Using convolutional neural networks to identify objects from very high-resolution remote sensing imagery

A new method for classifying land cover features in high resolution satellite imagery was developed. The method involves (1) performing image segmentation to delineate homogeneous ground objects in the image, and (2) using a convolutional neural network classifier to extract the land cover of the segments (i.e. whether they represent buildings, cropland, forest, etc.).