Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA

Residual magnetic error remains after standard levelling process.The weak non-geological effect,manifesting itself as streaky noise along flight lines,creates a challenge for airborne geophysical data processing and interpretation.Microleveling is the process to eliminate this residual noise and is now a standard areogeophysical data processing step.In this paper,we propose a two-step procedure for single aerogeophysical data microleveling:a deep convolutional network is first adopted as approximator to map the original data into a low-level part with nature geological structures and a corrugated residual which still contains high-level detail geological structures;second,the mixture of Gaussian robust principal component analysis(MoG-RPCA)is then used to separate the weak energy fine structures from the residual.The final microleveling result is the addition of low-level structures from deep convolutional network and fine structures from MoG-RPCA.The deep convolutional network does not need dataset for training and the handcrafted network serves as prior(deep image prior)to capture the low-level nature geological structures in the areogeophysical data.Experiments on synthetic data and field data demonstrate that the combination of deep convolutional network and MoG-RPCA is an effective framework for single areogeophysical data microleveling.

Characterizing Shorea robusta communities in the part of Indian Terai landscape

Shorea robusta Gaertn. f.（Sal）is one of the important tim-ber-yielding plants in India, which dominates the vegetation of Terai landscape of Uttar Pradesh state in India forming various communities based on its associations. The present study deals with delineation, map-ping and characterization of various communities of Sal （Shorea robusta） forests in Terai landscape of Uttar Pradesh, India ranging across over 16 districts. Field survey and visual interpretation based forest vegetation type classification and mapping was carried out as part of the project entitled ‘Biodiversity characterization at landscape level using remote sensing and GIS’. Indian Remote Sensing-P6 （Resourcesat-1） Linear Imaging Self Scanner-III satellite data was used during the study. The total area covered by different Sal forests was found to be approximately 2256.77 km2. Sal communities were identified and characterized based on their spectral properties, physiognomy and phytosociological charac-teristics. Following nine Sal communities were identified, delineated and mapped with reasonable accuracyviz.,Chandar,Damar, dry plains, moist plains, western alluvium, western alluvium plains, mixed moist deciduous, mixed dry deciduous andSiwalik. It is evident from the area estimates that mixed moist deciduous Sal is the most dominant commu-nity in the region covering around （1613.90 km2）, other major communi-ties were found as western alluvium plains Sal （362.44 km2）, mixed dry deciduous Sal （362.44 km2） and dry plains Sal （107.71 km2）. The Terai landscape of Uttar Pradesh faces tremendous anthropogenic pressure leading to deterioration of the forests. Community level information could be used monitoring the status as well as for micro level conserva-tion and planning of the Sal forests in Terai Landscape of Uttar Pradesh.