of convolutional layers

and the best feature extraction layer of AlexNet is the 6th full-connected layer.2) The window size for feature extraction has an influence on classification results.Accuracy initially increases and then decreases in the end with the increase of size.The best window size is 9×9.3) The classification accuracy obtained by using deep features of AlexNet is higher than those performed using only spectral values and spectral-texture features for classification.4) The proposed method has a drawback because of the ReLU nonlinear activation function and max pooling in AlexNet；moreover

a phenomenon of expansion or shrinkage of actual outline exist for the classes with extremely high or extremely low spectral values. In conclusion

the deep CNN can extract accurate features for land covers and obtain high classification accuracy

providing a valuable reference for land cover classification.In the future work

some further measures

such as selecting effective activation function and pooling method to build and train a new CNN model for land cover classification

must be considered to improve the proposed method.