  With the rapid growing of remotely sensed imagery data, there is a high
demand for effective and efficient image retrieval tools to manage and exploit
such data. In this letter, we present a novel content-based remote sensing
image retrieval method based on Triplet deep metric learning convolutional
neural network (CNN). By constructing a Triplet network with metric learning
objective function, we extract the representative features of the images in a
semantic space in which images from the same class are close to each other
while those from different classes are far apart. In such a semantic space,
simple metric measures such as Euclidean distance can be used directly to
compare the similarity of images and effectively retrieve images of the same
class. We also investigate a supervised and an unsupervised learning methods
for reducing the dimensionality of the learned semantic features. We present
comprehensive experimental results on two publicly available remote sensing
image retrieval datasets and show that our method significantly outperforms
state-of-the-art.
