Price: $72.50 – $62.88
(as of Dec 26,2024 23:23:32 UTC – Details)
Publisher : Mdpi AG (January 19, 2021)
Language : English
Hardcover : 256 pages
ISBN-10 : 3039438271
ISBN-13 : 978-3039438273
Item Weight : 1.66 pounds
Dimensions : 6.69 x 0.81 x 9.61 inches
Artificial Neural Networks and Evolutionary Computation are two powerful techniques that have revolutionized the field of remote sensing. By combining these two approaches, researchers are able to develop more accurate and efficient models for analyzing and interpreting remote sensing data.
Artificial Neural Networks (ANNs) are computational models inspired by the way the human brain processes information. These networks are able to learn complex patterns and relationships within data, making them ideal for tasks such as image classification, object detection, and land cover mapping in remote sensing applications.
Evolutionary Computation, on the other hand, is a bio-inspired optimization technique that mimics the process of natural selection to evolve solutions to complex problems. By using evolutionary algorithms, researchers can optimize the parameters of neural networks, such as the number of hidden layers, activation functions, and learning rates, to improve their performance in remote sensing tasks.
By combining these two approaches, researchers are able to create more robust and accurate models for analyzing remote sensing data. For example, ANN models optimized using evolutionary computation have been successfully used for land cover classification, crop monitoring, and disaster management.
Overall, the integration of Artificial Neural Networks and Evolutionary Computation in remote sensing has opened up new possibilities for improving the accuracy and efficiency of data analysis in various applications. As technology continues to advance, we can expect to see even more innovative uses of these techniques in the field of remote sensing.
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