A deep relearning method based on the recurrent neural network for land cover classification. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Tang, Yunwei; Qiu, Fang; Wang, Bangjin; Wu, Di; Jing, Linhai; Sun, Zhongchang
• Is Part Of: GIScience & remote sensing. Volume 59:Issue 1(2022)
• Page Start: 1344
• Page End: 1366
• Publication Date: 2022-12-31
• Subjects: Recurrent neural networks (RNN) -- relearning -- gated recurrent unit (GRU) -- long short-term memory (LSTM) -- land cover classification; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2022.2115589
• Languages: English
• ISSNs: 1548-1603

Abstract

ABSTRACT: Recent developments in deep learning (DL) techniques have provided a series of new methods for land cover classification. However, most DL-based methods do not consider the rich spatial association of land cover classes embedded in remote sensing images. In this research, a deep relearning method based on the recurrent neural network (DRRNN) is proposed for land cover classification. The relearning approach has great potential to improve classification, which has never been used in DL-based land cover classification. To utilize the spatial association of the pixels' information classes, a class correlated feature (CCF) is first extracted in a local window from an initial classification result. This feature can reflect both the spatial autocorrelation and spatial arrangement of land cover classes. Since the recurrent neural network (RNN) is designed to process sequential data, the CCF is formed as a feature sequence, allowing RNN to model the dependency between class labels. The relearning process is then applied to iteratively classify remote sensing images based on the CCF and spectral-spatial feature. At each relearning iteration, the CCF is learned from the previous classification result until a stopping condition is satisfied. This method was tested on five remote sensing images with different sensors and diverse environments. It was observed that noise in the classification result can be filtered by considering spatial autocorrelation, and misclassified areas can be corrected by incorporating spatial arrangement in the relearning process. The classification results indicate that compared to other state-of-the-art DL methods, the proposed method consistently achieves the highest accuracy.