The inherent speckle noise in synthetic aperture radar (SAR) images limits the accuracy of SAR image change detection. As a crucial step in unsupervised change detection, existing difference map generation methods primarily utilise neighbourhood information to counteract the interference caused by speckle noise. However, pixels within the neighbourhood can themselves be affected by heterogeneous pixels and noise. Therefore, this paper proposes a difference map generation method, partial neighbourhood ratio (PNR), which relies on high-confidence homogeneous pixels within the neighbourhood for difference calculation. Specifically, under the assumption that the local neighbourhood of SAR images follows a normal distribution, we develop a method for selecting high-confidence homogeneous pixels. This method quantifies inter-neighborhood dissimilarity by leveraging the statistical features of predominantly homogeneous pixel clusters within an adaptive framework, thereby reducing the impact of noise and enhancing the accuracy of difference expression. Experimental results demonstrate the superior performance of the proposed PNR. The change detection results, obtained by applying both manual trial-and-error and dual-domain network on three SAR datasets, have validated the effectiveness of the proposed algorithm.

Detecting changes in impervious surface from remote sensing images helps evaluate urbanization and environmental impacts and provides an important basis for urban planning and ecological protection. Most existing impervious surface change detection (CD) methods are supervised, which makes them time-consuming and laborious in practical applications. In this letter, a coarse-to-fine hierarchical approach, RIDKDC, which refines initial land cover CD using a domain knowledge driven constraint (DKDC) method, was proposed for unsupervised impervious surface CD. First, the initial land cover CD map containing all types of changes is generated by iteratively reweighted multivariate alteration detection (IRMAD) method. Then, DKDC is applied to mask the nonimpervious surface changes and pseudo changes, retaining only the changes in impervious surface. Finally, the desired result is obtained by mathematical morphology processing. Experimental results show that the proposed RIDKDC outperformed the compared methods.

The utilisation of synthetic aperture radar (SAR) imagery for change detection can effectively circumvents the stringent limitations imposed by weather and lighting conditions, and is finding widespread applications in fields such as disaster monitoring and urban research. To address issues of edge blurring, severe noise interference and sample imbalance, an automated SAR change detection framework is proposed based on enhanced edge information and prototype constrained clustering. Firstly, a gradient-based neighbourhood ratio is designed to reinforce the edge information of the difference map, facilitating robust differential information representations. Subsequently, to obtain accurate samples in an unsupervised manner, we have developed prototype constrained hierarchical clustering for pre-classification. The quantity and quality of selected samples can be precisely guaranteed through the utilisation of histogram analysis and prototype constraints. In the sample learning and prediction phases, a class-balanced noise-tolerant change detection network is proposed that combines focal loss and mean absolute error loss, further tackling the sample imbalance issue, strengthening noise resistance and improving change detection accuracy. Comprehensive experimental results and analysis conducted on five benchmark datasets have validated the effectiveness and robustness of the proposed method.

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.

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.

Coal, as a traditional energy source, has made remarkable contributions to global economic development. However, surface coal mining brings a series of eco-environmental problems. Therefore, it is crucial to obtain the distribution information of coal mines. Due to the diverse appearance of coal mines and complex background environments, it is very challenging to identify coal mines at a large scale. Exposed coal is an important indicator of coal mining. Spectral indices based on satellite images possess the advantages of simplicity and high efficiency. In this study, the Exposed Coal Index (ECI) was proposed. It enables the accurate identification of exposed coal at a large scale. The effectiveness of the ECI was investigated in four typical surface coal mine distribution regions across the world. Through spectral analysis, two key characteristics of coal spectra were discovered (i.e., the flat spectral shape in the visible to near-infrared range and the low reflectance in the near-infrared band). The ECI utilized these two features to successfully differentiate coal from various background land cover types in all study cases. The results showed that the ECI was effective in visual evaluation, separability analysis, and coal mapping, with superior performance than the three previously proposed indices. The ECI can also be perfectly applied to Landsat 8 images, demonstrating its excellent generalization capability. In addition, compared with three global mining datasets, ECI provided more comprehensive information on coal mine distribution. The proposed ECI is simple, robust, and expected to provide strong support for regional resource management and sustainable development.

As an important agriculture technique, plastic greenhouse (PG) has been widely used to increase crop yield and improve food security status in the world. The high-resolution spatial information of PG is of great significance to precise agricultural management and quantitative environmental assessment. Many studies have examined the role that remote sensing technology could play in mapping and monitoring PG coverage. However, these methods, which employ either the traditional machine learning algorithms or the deep learning models, depend on massive manually labeled samples. To address this problem, this paper proposes a new cross-scale sample transferring method to generate high-resolution samples for automated PG mapping. The proposed method aims to transfer reliable label information from Sentinel-2 images (10-m) to high-resolution images (0.2-m) in a pixel-scene-pixel-object (PSPO) transferring process. In the proposed PG mapping workflow, the low-resolution label information of PG/non-PG can be obtained from an advanced plastic greenhouse index (APGI) which is calculated in Sentinel-2 images, and then the label information is transferred to the corresponding high-resolution images using the proposed PSPO transferring method. Finally, the transferred high-resolution samples are used to train the deep semantic segmentation model and produce PG mapping results. The whole process is labor-free which requires no manually labeled samples. The experimental results on three collected datasets show that the proposed approach can automatically generate accurate and reliable high-resolution samples, and the final PG mapping results can achieve an OA (overall accuracy) of 89.52% ~ 97.65% and F1 score of 84.13% ~ 94.03%, which is comparable to the fully supervised semantic segmentation model.

Accurate cropland mapping is significant for food security and sustainable development. The existing cropland map based on remote sensing mainly focus on moderate to coarse spatial resolution, and these products are generally unsuitable for precision agriculture due to the lack of spatial details. Therefore, there is an urgent need to produce high-resolution (HR) cropland maps to meet current application demands. Recently, the typical classification workflow of HR images employs deep learning models combined with manually annotated samples, and visual interpretation of samples is usually labor-intensive and time-consuming, which is not conducive to large-scale applications. To address this problem, this paper proposes an automated HR cropland extraction solution, namely RRE (Refinement-Reclassification-Extraction), including (i) Refinement of 10 m spatial resolution cropland products, (ii) Reclassifying cropland using the refined product as sample source, and (iii) Extracting HR cropland via designed cross-scale sample transfer. The strength of the proposed framework is that it leverages existing moderate-resolution public products as prior knowledge and provides cross-scale transferable samples for HR images. The whole process does not require manual labeling of samples and is highly automated. Specifically, the experimental results in the three main grain production regions show that, the RRE framework effectively reduces the interference of road networks and ridges, and F1 scores of extracted 1 m HR cropland reaches 87.71 %~94.16 %, which is comparable to the fully supervised cropland extraction method. In addition, the 10 m reclassified cropland, produced by the intermediate process of the RRE, outperforms current cropland product of ESRI Land Cover and ESA World Cover.

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.