Change Representation and Extraction in Stripes: Rethinking Unsupervised Hyperspectral Image Change Detection With an Untrained Network

Abstract

Deep learning-based hyperspectral image (HSI) change detection (CD) approaches have a strong ability to leverage spectral-spatial-temporal information through automatic feature extraction, and currently dominate in the research field. However, their efficiency and universality are limited by the dependency on labeled data. Although the newly applied untrained networks can avoid the need for labeled data, their feature volatility from the simple difference space easily leads to inaccurate CD results. Inspired by the interesting finding that salient changes appear as bright “stripes” in a new feature space, we propose a novel unsupervised CD method that represents and models changes in stripes for HSIs (named as StripeCD), which integrates optimization modeling into an untrained network. The StripeCD method constructs a new feature space that represents change features in stripes and models them in a novel optimization manner. It consists of three main parts: 1) dual-branch untrained convolutional network, which is utilized to extract deep difference features from bitemporal HSIs and combined with a two-stage channel selection strategy to emphasize the important channels that contribute to CD. 2) multiscale forward-backward segmentation framework, which is proposed for salient change representation. It transforms deep difference features into a new feature space by exploiting the structure information of ground objects and associates salient changes with the stripe-shaped change component. 3) stripe-shaped change extraction model, which characterizes the global sparsity and local discontinuity of salient changes. It explores the intrinsic properties of deep difference features and constructs model-based constraints to better identify changed regions in a controllable manner. The proposed StripeCD method outperformed the state-of-the-art unsupervised CD approaches on three widely used datasets. In addition, the proposed StripeCD method indicates the potential for further investigation of untrained networks in facilitating reliable CD.