This paper implements a novel approach to automatically detect and classify rockfalls in Lunar Reconnaissance Orbiter narrow angle camera (NAC) images using a single-stage dense object detector (RetinaNet). The convolutional neural network has been trained with a data set of 2932 original rockfall images. In order to avoid overfitting, the initial training data set has been augmented during training using random image rotation, scaling, and flipping. Testing images have been labelled by human operators and have been used for RetinaNet performance evaluation. Testing shows that RetinaNet is capable to reach recall values between 0.98 and 0.39, precision values between 1 and 0.25, and average precisions ranging from 0.89 to 0.69, depending on the used confidence threshold and intersection-over-union values. Mean processing time of a single NAC image in RetinaNet is around 10 s using a GeForce GTX 1080 Ti and GeForce Titan Xp, which is in orders of magnitudes faster than a human operator. The processing speed allows to efficiently exploit the currently available NAC data stack with more than 1 million images in a reasonable timeframe. The combination of speed and detection performance can be used to produce lunar rockfall distribution maps on large spatial scales for utilization by the scientific and engineering community.

Hyperspectral sensors capture a portion of the visible and near-infrared spectrum with many narrow spectral bands. This makes it possible to better discriminate objects based on their reflectance spectra and to derive more detailed object properties. For technical reasons, the high spectral resolution comes at the cost of lower spatial resolution. To mitigate that problem, one may combine such images with conventional multispectral images of higher spatial, but lower spectral resolution. The process of fusing the two types of imagery into a product with both high spatial and spectral resolution is called hyperspectral super-resolution. We propose a method that performs hyperspectral super-resolution by jointly unmixing the two input images into pure reflectance spectra of the observed materials, along with the associated mixing coefficients. Joint super-resolution and unmixing is solved by a coupled matrix factorization, taking into account several useful physical constraints. The formulation also includes adaptive spatial regularization to exploit local geometric information from the multispectral image. Moreover, we estimate the relative spatial and spectral responses of the two sensors from the data. That information is required for the super-resolution, but often at most approximately known for real-world images. In experiments with five public datasets, we show that the proposed approach delivers up to 15% improved hyperspectral super-resolution.

The Sentinel-2 satellite mission delivers multi-spectral imagery with 13 spectral bands, acquired at three different spatial resolutions. The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance - GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution. We employ a state-of-the-art convolutional neural network (CNN) to perform end-to-end upsampling, which is trained with data at lower resolution, i.e., from 40->20 m, respectively 360->60 m GSD. In this way, one has access to a virtually infinite amount of training data, by downsampling real Sentinel-2 images. We use data sampled globally over a wide range of geographical locations, to obtain a network that generalises across different climate zones and land-cover types, and can super-resolve arbitrary Sentinel-2 images without the need of retraining. In quantitative evaluations (at lower scale, where ground truth is available), our network, which we call DSen2, outperforms the best competing approach by almost 50% in RMSE, while better preserving the spectral characteristics. It also delivers visually convincing results at the full 10 m GSD. The code is available at https://github.com/lanha/DSen2