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With the warming up and continuous development of machine learning, especially deep learning, the research on visual question answering field has made significant progress, with important theoretical research significance and practical application value. Therefore, it is necessary to summarize the current research and provide some reference for researchers in this field. This article conducted a detailed and in-depth analysis and summarized of relevant research and typical methods of visual question answering field. First, relevant background knowledge about VQA(Visual Question Answering) was introduced. Secondly, the issues and challenges of visual question answering were discussed, and at the same time, some promising discussion on the particular methodologies was given. Thirdly, the key sub-problems affecting visual question answering were summarized and analyzed. Then, the current commonly used data sets and evaluation indicators were summarized. Next, in view of the popular algorithms and models in VQA research, comparison of the algorithms and models was summarized and listed. Finally, the future development trend and conclusion of visual question answering were prospected.

Aiming at color remote sensing image with complexity, fuzziness and strong noise, an edge detection algorithm based on blocky fuzzy enhancement and multi-direction fuzzy mathematical morphology gradient is putted forward. It enhances the image by blocks in fuzzy domain, then employs fuzzy mathematical morphology gradient operation with multiple structure elements of different directions on the image to extract the edge, and it can not only detect real edge with directionality but also restrain non directional noise effectively. Experiments show the algorithm is effectual to detect the edge of color remote sensing image.