In-lab characterization of HYPSOS, a novel stereo hyperspectral observing system: first results

HYPSOS (HYPerspectral Stereo Observing System, patented) is a novel remote sensing instrument able to extract the spectral information from the two channels of a pushbroom stereo camera; thus it simultaneously provides 4D information, spatial and spectral, of the observed features. HYPSOS has been designed to be a compact instrument, compatible with small satellite applications, to be suitable both for planetary exploration as well for terrestrial environmental monitoring. An instrument with such global capabilities, both in terms of scientific return and needed resources, is optimal for fully characterizing the observed surface of investigation. HYPSOS optical design couples a pair of folding mirrors to a modified three mirror anastigmat telescope for collecting the light beams from the optical paths of the two stereo channels; then, on the telescope focal plane, there is the entrance slit of an imaging spectrograph, which selects and disperses the light from the two stereo channels on a bidimensional detector. With this optical design, the two stereo channels share the large majority of the optical elements: this allowed to realize a very compact instrument, which needs much less resources than an equivalent system composed by a stereo camera and a spectrometer. To check HYPSOS actual performance, we realized an instrument prototype to be operated in a laboratory environment. The laboratory setup is representative of a possible flight configuration: the light diffused by a surface target is collimated on the HYPSOS channel entrance apertures, and the target is moved with respect to the instrument to reproduce the in- flight pushbroom acquisition mode. Here we describe HYPSOS and the ground support equipment used to characterize the instrument, and show the preliminary results of the instrument alignment activities

Hyperspectral Data Compression Using Fully Convolutional Autoencoder

In space science and satellite imagery, better resolution of the data information obtained makes images clearer and interpretation more accurate. However, the huge data volume gained by the complex on-board satellite instruments becomes a problem that needs to be managed carefully. To reduce the data volume to be stored and transmitted on-ground, the signals received should be compressed, allowing a good original source representation in the reconstruction step. Image compression covers a key role in space science and satellite imagery and, recently, deep learning models have achieved remarkable results in computer vision. In this paper, we propose a spectral signals compressor network based on deep convolutional autoencoder (SSCNet) and we conduct experiments over multi/hyperspectral and RGB datasets reporting improvements over all baselines used as benchmarks and than the JPEG family algorithm. Experimental results demonstrate the effectiveness in the compression ratio and spectral signal reconstruction and the robustness with a data type greater than 8 bits, clearly exhibiting better results using the PSNR, SSIM, and MS-SSIM evaluation criteria

Sentinel 2 Time Series Analysis with 3D Feature Pyramid Network and Time Domain Class Activation Intervals for Crop Mapping

In this paper, we provide an innovative contribution in the research domain dedicated to crop mapping by exploiting the of Sentinel-2 satellite images time series, with the specific aim to extract information on “where and when” crops are grown. The final goal is to set up a workflow able to reliably identify (classify) the different crops that are grown in a given area by exploiting an end-to-end (3+2)D convolutional neural network (CNN) for semantic segmentation. The method also has the ambition to provide information, at pixel level, regarding the period in which a given crop is cultivated during the season. To this end, we propose a solution called Class Activation Interval (CAI) which allows us to interpret, for each pixel, the reasoning made by CNN in the classification determining in which time interval, of the input time series, the class is likely to be present or not. Our experiments, using a public domain dataset, show that the approach is able to accurately detect crop classes with an overall accuracy of about 93% and that the network can detect discriminatory time intervals in which crop is cultivated. These results have twofold importance: (i) demonstrate the ability of the network to correctly interpret the investigated physical process (i.e., bare soil condition, plant growth, senescence and harvesting according to specific cultivated variety) and (ii) provide further information to the end-user (e.g., the presence of crops and its temporal dynamics)

Two New Datasets for Italian-Language Abstractive Text Summarization

Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets