scholarly journals Region Driven Remote Sensing Image Captioning

2019 ◽  
Vol 165 ◽  
pp. 32-40
Author(s):  
S Chandeesh Kumar ◽  
M Hemalatha ◽  
S Badri Narayan ◽  
P Nandhini
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Captioning

scholarly journals VAA: Visual Aligning Attention Model for Remote Sensing Image Captioning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 137355-137364 ◽  
Author(s):  
Zhengyuan Zhang ◽  
Wenkai Zhang ◽  
Wenhui Diao ◽  
Menglong Yan ◽  
Xin Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Captioning ◽  
Attention Model

scholarly journals LAM: Remote Sensing Image Captioning with Label-Attention Mechanism

2019 ◽  
Vol 11 (20) ◽  
pp. 2349 ◽  
Author(s):  
Zhengyuan Zhang ◽  
Wenhui Diao ◽  
Wenkai Zhang ◽  
Menglong Yan ◽  
Xin Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Image Features ◽  
Attention Mechanism ◽  
Word Embedding ◽  
Remote Sensing Images ◽  
Image Captioning ◽  
Scoring Method ◽  
Label Information

Significant progress has been made in remote sensing image captioning by encoder-decoder frameworks. The conventional attention mechanism is prevalent in this task but still has some drawbacks. The conventional attention mechanism only uses visual information about the remote sensing images without considering using the label information to guide the calculation of attention masks. To this end, a novel attention mechanism, namely Label-Attention Mechanism (LAM), is proposed in this paper. LAM additionally utilizes the label information of high-resolution remote sensing images to generate natural sentences to describe the given images. It is worth noting that, instead of high-level image features, the predicted categories’ word embedding vectors are adopted to guide the calculation of attention masks. Representing the content of images in the form of word embedding vectors can filter out redundant image features. In addition, it can also preserve pure and useful information for generating complete sentences. The experimental results from UCM-Captions, Sydney-Captions and RSICD demonstrate that LAM can improve the model’s performance for describing high-resolution remote sensing images and obtain better S m scores compared with other methods. S m score is a hybrid scoring method derived from the AI Challenge 2017 scoring method. In addition, the validity of LAM is verified by the experiment of using true labels.

scholarly journals Boosting Memory with a Persistent Memory Mechanism for Remote Sensing Image Captioning

2020 ◽  
Vol 12 (11) ◽  
pp. 1874
Author(s):  
Kun Fu ◽  
Yang Li ◽  
Wenkai Zhang ◽  
Hongfeng Yu ◽  
Xian Sun
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
External Memory ◽  
Image Captioning ◽  
Time Step ◽  
Current Time ◽  
Input Information ◽  
Memory Mechanism ◽  
Persistent Memory

The encoder–decoder framework has been widely used in the remote sensing image captioning task. When we need to extract remote sensing images containing specific characteristics from the described sentences for research, rich sentences can improve the final extraction results. However, the Long Short-Term Memory (LSTM) network used in decoders still loses some information in the picture over time when the generated caption is long. In this paper, we present a new model component named the Persistent Memory Mechanism (PMM), which can expand the information storage capacity of LSTM with an external memory. The external memory is a memory matrix with a predetermined size. It can store all the hidden layer vectors of LSTM before the current time step. Thus, our method can effectively solve the above problem. At each time step, the PMM searches previous information related to the input information at the current time from the external memory. Then the PMM will process the captured long-term information and predict the next word with the current information. In addition, it updates its memory with the input information. This method can pick up the long-term information missed from the LSTM but useful to the caption generation. By applying this method to image captioning, our CIDEr scores on datasets UCM-Captions, Sydney-Captions, and RSICD increased by 3%, 5%, and 7%, respectively.

Global Visual Feature and Linguistic State Guided Attention for Remote Sensing Image Captioning

2021 ◽  
pp. 1-1
Author(s):  
Zhengyuan Zhang ◽  
Wenkai Zhang ◽  
Menglong Yan ◽  
Xin Gao ◽  
Kun Fu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Visual Feature ◽  
Image Captioning

Multi-View Attention Network for Remote Sensing Image Captioning

2021 ◽  
Author(s):  
Yun Meng ◽  
Yu Gu ◽  
Xiutiao Ye ◽  
Jingxian Tian ◽  
Shuang Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Captioning ◽  
Attention Network

Remote sensing image captioning via Variational Autoencoder and Reinforcement Learning

2020 ◽  
Vol 203 ◽  
pp. 105920 ◽  
Author(s):  
Xiangqing Shen ◽  
Bing Liu ◽  
Yong Zhou ◽  
Jiaqi Zhao ◽  
Mingming Liu
Keyword(s):  
Remote Sensing ◽  
Reinforcement Learning ◽  
Remote Sensing Image ◽  
Image Captioning ◽  
Variational Autoencoder
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