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，尤其是大型语言模型（LLMs）与基于深度学习的计算机视觉技术的结合，已展现出自动化多种任务的巨大潜力。其中，视觉ChatGPT（Visual ChatGPT）是一个融合了ChatGPT的LLM能力与视觉计算功能的模型，能够实现高效的图像分析。该模型根据文本输入处理图像的能力有望革新多个领域。然而，其在遥感领域的应用尚未被探索。本文首次探讨基于GPT架构的尖端LLM——视觉ChatGPT——在处理遥感相关图像处理任务中的潜力。在其现有功能中，视觉ChatGPT可生成图像的文本描述、执行Canny边缘检测与直线检测，并进行图像分割。这些功能为图像内容提供了有价值的洞察，促进了信息的解释与提取。通过在地球观测卫星图像公开数据集中测试这些技术的适用性，我们揭示了当前模型在处理遥感图像时的局限性，并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，但我们认为，LLM与视觉模型的结合具有变革遥感图像处理的巨大潜力，为该领域创造了易用且实用的应用机遇。