The satellite resembles a small rectangular cube, with length dimensions of 10 cm x 10 cm x 13.5 cm, a weight of almost 950 g. The satellite was launched in 625 km Sun-synchronous orbit. The satellite will perform the function of a remote sensing satellite and take images of Earth's surface with a resolution of 90 meter, the best achieved by any "PICO" category satellite in the world.
Remote Sensing Book By Meenakshi
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Irma Britton has a long career in academic publishing, and she is Member of the Academic Network of United Nations Global Geospatial Information Management (UN-GGIM). She acquires content in cartography, photogrammetry, remote sensing, surveying, GIS, and geospatial analysis applied and scattered between Civil and Geomatics Engineering, Geoscience, Geography, Earth Science, Computer Science, Environmental Sciences and Engineering. She acquires also in core areas of environment and sustainability from a science and engineering perspective, particularly in air quality, water management, environmental management and policy, climate change, smart cities, and energy. Irma is very interested to help new talents publishing their work and to expand her program with new books and book series in applied and emerging topics. Contact Irma to discuss your publishing project.
Geospatial technologyGeospatial technology refers to all the technology required for the collecting, storing and organizing of geographic information. It includes the satellite technology which allowed for the geographic mapping and analysis of Earth. Geospatial technology can be found in several related technologies, such as Geographic Information Systems (GIS), Global Positioning Systems (GPS), geofencing and remote sensing.
In remote sensing (RS) community, RSIR (Remote Sensing Image Retrieval) is considered as a tough topic and gained more attention because the data is collected via EO (Earth Observation) satellites. As huge numbers of RS images are available, the lack of labelled samples, complex contents obstructs the understanding of RS images. Therefore, accurate and effective image retrieval (IR) system named fusion based feature extraction and meta-heuristic algorithm based feature selection is presented in this work for performing RSIR. Pre-processing is done using Kernel PCA (KPCA). Next, fusion of 3 CNN (Fused CNN) architectures namely Visual Geometry Group (VGG 16, VGG 19) and ResNet (Residual Network) is used for feature extraction. The selection of features is performed using Joint MI (Joint Mutual Information) optimized using RFO (Rain-Fall Optimization) algorithm. Next, similarity is measured using Weighted Euclidean Distance (WED) metric. Finally, Relevance Feedback Model (RFM) verifies whether the search results have met the user query. The implementation tool is PYTHON and the three online databases used for testing are WHU-RS19, AID, and UCM. Hence, the simulation outcomes reveal that the presented Fused CNN model achieved improved mAP performances such as 93.693%, 94.716%, and 95.067% on the datasets than the baseline architectures. 2ff7e9595c
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