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Abstract:The complexity of the single linear hyperspectral pushbroom imaging based on a high altitude airship (HAA) without a three-axis stabilized platform is much more than that based on the spaceborne and airborne. Due to the effects of air pressure, temperature and airflow, the large pitch and roll angles tend to appear frequently that create pushbroom images highly characterized with severe geometric distortions. Thus, the in-flight calibration procedure is not appropriate to apply to the single linear pushbroom sensors on HAA having no three-axis stabilized platform. In order to address this problem, a new ground-based boresight calibration method is proposed. Firstly, a coordinate's transformation model is developed for direct georeferencing (DG) of the linear imaging sensor, and then the linear error equation is derived from it by using the Taylor expansion formula. Secondly, the boresight misalignments are worked out by using iterative least squares method with few ground control points (GCPs) and ground-based side-scanning experiments. The proposed method is demonstrated by three sets of experiments: (i) the stability and reliability of the method is verified through simulation-based experiments; (ii) the boresight calibration is performed using ground-based experiments; and (iii) the validation is done by applying on the orthorectification of the real hyperspectral pushbroom images from a HAA Earth observation payload system developed by our research team-"LanTianHao". The test results show that the proposed boresight calibration approach significantly improves the quality of georeferencing by reducing the geometric distortions caused by boresight misalignments to the minimum level.

Automatic fusion of different kinds of image datasets is so intractable with diverse imaging principle. This paper presents a novel method for automatic fusion of two different images: 2D hyperspectral images acquired with a hyperspectral camera and 3D laser scans obtained with a laser scanner, without any other sensor. Only a few corresponding feature points are used, which are automatically extracted from a scene viewed by the two sensors. Extraction method of feature points relies on SURF algorithm and camera model, which can convert a 3D laser scan into a 2D laser image with the intensity of the pixels defined by the attributes in the laser scan. Moreover, Collinearity Equation and Direct Linear Transformation are used to create the initial corresponding relationship of the two images. Adjustment is also used to create corrected values to eliminate errors. The experimental result shows that this method is successfully validated with images collected by a hyperspectral camera and a laser scanner.

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Based on field geological survey, stratigraphic section measurement and indoor comprehensive investigation, the Zanda Basin's tectonic location in the Himalaya Plate was ascertained, and the formation and evolution of the Zanda Basin during the Pliocene to Early Pleistocene was classified as six stages: (a) primary rift‐faulting stage, (b) quick rift‐faulting Stage, (c) intensive rift‐faulting stage, (d) stasis stage, (e) secondary rift‐faulting stage, and (f) secondary quick rift‐faulting stage. Based on this six‐staged formation‐evolution theory of the Zanda Basin, the upwelling process of the Western Himalaya Mountains from the Pliocene to Early Pleistocene was classified as the following five stages: (a) slow upwelling stage (5.4–4.4 Ma), (b) mid‐velocity upwelling stage (4.4–3.5 Ma), (c) quick upwelling stage (3.5–3.2 Ma), (d) upwelling‐ceasing stage (3.2–2.7 Ma), and (e) quick upwelling stage (2.7 Ma). Research has shown that in the duration from the Early Pliocene (4.7 Ma) to the End of Pliocene (2.67 Ma), which lasted 2.03 million years, the Himalaya Mountains had uplifted 1500 m at a velocity of 0.74 mm/a; this belongs to a mid‐velocity upwelling. During the 1.31 million years in the Early Stage of the Early Pleistocene, the Himalaya Mountains had risen up another 1500 m at a velocity of 1.15 mm/a; this is a rather quick upwelling. All of these data have shown that the upwelling of the Western Himalaya Mountains is along a complicated process with multi‐stages, multi‐velocities, and non‐uniformitarian features.