| Abstrakt: |
In recent years, deep reinforcement learning (DRL) techniques in image analysis have risen to popularity in a significant way. Using DRL, which has a large range of capabilities spanning from RL to deep learning DL, it is possible to handle difficult remote sensing photo classification problems. A key characteristic of DRL is its scalability, which allows it to be used to problems with huge dimensions, noisy and nonlinear patterns of picture data, and other challenging situations. First, we provide an introduction of deep learning, reinforcement learning, and deep reinforcement learning techniques in a range of image processing applications. We then go into more detail about the current state of the art. It was also evaluated to see whether the architecture of the DRL-based CNN-DeepQ model exhibited any of the characteristics described above. When faced with real-world object identification challenges such as risk variables and rising uncertainty, the experimental data reveal that DRL-based CNN-DeepQ models can outperform and outperform traditional algorithms by a wide margin. [ABSTRACT FROM AUTHOR] |
