Compressive sensing (CS) has been intensively studied as a superresolution technique in image acquisition and reconstruction. It has also been introduced into three-dimensional (3-D) laser imaging to enhance the spatial resolution of images obtained from existing few-pixel array sensors. These approaches, however, either suffer from low range resolution or lack long-range detecting abilities. To solve the CS reconstruction problem under the 3-D pulsed-laser imaging framework, we build a sampling model to combine the CS sampling process with the time-of-flight-based range measurement procedure in pulsed-laser imaging. To explore range information from the overlapping echo signals, we develop a gradual alternating minimization algorithm with a designed range equivalent matrix, in which the feasible region of the solution gradually shrinks to make the reconstruction procedure tractable. To further enhance the quality of the reconstructed intensity image, we introduce rank minimization and a fallback mechanism, in which the sparsity and stability of the reconstructed intensity image is refined. In this manner, the accuracy of reconstructed 3-D images can be improved in both the pixel direction and range direction. The simulation results based on real datasets demonstrate that our proposed scheme can yield superior accurate reconstruction and leads to significant improvements compared with recent approaches.

Multiscale segmentation is always needed to extract semantic meaningful objects for object-based remote sensing image analysis. Choosing the appropriate segmentation scales for distinct ground objects and intelligently combining them together are two crucial issues to get the appropriate segmentation result for target applications. With respect to these two issues, this paper proposes a simple scale-synthesis method which is highly flexible to be adjusted to meet the segmentation requirements of varying image-analysis tasks. The main idea of this method is to first divide the whole image area into multiple regions; each region consisted of ground objects that have similar optimal segmentation scale. Then, synthesize the suboptimal segmentations of each region to get the final segmentation result. The result is the combination of suboptimal scales of objects and is therefore more coherent to ground objects. To validate this method, the land-cover-category map is used to guide the scale synthesis of multiscale image segmentations for the Quickbird-image land-use classification. First, the image is coarsely divided into multiple regions; each region belongs to a certain land-cover category. Then, multiscale-segmentation results are generated by the Mumford-Shah function based region-merging method. For each land-cover category, the optimal segmentation scale is selected by the supervised segmentation-accuracy-assessment method. Finally, the optimal scales of segmentation results are synthesized under the guide of land-cover category. It is proved that the proposed scale-synthesis method can generate a more accurate segmentation result that benefits the latter classification. The land-use-classification accuracy reaches to 77.8%.

This correspondence proposes an edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation. Two improvement techniques are proposed for the two key steps of maker extraction and pixel labeling, respectively, to make it more effective and efficient for high spatial resolution image segmentation. Moreover, the edge information, detected by the edge detector embedded with confidence, is used to direct the two key steps for detecting objects with weak boundary and improving the positional accuracy of the objects boundary. Experiments on different images show that the proposed method has a good generality in producing good segmentation results. It performs well both in retaining the weak boundary and reducing the undesired over-segmentation.