Deep semantic understanding of high resolution remote sensing image

By Bo Qu, Xuelong Li, Dacheng Tao, Xiaoqiang Lu. International Conference on Computer, Information and Telecommunication Systems, 2016

351 citations recorded by the source corpus.

With the rapid development of remote sensing technology, huge quantities of high resolution remote sensing images are available now. Understanding these images in semantic level is of great significance. Hence, a deep multimodal neural network model for semantic understanding of the high resolution remote sensing images is proposed in this paper, which uses both visual and textual information of the high resolution remote sensing images to generate natural sentences describing the given images. In the proposed model, the convolution neural network is utilized to extract the image feature, which is then combined with the text descriptions of the images by RNN or LSTMs. And in the experiments, two new remote sensing image-captions datasets are built at first. Then different kinds of CNNs with RNN or LSTMs are combined to find which is the best combination for caption generation. The experiments results prove that the proposed method achieves good performances in semantic understanding of high resolution remote sensing images.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/80e65772132b356358f192470ae1e36d2ed6a965

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xuelong Li
2. Tong Zhang
3. Wei Zhang
4. Zhuang Yin
5. Xuerui Mao
6. Miaoxin Cai
7. Xiaoqiang Lu
8. Xiangtao Zheng

Citations in the corpus

• Exploring Models and Data for Remote Sensing Image Caption Generation (2017, methodology): “Qu et al. [26] firstly proposed a deep multimodal neural network model to generate sentences for the high resolution remote sensing images.”
• RSGPT: A Remote Sensing Vision Language Model and Benchmark (2023, methodology): “Qualitative comparison among UCM-Captions [44], Sydney-Captions [44], RSICD [36], NWPU-Captions [10], RS5M [79] and RSICap (ours).”
• EarthGPT: A Universal Multimodal Large Language Model for Multisensor Image Comprehension in Remote Sensing Domain (2024, methodology): “These images were extracted from the Tiangong-2 Authorized licensed use limited to the terms of the applicable license agreement with IEEE.”