Hyperspectral data usually consists of hundreds of narrow spectral bands and provides more detailed spectral characteristics compared to commonly used multispectral data in remote sensing applications. However, highly correlated spectral bands in hyperspectral data lead to computational complexity, which limits many applications or traditional methods when applied to hyperspectral data. The dimensionality reduction of hyperspectral data becomes one of the most important pre-processing steps in hyperspectral data analysis. Recently, deep reinforcement learning (DRL) has been introduced to hyperspectral data band selection (BS); however, the current DRL methods for hyperspectral data BS simply remove redundant bands, lack the significance analysis for the selected bands, and the reward mechanisms used in DRL only take basic forms in general. In this paper, a new reward mechanism strategy has been proposed, and Double Deep Q-Network (DDQN) is introduced during BS using DRL to improve the network stabilities and avoid local optimum. To verify the effect of the proposed BS method, land cover classification experiments were designed and carried out to analyze and compare the proposed method with other BS methods. In the land cover classification experiments, the overall accuracy (OA) of the proposed method can reach 98.37%, the average accuracy (AA) is 95.63%, the kappa coefficient (Kappa) is 97.87%. Overall, the proposed method is superior to other BS methods. Experiments have also shown that the proposed method works not only for airborne hyperspectral data (AVIRIS and HYDICE), but also for hyperspectral satellite data, such as PRISMA data. When hyperspectral data is applied to similar applications, the proposed BS method could be a candidate for the BS preprocessing options.

Shallow water bathymetry is critical in understanding and managing marine ecosystems. Bathymetric inversion models using airborne/satellite multispectral data are an efficient way to retrieve shallow bathymetry due to the affordable cost of airborne/satellite images and less field work required. With the increasing availability and popularity of unmanned aerial vehicle (UAV) imagery, this paper explores a new approach to obtain bathymetry using UAV visual-band (RGB) images. A combined approach is therefore proposed for retrieving bathymetry from aerial stereo RGB imagery, which is the combination of a new stereo triangulation method (an improved projection image based two-medium stereo triangulation method) and spectral inversion models. In general, the inversion models require some bathymetry reference points, which are not always feasible in many scenarios, and the proposed approach employs a new stereo triangulation method to obtain reliable bathymetric points, which act as the reference points of the inversion models. Using various numbers of triangulation points as the reference points together with a Geographical Weighted Regression (GWR) model, a series of experiments were conducted using UAV RGB images of a small island, and the results were validated against LiDAR points. The promising results indicate that the proposed approach is an efficient technique for shallow water bathymetry retrieval, and together with UAV platforms, it could be deployed easily to conduct a broad range of applications within marine environments.

Currently, the practical solution to remove the errors and artifacts in the digital surface models (DSM ) through stereo images is still manual or semiautomatic editing those affected patches. Although some degrees of semiautomation can be gained, the DSM refinement remains a labor consuming and expensive process. This paper proposes a new method to correct errors in DSM or/and refine an existing coarse DSM. The method employs the concept of projected images together with some image matching techniques to correct/ refine the affected regions in DSM. Since projected images are used, the proposed method can greatly simplify the complicated coordinate transformations and pixel resampling; therefore, the errors/artifacts in DSM can be amended more efficiently and accurately. Several experiments demonstrate the practical usefulness of the proposed method under some scenarios, and some potential improvements are also pointed out to accommodate the various needs during refining DSM.