Superpixel-Based Long-Range Dependent Network for High-Resolution Remote-Sensing Image Classification

Liangzhi Li, Ling Han, Qing Miao, Yang Zhang and Ying Jing ()
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Liangzhi Li: College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, China
Ling Han: School of Land Engineering, Chang’an University, Xi’an 710064, China
Qing Miao: Academy of Social Governance, Laboratory of Social Entrepreneurship, Center of Social Welfare and Governance, School of Public Affairs, Zhejiang University, Hangzhou 310058, China
Yang Zhang: School of Economics and Management, Xi’an Aeronautical Institute, Xi’an 710077, China
Ying Jing: Business School, NingboTech University, Ningbo 315100, China

Land, 2022, vol. 11, issue 11, 1-17

Abstract: Data-driven deep neural networks have demonstrated their superiority in high-resolution remote-sensing image (HRSI) classification based on superpixel-based objects. Currently, most HRSI classification methods that combine deep learning and superpixel object segmentation use multiple scales of stacking to satisfy the contextual semantic-information extraction of one analyzed object. However, this approach does not consider the long-distance dependencies between objects, which not only weakens the representation of feature information but also increases computational redundancy. To solve this problem, a superpixel-based long-range dependent network is proposed for HRSI classification. First, a superpixel segmentation algorithm is used to segment HRSI into homogeneous analysis objects as input. Secondly, a multi-channel deep convolutional neural network is proposed for the feature mapping of the analysis objects. Finally, we design a long-range dependent framework based on a long short-term memory (LSTM) network for obtaining contextual relationships and outputting classes of analysis objects. Additionally, we define the semantic range and investigate how it affects classification accuracy. A test is conducted by using two HRSI with overall accuracy (0.79, 0.76) and kappa coefficients ( κ ) (0.92, 0.89). Both qualitative and quantitative comparisons are adopted to test the proposed method’s efficacy. Findings concluded that the proposed method is competitive and consistently superior to the benchmark comparison method.

Keywords: remote-sensing image; deep learning; image classification; long-range dependence; semantic scope (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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