Change vector analysis (CVA) and post-classification change detection (PCC) have been the most widely used change-detection methods. However, CVA requires sound radiometric correction to achieve optimal performance, and PCC is susceptible to accumulated classification errors. Although change vector analysis in the posterior probability space (CVAPS) was developed to resolve the limitations of PCC and CVA, the uncertainty of remote sensing imagery limits the performance of CVAPS owing to three major problems: 1) mixed pixels, 2) identical ground cover type with different spectra, and 3) different ground cover types with the same spectrum. To address this problem, this study proposes the FCM-CSBN-CVAPS approach under the CVAPS framework. The proposed approach decomposes the mixed pixels into multiple signal classes using the fuzzy C means (FCM) algorithm. Although the mixed pixel problem is less severe in the high-resolution image, the change detection performance is still enhanced because, as a soft clustering algorithm, FCM is less susceptible to cumulative clustering error. Then, a context-sensitive Bayesian network (CSBN) is constructed to establish multiple-to-multiple stochastic linkages between signal pairs and ground cover types by incorporating spatial information to resolve problems 2 and 3 discussed above. Finally, change detection is performed using CVAPS in the posterior probability space. The effectiveness of the proposed approach is evaluated on three bi-temporal remote sensing datasets with different spatial sizes and resolutions. The experimental results confirm the effectiveness of FCM-CSBN-CVAPS in addressing the uncertainty problems of change detection and its superiority over other relevant change-detection techniques.

Water use efficiency of vegetation (WUE), the ratio of carbon gain to water loss, is a valid indicator to describe the photosynthetic carbon–water coupling relationship. Understanding how and why WUE changes are essential for regional ecological conservation. However, the impacts of various factors and their interactions on the spatial variation of WUE remain uncertain in the arid land of Northwest China. Here, we selected the Qilian Mountains (QM) and Hexi Corridor (HC) as the study areas. Supported by the Google Earth Engine, we explored the spatio-temporal variations of WUE in QM and HC for 2002–2021 using STL decomposition (a seasonal-trend decomposition procedure), trend analysis, and the Hurst index. Then, the Geodetector method was applied to quantify impacts of geographical and eco-meteorological factors on the spatial variation of WUE. The WUE in HC was higher than that in QM. Interestingly, the opposite longitude zonality characteristics are shown in the QM and HC. In QM, the WUE showed an upward trend with longitude increasing, while a downward trend with longitude increasing in the oases of HC. The WUE of cropland was the highest (1.15 ± 0.35 gC kg⁻¹ H₂O), and that of alpine vegetation was the lowest (0.2 ± 0.15 gC kg⁻¹ H₂O). WUE showed a decreasing trend across the study area, almost certainly due to a drop from May to July during 2002–2021. The air temperature is the dominant factor influencing the spatial variation of WUE. In addition, the interaction of any two factors is greater than the independent influence of either factor alone. The Geodetector method proved to be effective for quantifying the impact of complex multi-factors on the spatial variation of WUE. This study provides a new technical scheme to analyze the spatio-temporal pattern and quantify the impact of multi-factors on the spatial variation of WUE. These findings aid in understanding underlying mechanisms of WUE variation and thereby will be beneficial for clarifying the response of vegetation to climate change.

Hyperspectral satellite imagery has developed rapidly over the last decade because of its high spectral resolution and strong material recognition capability. Nonetheless, the spatial resolution of available hyperspectral imagery is inferior, severely affecting the accuracy of ground object identification. In the paper, we propose an adaptively optimized pulse-coupled neural network (PCNN) model to sharpen the spatial resolution of the hyperspectral imagery to the scale of the multispectral imagery. Firstly, a SAM-CC strategy is designed to assign hyperspectral bands to the multispectral bands. Subsequently, an improved PCNN (IPCNN) is proposed, which considers the differences of the neighboring neurons. Furthermore, the Chameleon Swarm Optimization (CSA) optimization is adopted to generate the optimum fusion parameters for IPCNN. Hence, the injected spatial details are acquired in the irregular regions generated by the IPCNN. Extensive experiments are carried out to validate the superiority of the proposed model, which confirms that our method can realize hyperspectral imagery with high spatial resolution, yielding the best spatial details and spectral information among the state-of-the-art approaches. Several ablation studies further corroborate the efficiency of our method.

Change vector analysis in posterior probability space (CVAPS) is an effective change detection (CD) framework that does not require sound radiometric correction and is robust against accumulated classification errors. Based on training samples within target images, CVAPS can generate a uniformly scaled change-magnitude map that is suitable for a global threshold. However, vigorous user intervention is required to achieve optimal performance. Therefore, to eliminate user intervention and retain the merit of CVAPS, an unsupervised CVAPS (UCVAPS) CD method, RFCC, which does not require rigorous user training, is proposed in this study. In the RFCC, we propose an unsupervised remote sensing image segmentation algorithm based on the Mamba model, i.e., RVMamba differentiable feature clustering, which introduces two loss functions as constraints to ensure that RVMamba achieves accurate segmentation results and to supply the CSBN module with high-quality training samples. In the CD module, the fuzzy C-means clustering (FCM) algorithm decomposes mixed pixels into multiple signal classes, thereby alleviating cumulative clustering errors. Then, a context-sensitive Bayesian network (CSBN) model is introduced to incorporate spatial information at the pixel level to estimate the corresponding posterior probability vector. Thus, it is suitable for high-resolution remote sensing (HRRS) imagery. Finally, the UCVAPS framework can generate a uniformly scaled change-magnitude map that is suitable for the global threshold and can produce accurate CD results. The experimental results on seven change detection datasets confirmed that the proposed method outperforms five state-of-the-art competitive CD methods.

Heterogeneous high-resolution remote sensing image matching will be disturbed by the differences in sensor type, imaging angle, height, and imaging time, and the matching difficulty is further increased in complex scenes with dense urban buildings and noticeable height differences. This paper proposes a method for matching heterogeneous high-resolution remote sensing images based on partitioned feature extraction and three-dimensional spatial constraints. First, this paper conducts image partitioning based on the geometric differences of ground objects. Two feature extraction methods, namely, adaptive phase threshold and weighted moment map, are employed to extract feature points independently. To address the issue of inaccurate feature descriptions caused by drastic changes in viewing angles in buildings, we construct a robust feature descriptor by combining a multi-scale phase weighted energy convolution histogram (MSPW-ECH) with a new gradient location orientation histogram (GLOH)-like local feature descriptor. Additionally, a new similarity measure incorporating three-dimensional spatial constraints and the Marginalizing Sample Consensus (MAGSAC) method is applied to eliminate mismatched point pairs, ensuring the acquisition of precise matching points. Based on the feature detection results of two different synthetic data sets, it is evident that the proposed detector outperforms the three classical detectors in terms of repeatability and uniformity. Ultimately, the matching performance is experimentally verified on six groups of heterogeneous high-resolution remote sensing images. The experimental results show that the proposed method significantly outperforms RIFT, HAPCG, and MS-HLMO methods and achieves the best matching accuracy results.

In recent years, Bayesian networks using unsupervised extracted image features have been applied in many remote sensing information mining systems to enable semantic-sensitive image retrieval. However, a simple Bayesian network insufficiently accounts for the spatial information, that is, the relations among image regions, for the semantic inference process. This drawback significantly impacts the retrieval performance, especially if the utilised features contain no or little spatial information. Therefore, this article proposes a context-sensitive Bayesian network, which infers semantic concepts of image regions based on the spectral and textural characteristics of the regions themselves as well as their contexts, that is, the adjacent regions. In order to compare the context-sensitive Bayesian network with the simple Bayesian network, comprehensive experiments were conducted based on high-resolution multispectral IKONOS imagery. The results show that the incorporation of the image regions' spatial relations not only significantly improves the accuracy of the semantic concepts inference, but also allows more flexibility in choosing the type of low-level features.