The use of hyperspectral images (HSI) and 3D data has proven to be an efficient combination for numerous applications. A common method of obtaining corresponding data is 3D reconstruction from HSI, for which the local feature descriptor is vital to the final accuracy. However, redundant bands may hamper the performance of the descriptor, which is similar to the so-called Hughes phenomenon. Band selection (BS) is effective in overcoming such problems. Existing BS methods fail to select the band in a differentiable manner and, thus, cannot be jointly optimized with downstream tasks. In this letter, we propose a novel end-to-end HSI local feature descriptor network (with joint optimal band selection) called HyperDesc. It implements a true band selection module by turning band selection into a CONCRETE random variable-based differentiable sampling operation. The discrete distribution of each selected band can be learned from input HSI with a non-local spectral-spatial attention network. Finally, the selected band is sent to a descriptor network to extract the local feature descriptor. The whole network is trained in an end-to-end manner. Experiments are conducted on multi-view close range HSI. The results show that spectral information provided by selected bands can boost the performance of the descriptor.

Geological hazard risk assessment provides essential scientific support for geological disaster prevention and governance. The selection of appropriate evaluation factors is crucial to the accuracy and practicality of the risk assessment results. The existing factors for geological hazard risk assessment often suffer from issues such as poor timeliness and insufficient completeness. Interferometric Synthetic Aperture Radar (InSAR) technology, which offers large-scale, high spatiotemporal resolution monitoring of surface deformation, can effectively compensate for the shortcomings of existing risk assessment factors. How to effectively integrate time-series InSAR deformation results into geological hazard risk assessment has become a focus of research. This study fully considers the time-series InSAR deformation information; both the ascending and descending orbit results of the time-series InSAR deformation are introduced as two categories of evaluation factors in the risk assessment model. Subsequently, 11 types of assessment factors are selected by the Pearson correlation coefficient method, while the Information Volume Model and Evidence Weight Model are applied in the partitioning and assessment of risks in Xiaojin County, China. Finally, ROC (Receiver Operating Characteristic Curve) analysis is utilized to compare the accuracy of model evaluations before and after incorporating time-series InSAR deformation results. The results indicate that: (1) after incorporating time-series InSAR deformation monitoring results as evaluation factors into the information volume model and evidence weight model, the evaluation accuracy of the two models improved by 9.69% and 11.26%, respectively; (2) there are differences in risk partitioning among different evaluation models. From the risk partitioning result of Xiaojin County in this study, the evaluation accuracy of the information volume model is higher than that of the evidence weight model, and the performance is more prominent after adding the time-series InSAR deformation results.