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This paper proposes an endmember matrix constraint unmixing method for ZY-1 02D hyperspectral imagery (HSI) super-resolution reconstruction (SRR) to overcome the low resolution of ZY-1 02D HSI. The proposed method combines spectral unmixing and adds novel smoothing constraints to traditional non-negative matrix factorization to improve details and preserve the spectral information of traditional SRR methods. The full utilization of the endmember spectral matrix and endmember abundance matrix of HSI and multispectral imagery (MSI) reconstructs the high spatial resolution and high spectral fidelity HSI. Furthermore, given the ZY-1 02D HSI infrared bands are seriously corrupted by noise, the influence of denoising on the SRR accuracy is also discussed. Experiments show that the proposed method restores spatial details and spectral information and is robust for noise, preserving more spectral information. Therefore, the proposed method is a ZY-1 02D HSI SRR method with high spatial resolution and high spectral fidelity, which improves the spatial resolution while simultaneously solving spectral mixing and provides the possibility for the data further expansion.

Abstract. Fire serves as a successional initiation in jack pine (Pinus banksiana) forests of North America, as jack pine reproduce using seratonous cones that open only in intense heat. Jack pine seedling resilience after fire is characterized by high numbers of mortality. The estimation of sapling survivability and density is useful for understanding dynamics of carbon sequestration, forest structure and dynamic, and supporting management of the landscape. Most studies concerning the interaction of forest disturbances occurs at moderate spatial resolution. These moderate resolution data analyses do not adequately capture the fine scale spatial variation of the landscape after fire for understanding sapling survival. Thus, high-resolution data, such as aerial photography may provide more detailed information to support decision-making. A key to the types of spatial patterns that emerge in these early years is the pre-fire stand condition. In heavily managed areas, the mosaic of forest patches may include extensive variety in disturbance conditions. In this current research we address the problem of scale in relation to understanding the influence of pre-fire condition on post-fire early recovery patterns. To do this, we combine data output from the LandTrendr algorithm in Google Earth Engine with spectral data from aerial photography collected by airplane and Unmanned Aerial System to perform a random forest classification. The result is a finer scale resolution map of forest conditions of varying sapling density.

Human experts are integral to the success of computational earth observation. They perform various visual decision-making tasks, from selecting data and training machine-learning algorithms to interpreting accuracy and credibility. Research concerning the various human factors which affect performance has a long history within the fields of earth observation and the military. Shifts in the analytical environment from analog to digital workspaces necessitate continued research, focusing on human-in-the-loop processing. This article reviews the history of human-factors research within the field of remote sensing and suggests a framework for refocusing the discipline's efforts to understand the role that humans play in earth observation.