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Multi-Label Remote Sensing Image Classification with Latent Semantic Dependencies

College of Information and Computer Engineering, Northeast Forestry University, Harbin 150036, China
Key Laboratory of Forestry Data Science and Cloud Computing of State Forestry Adiminstration, Harbin 150036, China
Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Author to whom correspondence should be addressed.
Received: 11 February 2020 / Revised: 21 March 2020 / Accepted: 25 March 2020 / Published: 31 March 2020
(This article belongs to the Special Issue Computer Vision and Machine Learning Application on Earth Observation)
Deforestation in the Amazon rainforest results in reduced biodiversity, habitat loss, climate change, and other destructive impacts. Hence obtaining location information on human activities is essential for scientists and governments working to protect the Amazon rainforest. We propose a novel remote sensing image classification framework that provides us with the key data needed to more effectively manage deforestation and its consequences. We introduce the attention module to separate the features which are extracted from CNN(Convolutional Neural Network) by channel, then further send the separated features to the LSTM(Long-Short Term Memory) network to predict labels sequentially. Moreover, we propose a loss function by calculating the co-occurrence matrix of all labels in the dataset and assigning different weights to each label. Experimental results on the satellite image dataset of the Amazon rainforest show that our model obtains a better F 2 score compared to other methods, which indicates that our model is effective in utilizing label dependencies to improve the performance of multi-label image classification. View Full-Text
Keywords: multi-label; remote-sensing image; CNN-RNN; attention; dependencies multi-label; remote-sensing image; CNN-RNN; attention; dependencies
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MDPI and ACS Style

Ji, J.; Jing, W.; Chen, G.; Lin, J.; Song, H. Multi-Label Remote Sensing Image Classification with Latent Semantic Dependencies. Remote Sens. 2020, 12, 1110.

AMA Style

Ji J, Jing W, Chen G, Lin J, Song H. Multi-Label Remote Sensing Image Classification with Latent Semantic Dependencies. Remote Sensing. 2020; 12(7):1110.

Chicago/Turabian Style

Ji, Junchao, Weipeng Jing, Guangsheng Chen, Jingbo Lin, and Houbing Song. 2020. "Multi-Label Remote Sensing Image Classification with Latent Semantic Dependencies" Remote Sensing 12, no. 7: 1110.

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