Object-based change detection for VHR remote sensing images based on a Trisiamese-LSTM. Issue 16 (17th August 2020)

• Record Type: Journal Article
• Authors: Jing, Ran; Liu, Shuang; Gong, Zhaoning; Wang, Zhiheng; Guan, Hongliang; Gautam, Atul; Zhao, Wenji
• Is Part Of: International journal of remote sensing. Volume 41:Issue 16(2020)
• Page Start: 6209
• Page End: 6231
• Publication Date: 2020-08-17
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2020.1734253
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Change detection has been a research hotspot in remote sensing fields for decades. However, the increasing use of very high-resolution (VHR) remote sensing images have introduced more difficulties in change detection because of the complex details these images contain. In this paper, we propose a novel deep learning architecture for change detection composed of a Trisiamese subnetwork and a long short-term memory (LSTM) subnetwork that fully utilizes the spatial, spectral and multiphase information and improves the change detection capabilities for VHR remote sensing images. Multiscale simple linear iterative clustering (SLIC)-based image segmentation is first performed on multitemporal images at different image scales to obtain edge information-based objects. A Trisiamese subnetwork with six inputs can extract abundant spectral-spatial feature representations; the LSTM subnetwork then uses the extracted image features to effectively analyse the multiphase information in bitemporal images. The proposed method has the following advantages: 1) it can fully utilize the significant spatial information to improve the detection task; 2) it combines the advantages of convolutional architectures for image feature representation and recurrent neural network (RNN) architectures for sequential data representation, unlike most of the algorithms that use either method or that merely use image differencing or stacking operations. The controlled experiments reveal that the multiphase information extracted by the LSTM subnetwork is important to improve the accuracy of the change detection results. The influence of the Trisiamese subnetwork on change detection is even more significant than that of the LSTM subnetwork. Comparisons with other state-of-the-art change detection methods indicate that in areas with clear surface features and limited interference, the proposed method obtains more competitive results compared to state-of-the-art methods, and in regions where the changed objects occur in complex patterns, the proposed method exhibited an ideal performance.