Price: $101.76 – $84.84
(as of Dec 27,2024 10:39:07 UTC – Details)
Publisher : Mdpi AG (October 27, 2022)
Language : English
Hardcover : 344 pages
ISBN-10 : 3036546294
ISBN-13 : 978-3036546292
Item Weight : 2.07 pounds
Dimensions : 6.69 x 1.06 x 9.61 inches
Deep learning methods have revolutionized the field of remote sensing by enabling more accurate and efficient analysis of satellite imagery. With the ability to automatically extract features and patterns from large amounts of complex data, deep learning algorithms have proven to be highly effective in tasks such as image classification, object detection, and semantic segmentation.
One of the key advantages of deep learning methods in remote sensing is their ability to learn hierarchical representations of the data, which allows them to capture subtle and intricate patterns that may be difficult for traditional machine learning algorithms to discern. This has led to significant improvements in the accuracy and reliability of remote sensing applications, such as land cover classification, vegetation monitoring, and disaster detection.
Some of the most commonly used deep learning architectures in remote sensing include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). CNNs, in particular, have been widely adopted for tasks such as image classification and object detection due to their ability to automatically learn spatial hierarchies of features from raw pixel data.
In addition to traditional deep learning methods, researchers are also exploring the use of novel techniques such as transfer learning, which involves pre-training a deep learning model on a large dataset and then fine-tuning it on a smaller, domain-specific dataset. This approach can help improve the performance of deep learning models in remote sensing applications, especially when labeled training data is limited.
Overall, deep learning methods hold great promise for advancing the field of remote sensing and unlocking new insights from satellite imagery. By leveraging the power of deep neural networks, researchers and practitioners can enhance the accuracy, efficiency, and scalability of remote sensing applications, ultimately leading to more informed decision-making and sustainable resource management.
#Deep #Learning #Methods #Remote #Sensing
