Application of remote sensing image classification algorithm in monitoring changes in marine ecosystems

The remote sensing image classification algorithm plays a core role in monitoring marine ecosystems and is a key means for us to gain insight into changes in the marine environment. However, ocean remote sensing images often contain noise that is difficult to remove due to complex environments and technological limitations, which poses a serious challenge to image quality and subsequent scene classification. Noise not only weakens image clarity, but may also lead to poor denoising effects, insufficient enhancement of image features, and decreased classification accuracy. In view of this, this article innovatively proposes a remote sensing image classification method based on deep learning (DL). This method relies on the excellent ability of DL in image processing to enhance the denoising performance of remote sensing images, highlight image features, and thus improve classification accuracy. Through a carefully designed neural network architecture, this algorithm can autonomously learn and capture the core features of images, while effectively resisting noise interference, ensuring more accurate classification results. Experimental data comparison shows that compared with traditional techniques, the DL classification algorithm proposed in this paper has achieved significant improvements in multiple aspects.