GTMSiam: Gated Transmitting-Based Multiscale Siamese Network for Hyperspectral Image Change Detection

Abstract

Hyperspectral image change detection (HSI-CD) is a technique that detects changes in land cover occurring in a specific area within a closed time. At present, most existing methods for HSI-CD employ exceedingly intricate network architectures, leading to a high model complexity that hampers the achievement of a favorable trade-off between change detection accuracy and timeliness. Furthermore, existing methods often confine the feature extraction process to a single scale rather than multiple diverse scales. However, employing a multiscale approach for feature extraction allows for capturing finer-grained features encompassing more intricate details, as well as coarser-grained features that aggregate local information over a larger range. On the other hand, most existing methods overemphasize the complexity of the feature extraction process and underestimate the importance of the conversion process from bi-temporal features to valuable change features. To this end, a gated transmitting based multiscale siamese network (GTMSiam) is proposed, which mainly contains the following two portions: 1) dual branches with the siamese structure, which capture spatial features of the HSIs at multiple scales while preserving rich spectral information. Moreover, the siamese design effectively reduces the network parameters, thereby alleviating the computational complexity of the model. 2) gated change information transmitting module (GTM), which utilizes gated neural units to transform bi-temporal image features into land cover change information, while progressively transmitting change information at different scales. This enables the network to leverage diverse scale change information for comprehensive discrimination of land object changes. Experimental results on three publicly available datasets demonstrate the superior performance of the proposed GTMSiam. Simultaneously, the complexity analysis experiment proves that the GTMSiam can give consideration to both detection performance and timeliness. The source code of this letter will be released at https://github.com/zkylnnu/GTMSiam.