General Structure Physics of an Aerial Remote Sensing Platform and Its Systemic Accuracy Criterion

Accuracy is a key factor in high-resolution remote sensing and photogrammetry. The factors that affect accuracy are imaging system errors and data processing errors. Due to the complexity of aerial camera errors, this paper focuses on the design of digital aerial camera systems and the means to reduce system error and data processing inefficiencies. There are many kinds of digital aerial camera systems at present;however, these systems lack a unified physical model, which ultimately leads to more complicated designs and multi-camera modes. Such a system is complex and costly, as it is easily affected by factors such as vibration and temperature. Thus, the installed accuracy can only reach the millimeter level. Here, we describe a unified physical structure for a digital aerial camera that imitates an out-of-field multi-charge-coupled device (CCD), an in-field multi-CCD, and once-imaging and twice-imaging digital camera systems. This model is referred to as the variable baseline-height ratio spatiotemporal model. The variable ratio allows the opto-mechanical spatial parameters to be linked with height accuracy, thus providing a connection to the surface elevation. The twice-imaging digital camera prototype system and the wideband limb imaging spectrometer provide a transformation prototype from the current multi-rigid once-imaging aerial camera to a single rigid structure. Thus, our research lays a theoretical foundation and prototype references for the construction and industrialization of digital aerial systems.

Urban land-use classification by combining high-resolution optical and long-wave infrared images

Multi-sensor and multi-resolution source images consisting of optical and long-wave infrared (LWlR) images are analyzed separately and then combined for urban mapping in this study.The framework of its methodology is based on a two-level classification approach.In the first level,contributions of these two data sources in urban mapping are examined extensively by four types of classifications,i.e.spectral-based,spectral-spatial-based,joint classification,and multiple feature classification.In the second level,an objected-based approach is applied to decline the boundaries.The specificity of our proposed framework not only lies in the combination of two different images,but also the exploration of the LWlR image as one complementary spectral information for urban mapping.To verify the effectiveness of the presented classification framework and to confirm the LWlR's complementary role in the urban mapping task,experiment results are evaluated by the grss_dfc_2014 data-set.