In this paper, an efficient and accurate multilevel change detection method is presented, achieved by the combination of an unsupervised object-based correlation analysis and a supervised post-classification comparison. Before the change detection procedure, fast multitemporal segmentation is applied to provide object-level information on two registered images. Then the proposed object-based correlation analysis method is used to extract the potential changed areas efficiently and stably, which improves the accuracy of overall performance. Notably, all the procedures are highly automatic except for the necessary selection of training examples within all supervised algorithms. The experimental results demonstrate the superior performance of our method compared with the four typical state-of-the-art change detection methods.

Automatic detection of moving targets is one of important research area in the remote sensing field. In this paper, we propose a method that accurately detects moving targets in aerial videos using hierarchical spatiotemporal saliency analysis. First, coarse motion regions are extracted by utilizing global temporal saliency analysis. Based on these local candidate regions, spatial saliency methods are used to obtain accurate description of targets. After fusing spatial and temporal saliency values, we can get refined results of the detection. Considering about the inter-frame consistency of motion, trajectory level analysis is added in the proposed method to eliminate false alarms. Experiments conducted on the VIVID dataset validate the effectiveness and efficiency of the proposed method.

Sea-land segmentation is a key step for some important applications of panchromatic remote sensing image processing. However, robust and effective sea-land segmentation for high-resolution panchromatic remote sensing images is still a challenging problem. This letter presents an accurate and robust approach by integrating the improved multiscale normalized cut (IMNcut) method and improved Chan-Vese model for sea-land segmentation. At first, the image is downsampled and segmented into multiple regions by the IMNcut method. Next, the homogeneous regions are merged to obtain a coarse segmentation result. Finally, gray intensity and local entropy features are integrated as discriminants of the improved Chan-Vese model, which is used to obtain the final segmentation result through a low- to high-resolution segmentation scheme. Experimental results performed on several real data sets demonstrate the effectiveness of the proposed model in terms of visual and objective evaluations.

Traditional region-of-interest (ROI) detection methods for remote sensing images are generally formulated at pixel level and are less efficient when applied on large high-resolution images. This letter presents an accurate and efficient approach via superpixel-to-pixel saliency analysis for ROI detection. At first, the image is downsampled and segmented into superpixels by simple linear iterative clustering. Next, structure tensor and background contrast are used to yield superpixel feature maps for texture and color. After fusing the feature maps, the overall superpixel saliency map is obtained and then used to achieve the final pixel-level saliency map by superpixel-to-pixel mapping. Through experimentations, we validate the effectiveness and computational efficiency of the proposed model in comparison with state-of-the-art techniques.