Efficient and reliable co-seismic landslide mapping is essential for emergency response, facilitating rescue, and post-disaster reconstruction after an earthquake. Many earthquake-prone areas globally are vegetated, making it imperative to conduct focused research on co-seismic landslides in these areas. Remote sensing images offer significant advantages for obtaining co-seismic landslide maps due to their large area coverage and short revisit time. However, many existing methods can hardly meet the accurate, robust, and efficient needs of co-seismic landslide mapping, as they encounter difficulties in differentiating co-seismic landslides from historical landslides and face complications due to other seismic-induced changes. To address these challenges, an automatic change detection-based method for fine co-seismic landslide mapping in vegetated areas was proposed. This method enabled automated landslide mapping in areas with vegetation through change detection and ensemble learning strategy incorporating logical information and spectral features of tri-temporal remote sensing images. Three experiments were carried out through PlanetScope images covering typical areas in China and Japan. The results showed that the proposed method outperformed comparative methods, including iterated principal component analysis (ITPCA), spectral angle mapper (SAM), and deep change vector analysis (DCVA). The overall accuracy (OA) of the proposed approach exceeded 94%, and both the Kappa coefficient and F1-score surpassed 0.87. These findings confirmed the superiority of the proposed method and demonstrated its promising prospects in emergency response in the future.

Remote sensing image change detection (CD) is crucial for disaster assessment, land use change, and urban management. Most CD methods are realized by CNN and Transformer. However, these methods are not satisfied with modeling global dependencies while keeping a low computational complexity. Recently, the emergence of Mamba architectures based on state space models (SSMs) can remedy the above problems. In this article, we propose a visual Mamba-based multiscale feature extraction network to efficiently interactively fuse global and local information, which is named as MF-VMamba (MF: multiscale feature). First, a VMamba-based encoder is used to extract multiscale semantic features from bitemporal images. Then, a feature enhancement module (FEM) is proposed to capture the difference information between images. In addition, we employ a multilevel attention decoder (MAD) based on large kernel convolution (LKC) to obtain the information in spatial and spectral dimensions to realize the information interaction between global and local features. After the sequential processing of these three modules, the discriminative ability of changing objects is significantly improved. Notably, the computational complexity of our VMamba-based model grows linearly, which can significantly reduce the computational cost. In the experiments, our method performs well on CDD, DSIFN-CD, LEVIR-CD, and SYSU-CD datasets, with$F1$scores and OA reaching$95.69\%/88.05\%/90.64\%{/86.95\%}$and$98.97\%/96.01\%/99.07\%{/90.75\%}$, respectively. The code can be accessed athttps://github.com/121zzy/MF-Mamba.git.

Change detection is a process of identifying changed ground objects by comparing image pairs obtained at different times. Compared with the pixel-level and object-level change detection, scene-level change detection can provide the semantic changes at image level, so it is important for many applications related to change descriptions and explanations such as urban functional area change monitoring. Automatic scene-level change detection approaches do not require ground truth used for training, making them more appealing in practical applications than nonautomatic methods. However, the existing automatic scene-level change detection methods only utilize low-level and mid-level features to extract changes between bitemporal images, failing to fully exploit the deep information. This article proposed a novel automatic binary scene-level change detection approach based on deep learning to address these issues. First, the pretrained VGG-16 and change vector analysis are adopted for scene-level direct predetection to produce a scene-level pseudo-change map. Second, pixel-level classification is implemented by using decision tree, and a pixel-level to scene-level conversion strategy is designed to generate the other scene-level pseudo-change map. Third, the scene-level training samples are obtained by fusing the two pseudo-change maps. Finally, the binary scene-level change map is produced by training a novel scene change detection triplet network (SCDTN). The proposed SCDTN integrates a late-fusion subnetwork and an early fusion subnetwork, comprehensively mining the deep information in each raw image as well as the temporal correlation between two raw images. Experiments were performed on a public dataset and a new challenging dataset, and the results demonstrated the effectiveness and superiority of the proposed approach

Scene change detection identifies functional changes at the scene level. Compared with pixel-level and object-level change detection, it can provide a higher level understanding of changes on the Earth’s surface. Triple-branch networks that perform scene binary change detection and scene classification tasks simultaneously are competitive in the field of scene change detection, as they consider both single-temporal scene semantic information and cross-temporal change features. However, some problems still exist. First, the temporal change feature extraction is insufficient, and the 1-D feature vector used for scene change detection and classification is lacking in representativeness. Second, the predicted scene binary change detection and classification results are often contradictory at the network prediction stage, leading to the unsatisfactory performance of change trajectory identification. To address these issues, a novel framework that integrates a differential aggregation network (DAN) and class probability-based fusion strategy (CPFS) was proposed. The designed DAN can fully capture the temporal change features using four advanced differential fusion modules (DFMs) to aggregate the multilevel difference information. In addition, it is able to generate more representative 1-D feature vectors by adopting two novel attention-aware adaptive pooling modules (AAPMs). The developed CPFS produces the final consistent scene binary change detection and classification maps by fusing three predicted class probability vectors. The proposed method was validated on two datasets, and the results demonstrated its superiority to the comparison methods.

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Change detection is a research hotspot in the remote sensing field. In this letter, an unsupervised change detection method was proposed by optimizing two critical steps, i.e., the generation and analysis of difference image. First, the difference vectors of features are calculated using the simple differencing method. Some changed and unchanged pixels are generated by the majority voting on the results produced by clustering the difference vectors and then are used for the weight calculation of difference vectors. The weights are calculated by means of F-Score and considered in the weighted change vector analysis to produce a discriminative difference image. Finally, the change map is obtained by the improved Markov random field which takes the difference in the neighborhood pixel values into account. Experimental results on three data sets demonstrated that the proposed method outperformed six unsupervised change detection methods in terms of overall accuracy.

Continuous change detection (CCD) is one of the most famous algorithms in remote sensing time series change detection, making any improvements on it are of great significance for the practice of monitoring land cover dynamics. Inspired by the directionality of CCD, a bidirectional CCD (Bi-CCD) is proposed to improve the accuracy of change detection by selecting the optimal result from the results of CCD running from both the forward and backward directions of time series. Three criteria including root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were adopted to define the “optimal” in this study, and their effects under three common parameter settings were evaluated in a simulation dataset. The quantitative results show that Bi-CCD based on different result selection criteria can indeed improve the accuracy of change detection in terms of omission error and commission error. In general, Bi-CCD based on RMSE achieved the lowest omission error, while Bi-CCD based on BIC obtained the lowest commission error. Compared with the unidirectional CCD, Bi-CCD reduces the omission error and commission error by at most 11.19% and 15.08%, respectively. In addition, the different effects of different optimal result selection criteria on the accuracy of Bi-CCD enable Bi-CCD more flexible to handle different tasks than the standard CCD.