Saliency detection is a conventional computer vision technique used to identify salient regions in difference images for change detection (CD) in multi-temporal/bi-temporal remotely sensed images. However, most existing saliency-based CD methods have primarily focused on analyzing the colors and brightness of different images, often neglecting the rich spectral-spatial and context-aware features of remotely sensed multi-spectral images. Furthermore, these methods independently leverage the visual saliency features and spectral-spatial features of remote sensing images, without seamlessly integrating the two components. In response to this limitation, this paper proposes a novel CD method based on context-aware saliency-spectral-spatial features, introducing a weighted coarse-to-fine fusion strategy. The proposed method fully leverages context-aware saliency features, as well as spectral and spatial features from remotely sensed images. It summarizes the combination of these features into three principles: local low-level feature, global low-level feature, and high-level feature. Then, with the assistance of segmentation objects, the proposed method generates a single-scale salience map through global-local comparisons between an object and its K-level neighborhood objects, employing the three principles of multi-level features. Finally, in accordance with principles 2 and 3, a weighted coarse-to-fine fusion strategy at the pixel level is designed to incorporate multi-scale saliency maps. Fusion weights for pixels, from coarse-to-fine scale, are adaptively obtained by considering the heterogeneity of the object they belong to and the intensity difference between the pixel and the object. To verify the effectiveness of the proposed method, ten comparative experiments were conducted against other state-of-the-art methods The experimental results show that the proposed method better employs context-aware saliency-spectral-spatial features to preserve the details of CD map changes and reduce noise. Furthermore, experiments conducted on three datasets, featuring various region types, region sizes, and spatial resolutions of remotely sensed images, highlight the feasibility and transferability of the proposed method for change detection.