Hyperspectral image change detection (HSI-CD) aims to identify the
differences in bitemporal HSIs. To mitigate spectral redundancy and improve the
discriminativeness of changing features, some methods introduced band selection
technology to select bands conducive for CD. However, these methods are limited
by the inability to end-to-end training with the deep learning-based feature
extractor and lack considering the complex nonlinear relationship among bands.
In this paper, we propose an end-to-end efficient spectral-spatial change
detection network (ES2Net) to address these issues. Specifically, we devised a
learnable band selection module to automatically select bands conducive to CD.
It can be jointly optimized with a feature extraction network and capture the
complex nonlinear relationships among bands. Moreover, considering the large
spatial feature distribution differences among different bands, we design the
cluster-wise spatial attention mechanism that assigns a spatial attention
factor to each individual band to individually improve the feature
discriminativeness for each band. Experiments on three widely used HSI-CD
datasets demonstrate the effectiveness and superiority of this method compared
with other state-of-the-art methods.

为了解决选择频段困难和频段之间的复杂非线性关系的问题，本文提出了一种端到端的高效光谱空间变化检测网络（ES2Net），通过引入可学习的频段选择模块和聚类式空间注意机制，能够自动选择适合变化检测的频段并优化特征提取网络，从而提高光谱变化检测的效果。实验证明，该方法相较于其他最先进的方法具有更高的效能和优越性。

ES2Net：高效的光谱 - 空间网络用于高光谱图像变化检测

ES2Net: An Efficient Spectral-Spatial Network for Hyperspectral Image  Change Detection

Hyperspectral image change detection (HSI-CD) has emerged as a crucial
research area in remote sensing due to its ability to detect subtle changes on
the earth's surface. Recently, diffusional denoising probabilistic models
(DDPM) have demonstrated remarkable performance in the generative domain. Apart
from their image generation capability, the denoising process in diffusion
models can comprehensively account for the semantic correlation of
spectral-spatial features in HSI, resulting in the retrieval of semantically
relevant features in the original image. In this work, we extend the diffusion
model's application to the HSI-CD field and propose a novel unsupervised HSI-CD
with semantic correlation diffusion model (DiffUCD). Specifically, the semantic
correlation diffusion model (SCDM) leverages abundant unlabeled samples and
fully accounts for the semantic correlation of spectral-spatial features, which
mitigates pseudo change between multi-temporal images arising from inconsistent
imaging conditions. Besides, objects with the same semantic concept at the same
spatial location may exhibit inconsistent spectral signatures at different
times, resulting in pseudo change. To address this problem, we propose a
cross-temporal contrastive learning (CTCL) mechanism that aligns the spectral
feature representations of unchanged samples. By doing so, the spectral
difference invariant features caused by environmental changes can be obtained.
Experiments conducted on three publicly available datasets demonstrate that the
proposed method outperforms the other state-of-the-art unsupervised methods in
terms of Overall Accuracy (OA), Kappa Coefficient (KC), and F1 scores,
achieving improvements of approximately 3.95%, 8.13%, and 4.45%, respectively.
Notably, our method can achieve comparable results to those fully supervised
methods requiring numerous annotated samples.

本文介绍了一种基于扩散模型的远程感知高光谱图像变化检测方法，结合语义相关性扩散模型和交叉时序对比学习机制，利用丰富的未标定样本获得不变的光谱差异特征在三个数据集上的实验表明，该方法在准确性、kappa 系数和 F1 值等方面都优于最先进的无监督方法，同时可以跟需要大量注释样本的全监督方法达成可比较的结果。