A Cross-Hierarchical Multi-Feature Fusion Network Based on Multiscale Encoder-Decoder for Hyperspectral Change Detection

• URL: http://arxiv.org/abs/2509.16988v1
• Date: Sun, 21 Sep 2025 09:04:28 GMT
• Title: A Cross-Hierarchical Multi-Feature Fusion Network Based on Multiscale Encoder-Decoder for Hyperspectral Change Detection
• Authors: Mingshuai Sheng, Bhatti Uzair Aslam, Junfeng Zhang, Siling Feng, Yonis Gulzar,
• Abstract summary: This paper proposes a cross-hierarchical multi-feature fusion network (CHMFFN) based on a multiscale encoder-decoder architecture.<n> Experiments on four public hyperspectral datasets show CHMFFN outperforms state-of-the-art methods, verifying its effectiveness.
• Score: 3.5421087596321352
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral change detection (HCD) aims to accurately identify land-cover changes in hyperspectral images of the same area acquired at different times, with key applications in environmental monitoring and disaster assessment. To address limitations of existing methods, such as insufficient use of multiscale features and low efficiency in differential feature fusion, this paper proposes a cross-hierarchical multi-feature fusion network (CHMFFN) based on a multiscale encoder-decoder architecture. The front-end adopts a multiscale feature extraction subnetwork, built on an encoder-decoder backbone with residual connections and a dual-core channel-spatial attention (DCCSA) module to extract spectral-spatial-temporal features (SSTF). The encoder captures multiscale features from shallow details to deep semantics via residual blocks and convolutional kernels with varying receptive fields. The decoder restores spatial resolution and suppresses noise information through skip connections integrating encoder features. Additionally, a spectral-temporal change feature learning (STCFL) module learns cross-temporal change features at different levels, strengthening inter-temporal difference capture. An adaptive fusion of advanced features (AFAF) module dynamically balances hierarchical differential features via adaptive weights, enhancing representation of complex changes. Experiments on four public hyperspectral datasets show CHMFFN outperforms state-of-the-art methods, verifying its effectiveness.

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