IMAGE OF THE DAY
Pleiades Neo Satellite Image (30cm)
Pleiades Neo satellite (30cm) constellation consisting of four identical satellites was successfully launched on April 28, 2021 from the Guiana Space Center, view launch. The Pleiades Neo satellite will provide customers with 30cm panchromatic and 1.2m multi-spectral satellite imagery with six bands and will offer mono, stereo and tri-stereo images for various mapping applications. This Color Infra Red (CIR) sample Pleiades Neo satellite image is used for vegetation analysis. View sample Pleiades Neo satellite imagery gallery.
With over four decades of experience, we use our unique understanding of the oil industry to help you assess risk and reduce expenses. By utilizing old and current multispectral satellite image data, spectral analysis and sub-pixel classification, old well locations can be recovered and coordinates adjusted to improve the geophysical and geological interpretation "before" additional wells are drilled in the same area. This increases the success ration considerable, especially when some of the old well locations have coordinate problems in excess of 1 km. Specialty services include recovery and coordinate adjustments of old wells, as well as seismic surveys in heavy jungle and desert terrain.
Multispectral imaging (VNIR), WorldView-3 Short Wave InfraRed (SWIR) and thematic mineral mapping allows researchers to collect reflectance data and absorption properties of soils, rock, and vegetation. By leveraging wavelengths and spectral reflectance data, spectral signatures, invisible to the human eye, can be established for mineral traces located on the surface. Our satellite imaging technology can yield unrivaled insight as to the composition of earth surfaces, greatly aiding geologists, scientists and researchers. This data could be utilized by trained photo geologists to interpret surface lithology's, identify clays, oxides, soil types and identify potential locations of minerals, from high and medium resolution satellite imagery.
Satellite Imaging Corporation (SIC) has developed comprehensive policy and procedures to include QA and QC in the planning stage of every project involving the use of satellite, aerial and UAV remote sensing data for GIS mapping. Using our extensive array of advanced satellite sensors to acquire new imagery, or use customer provided UAV imagery, we can provide you with unparalleled quality and geospatial accuracy to support your 2D or 3D GIS map applications such as precision agriculture mapping, land-cover classifications, change-detection from detailed VNIR, SWIR satellite imagery, Hyperspectral analysis and development/ utilization of deep learning techniques such as (semi) automated object-based image analysis (OBIA) and convolutional neural network (CNN) algorithms.
Mitigate business risks, accelerate pipeline planning, learn about surface composition, and predict environmental impact using the solutions we have tailored to enhance mining and energy projects.
From construction site selection and evaluation to the assessment of existing structures, we have solutions designed to help facilitate every step of your project.
Defense agencies, military contractors, and law enforcement are continually faced with new challenges. We provide an unrivaled advantage when planning strategic and tactical operations, carrying out combat missions, and developing simulations.
Leverage our geographic information system (GIS) technology solutions to map data in a visually concise, easily consumable manner.
When timeliness counts, rely on our satellite image data — collected before and after a natural or manmade disaster — to provide crucial insight for disaster response efforts and insurance operations.
The world is always in motion. Use our proven methods to keep an eye on the environment, enhance coastline management efforts, assess forests, and facilitate the development of agricultural resources. Utilizing our cost effective Satellite and UAV remote sensing services, including artificial neural network processing, can improve the quality of results obtained, at a lower cost than using standard remote sensing algorithms.