For hyperspectral images (HSI) change detection (CD), multi-scale features are usually used to construct the detection models. However, the existing studies only consider the multi-scale features containing changed and unchanged components, which is difficult to represent the subtle changes between bi-temporal HSIs in each scale. To address this problem, we propose a multi-scale diff-changed feature fusion network (MSDFFN) for HSI CD, which improves the ability of feature representation by learning the refined change components between bi-temporal HSIs under different scales. In this network, a temporal feature encoder-decoder sub-network, which combines a reduced inception module and a cross-layer attention module to highlight the significant features, is designed to extract the temporal features of HSIs. A bidirectional diff-changed feature representation module is proposed to learn the fine changed features of bi-temporal HSIs at various scales to enhance the discriminative performance of the subtle change. A multi-scale attention fusion module is developed to adaptively fuse the changed features of various scales. The proposed method can not only discover the subtle change of bi-temporal HSIs but also improve the discriminating power for HSI CD. Experimental results on three HSI datasets show that MSDFFN outperforms a few state-of-the-art methods.

Graph can achieve good performance to extract the low-dimensional features of hyperspectral image (HSI). However, the present graph-based methods just consider the individual information of each sample in a certain characteristic, which is very difficult to represent the intrinsic properties of HSI for the complex imaging condition. To better represent the low-dimensional features of HSI, we propose a multistructure unified discriminative embedding (MUDE) method, which considers the neighborhood, tangential, and statistical properties of each sample in HSI to achieve the complementarity of different characteristics. In MUDE, we design the intraclass and interclass neighborhood structure graphs with the local reconstruction structure of each sample; meanwhile, we also utilize the adaptive tangential affine combination structure to construct the intraclass and interclass tangential structure graphs. To further enhance the discriminating performance between different classes, we consider the influence of the statistical distribution difference for each sample to develop an interclass Gaussian weighted scatter model. Then, an embedding objective function is constructed to enhance the intraclass compactness and the interclass separability and obtain more discriminative features for HSI classification. Experiments on three real HSI datasets show that the proposed method can make full use of the structure information of each sample in different characteristics to achieve the complementarity of different features and improve the classification performance of HSI compared with the state-of-the-art methods.

Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HSI) that possesses many homogenous areas. In this paper, we propose a new dimensionality reduction (DR) method, termed local geometric structure Fisher analysis (LGSFA), for HSI classification. Firstly, LGSFA uses the intraclass neighbor points of each point to compute its reconstruction point. Then, an intrinsic graph and a penalty graph are constructed to reveal the intraclass and interclass properties of hyperspectral data. Finally, the neighbor points and corresponding intraclass reconstruction points are used to enhance the intraclass-manifold compactness and the interclass-manifold separability. LGSFA can effectively reveal the intrinsic manifold structure and obtain the discriminating features of HSI data for classification. Experiments on the Salinas, Indian Pines, and Urban data sets show that the proposed LGSFA algorithm achieves the best classification results than other state-of-the-art methods.

Multi-temporal Hyperspectral images (HSIs) have wide applications in change detection (CD) of different land-covers for their rich spectral features and image details. Traditional supervised learning-based HSI CD algorithms often rely on a substantial number of labeled samples. However, it requires a significant cost in sample annotation. In this paper, we propose a diff-feature contrast enhancement network (DCENet) for semi-supervised HSI CD, which leverages a limited number of labeled samples to guide the training process and a large number of unlabeled samples to improve the confidence of change detection. To achieve this, a differential fusion attention (DFA) sub-network is constructed to extract temporal features from the initial input HSI patches. The dual-branch siamese enhancement module (SEM) is utilized to enhance the generalization of differential features in the feature maps. Herein, multi-scale Kullback-Leibler divergence and feature-enhanced probabilistic contrast loss are designed to constrain the SEM. The proposed method excels at detecting subtle changes in bi-temporal HSIs simultaneously improving the generalization performance of networks. The visual and quantitative experimental results on four HSI datasets show that the proposed DCENet outperforms the compared state-of-the-art methods for HSI CD. Codes: https://github.com/Zhoutya/ChangeDetection-DCENet.

Recently, semi-supervised change detection (SSCD) has attracted considerable attention due to its remarkable capability to enhance model performance with limited annotated data. However, most existing SSCD methods rely primarily on pixel-level consistency learning to leverage unlabeled data. Although this approach can provide basic prediction consistency constraints, it exhibits notable limitations in capturing spatial continuity and global semantic coherence in structural change regions, and is easily affected by noise. To address this issue, we propose a novel SSCD framework, termed the local-global tri-consistency network (LGTC). LGTC incorporates a tri-consistency learning strategy at the pixel, region, and image levels, which provides complementary supervisory signals from fine-grained to global semantic scales, significantly enhancing representation ability and robustness on unlabeled data. Furthermore, a local-global interaction block (LGIBlock) is introduced to integrate local feature details with global context. Within it, the GFTBlock employs frequency domain attention to achieve efficient modeling of global information while effectively suppressing background noise, further enhancing the recognition performance of the model. Experimental results demonstrate that LGTC achieves state-of-the-art performance, attaining F1 scores of 87.12%, 88.65% and 83.25% on the LEVIR-CD, WHU-CD and CDD-CD dataset with 5% labeled data, respectively, fully verifying the effectiveness of the proposed method. Our source codes are available at https://github.com/sherryxu21/LGTC.