Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. One notable model is Visual ChatGPT, which combines ChatGPT's LLM capabilities with visual computation to enable effective image analysis. The model's ability to process images based on textual inputs can revolutionize diverse fields. However, its application in the remote sensing domain remains unexplored. This is the first paper to examine the potential of Visual ChatGPT, a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of image processing related to the remote sensing domain. Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation. These offer valuable insights into image content and facilitate the interpretation and extraction of information. By exploring the applicability of these techniques within publicly available datasets of satellite images, we demonstrate the current model's limitations in dealing with remote sensing images, highlighting its challenges and future prospects. Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing, creating accessible and practical application opportunities in the field.

翻译：自然语言处理（NLP）的最新进展，特别是基于深度学习计算机视觉技术的大语言模型（LLMs），已展现出自动化多种任务的巨大潜力。其中，Visual ChatGPT作为一款结合ChatGPT大语言模型能力与视觉计算功能的代表性模型，能够实现高效的图像分析。该模型基于文本输入处理图像的能力有望革新多个领域，但其在遥感领域的应用仍未被探索。本文首次探讨基于GPT架构的尖端大语言模型Visual ChatGPT在解决遥感领域图像处理问题中的潜力。在其现有功能中，Visual ChatGPT可生成图像的文本描述、执行Canny边缘与直线检测、以及进行图像分割。这些功能为理解图像内容提供了宝贵视角，并有助于信息解译与提取。通过探索这些技术在公开卫星图像数据集中的适用性，我们揭示了当前模型在遥感图像处理中的局限性，指出了其面临的挑战与未来前景。尽管尚处于早期发展阶段，我们相信大语言模型与视觉模型的结合在变革遥感图像处理领域具有重要潜力，将为该领域创造易于获取且实用的应用机会。