Robust multi-sensor image matching based on normalized self-similarity region descriptor

Multi-modal image matching is crucial in aerospace applications because it can fully exploit the complementary and valuable information contained in the amount and diversity of remote sensing images.However,it remains a challenging task due to significant non-linear radiometric,geometric differences,and noise across different sensors.To improve the performance of heterologous image matching,this paper proposes a normalized self-similarity region descriptor to extract consistent structural information.We first construct the pointwise self-similarity region descriptor based on the Euclidean distance between adjacent image blocks to reflect the structural properties of multi-modal images.Then,a linear normalization approach is used to form Modality Independent Region Descriptor(MIRD),which can effectively distinguish structural features such as points,lines,corners,and flat between multi-modal images.To further improve the matching accuracy,the included angle cosine similarity metric is adopted to exploit the directional vector information of multi-dimensional feature descriptors.The experimental results show that the proposed MIRD has better matching accuracy and robustness for various multi-modal image matching than the state-of-the-art methods.MIRD can effectively extract consistent geometric structure features and suppress the influence of SAR speckle noise using non-local neighboring image blocks operation,effectively applied to various multi-modal image matching.

Characterization and evaluation of oil shale based on terahertz spectroscopy: A review

Oil shale is a fine-grained sedimentary rock rich in organic kerogen,which is a national strategic reserve resource and supplementary energy and shale oil and pyrolysis gas can be obtained through carbonization and pyrolysis.Due to the diversity of main components in oil shale and the complexity of the occurrence relationship between organic matter and minerals,the current understanding of the oil generation potential of oil shale and the dy-namic process of organic matter pyrolysis is still unclear,leading to the relatively backward level of its devel-opment technology.The development of new theories and methods for efficient utilization of oil shale resources has great strategic significance.The rotation and vibration frequency of macromolecules in oil shale occur in the terahertz(THz)band,so the THz optical parameters of oil shale can reflect the physical properties of kerogen.In this review,the experimental work progress in THz spectroscopy for characterization and evaluation of oil shale is provided.Oil shale is high anisotropic,resulting that the anisotropy in THz optical parameters strongly depends on the oil yield which is an effective selection for evaluating the oil yield to avoid the problem of environmental pollution in traditional detection.The heterogeneous distributions of organics on the oil shale surface,e.g.,the existence of kerogen-rich and kerogen-poor areas,was evaluated by THz amplitude imaging.In addition,the thermal THz analysis technology was developed to determine the pyrolysis process of kerogen and establish the pyrolysis model of kerogen,and the optimization of oil shale pyrolysis process was realized using THz parameters.Moreover,kerogen in oil shale displays a characteristic absorption peak in the THz range,which provides a new insight into the research of kerogen in the micro and nano scale.The pyrolysis oil shale at high temperature absorbs THz radiation strongly due to the decomposition of pyrite in oil shale.These recent works suggest that THz spectroscopy can realize rapid and non-contact detection of oil shale.At last,we propose the prospects and challenges of the THz technology in future as a new method for optimizing oil shale processing technology and improving the utilization efficiency of oil shale.