Object-Oriented Change Detection Method Based on Spectral–Spatial–Saliency Change Information and Fuzzy Integral Decision Fusion for HR Remote Sensing Images

Abstract

Spectral features in remote sensing images are extensively utilized to detect land cover changes. However, detection noise appearing in the changing maps due to the abundant spatial details in the high-resolution images makes it difficult to acquire an accurate interpretation result. In this paper, an object-oriented change detection approach is proposed which integrates spectral–spatial–saliency change information and fuzzy integral decision fusion for high-resolution remote sensing images with the purpose of eliminating the impact of detection noise. First, to reduce the influence of feature uncertainty, spectral feature change is generated by three independent methods, and spatial change information is obtained by spatial feature set construction and the optimal feature selection strategy. Secondly, the saliency change map of bi-temporal images is obtained with the co-saliency detection method to complement the insufficiency of image features. Then, the image objects are acquired by multi-scale segmentation based on the staking images. Finally, different pixel-level image change information and the segmentation result are fused using the fuzzy integral decision theory to determine the object change probability. Three high-resolution remote sensing image datasets and three comparative experiments were carried out to evaluate the performance of the proposed algorithm. Spectral–spatial–saliency change information was found to play a major role in the change detection of high-resolution remote sensing images, and the fuzzy integral decision strategy was found to effectively obtain reliable changed objects to improve the accuracy and robustness of change detection.

Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification—A Review

“…In [49], a novel method was used for spatiotemporal data fusion with the help of Bayesian decision theory. An object-oriented method for change detection was discussed again in [50] and achieved a solid fusion using fuzzy integral decision rules. In [51], the authors introduced a novel algorithm rooted in the methodology of a fuzzy decision tree, utilizing spectral bands from multispectral imagery as attributes from fuzzy data sources, along with cumulative mutual information for decision tree induction, which not only enhances classification accuracy compared to traditional methods but also achieves substantial data dimensionality reduction through the selection of informative spectral bands.…”

A Siamese Network Combining Multiscale Joint Supervision and Improved Consistency Regularization for Weakly Supervised Building Change Detection

“…Depending on the above GradCAM++ technique, the multiscale changed building CAMs from low-level detailed information from high-level semantic features can be obtained in sequence. Finally, using the fusion strategy proposed in [30], we can merge the multiscale changed building CAMs into the final CAM by the CAM final = 1 3 CAM n , where n ∈ (23,4) represent that we just use the CAMs generated from the second, third, and last residual units.…”

“…Digital Object Identifier 10.1109/JSTARS.2023.3279863 two or more periods, and it can serve for urban planning, disaster rescue, and illegal building assessment [1], [2], [3], [4], [5], [6], [7], [8], [9]. Recently, with its powerful capacity for feature expression, deep learning-based semantic segmentation (SS) has achieved good performance in BCD from very high resolution (VHR) remote sensing images [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24].…”

A Bi-Temporal-Feature-Difference- and Object-Based Method for Mapping Rice-Crayfish Fields in Sihong, China

“…Li et al [21] divided the changed and non-changed regions according to the change vector value of each object. Ge et al [22] integrated spectral-spatial-saliency change information and fuzzy integral decision fusion to identify the changing areas. However, the abovementioned methods can only obtain the locations of all the changed areas, while their types cannot be identified.…”