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.

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.

Automatic sea‐land segmentation is an essential and challenging field for the practical use of panchromatic satellite imagery. Owing to the temporal variations as well as the complex and inconsistent intensity contrast in both land and sea areas, it is difficult to generate an accurate segmentation result by using the conventional thresholding methods. Additionally, the freely available digital elevation model (DEM) also difficultly meets the requirements of high‐resolution data for practical usage, because of the low precision and high memory storage costs for the processing systems. In this case, we proposed a fully automatic sea‐land segmentation approach for practical use with a hierarchical coarse‐to‐fine procedure. We compared our method with other state‐of‐the‐art methods with real images under complex backgrounds and conducted quantitative comparisons. The experimental results show that our method outperforms all other methods and proved being computationally efficient.

Ground-glass opacity (GGO) detection is paramount for the prognosis and diagnosis of lung diseases. We present a novel GGO detection method for 2D lung CT images in this paper, which focuses on detecting GGOs with high sensitivity and reducing false positives as much as possible. To this end, we propose a local-to-global multilevel thresholding algorithm for segmentation and a novel discriminative learning algorithm for identification to solve the problem of GGO detection. There are two components in our method. In the first component, we perform clustering on the local Ostu thresholds of CT levels for each patch of an image, the candidate regions of interests (ROIs) are segmented based on the clustering results by multilevel thresholding techniques. The second component is a Bayesian modeling process for identifying the GGOs from ROI candidates, the classifier is trained based on Bayesian risk minimization and margin maximization by our discriminative learning algorithm. The proposed GGO detection approach is evaluated on the LISS database with 45 GGOs. Finally, our detection approach performed better than other GGO detection methods in the experimental results, which achieved a sensitivity of 100% and a specificity of 33.13%.

Computer-aided detection (CAD) of lobulation can help radiologists to diagnose/detect lung diseases easily and accurately. Compared to CAD of nodule and other lung lesions, CAD of lobulation remained an unexplored problem due to very complex and varying nature of lobulation. Thus, many state-of-the-art methods could not detect successfully. Hence, we revisited classical methods with the capability of extracting undulated characteristics and designed a sliding window based framework for lobulation detection in this paper. Under the designed framework, we investigated three categories of lobulation classification algorithms: template matching, feature based classifier, and bending energy. The resultant detection algorithms were evaluated through experiments on LISS database. The experimental results show that the algorithm based on combination of global context feature and BOF encoding has best overall performance, resulting inF1score of 0.1009. Furthermore, bending energy method is shown to be appropriate for reducing false positives. We performed bending energy method following the LIOP-LBP mixture feature, the average positive detection per image was reduced from 30 to 22, andF1score increased to 0.0643 from 0.0599. To the best of our knowledge this is the first kind of work for direct lobulation detection and first application of bending energy to any kind of lobulation work.