Lunar cryptomare records both early-stage mare volcanisms and large-scale impact cratering, which can provide important information about the thermal evolution of the Moon. We built a mixing dielectric constant model to represent the cryptomare deposits mixed by highland debris and mare deposits, and the proper radiative transfer simulation was constructed to evaluate the thermal emission features of surface deposits in the cryptomare region. The microwave radiometer (MRM) data in the Balmer-Kapteyn region were extracted, and the linear interpolation method was used to generate brightness temperature (TB) maps at noon and at night. To enhance the correlation between cryptomare deposits and TB performances, normalized TB (nTB) and TB difference (dTB) maps were also generated. Combined with the datasets, including Lunar Reconnaissance Orbiter Wide Angle Camera, Lunar Orbiter Laser Altimeter, and Diviner and Clementine UV–VIS, the main findings are as follows: (1) The mare-like cryptomare deposits were discovered and identified according to the nTB and dTB performances. Combined with the surface compositions, at least two kinds of buried mare deposits were identified in the B-K region, which erupted during different episodes. (2) A construct-like volcanic feature was suggested by the nTB and dTB performances. (3) The results of our analysis indicated the presence of materials with low dTB anomalies in the northern and southwestern parts of the cryptomare region and in the mare unit within the Vendelinus crater, which illustrates the heterogeneity of the lunar crust in the vertical direction.

Abstract:The principle of multiresolution segmentation was represented in detail in this study, and the canny algorithm was applied for edge-detection of a remotely sensed image based on this principle. The target image was divided into regions based on object-oriented multiresolution segmentation and edge-detection. Furthermore, object hierarchy was created, and a series of features (water bodies, vegetation, roads, residential areas, bare land and other information) were extracted by the spectral and geometrical features. The results indicate that the edge-detection has a positive effect on multiresolution segmentation, and overall accuracy of information extraction reaches to 94.6% by the confusion matrix.