Pan-sharpening is an important preprocessing step for remote sensing image processing tasks; it fuses a low-resolution multispectral image and a high-resolution (HR) panchromatic (PAN) image to reconstruct a HR multispectral (MS) image. This paper introduces a new end-to-end bidirectional pyramid network for pan-sharpening. The overall structure of the proposed network is a bidirectional pyramid, which permits the network to process MS and PAN images in two separate branches level by level. At each level of the network, spatial details extracted from the PAN image are injected into the upsampled MS image to reconstruct the pan-sharpened image from coarse resolution to fine resolution. Subpixel convolutional layers and the enhanced residual blocks are used to make the network efficient. Comparison of the results obtained with our proposed method and the results using other widely used state-of-the-art approaches confirms that our proposed method outperforms the others in visual appearance and objective indexes.

Digital terrain models (DTMs) are considered important basic geographic data. They are widely used in the fields of cartography, land utilization, urban planning, communications, and remote sensing. Digital photogrammetry mainly based on stereo image matching is a frequently applied technique to generate DTMs. Generally, the process of ground filtering should be applied to the point cloud derived from image matching to separate terrain and off-terrain points before DTM generation. However, many of the existing filtering methods perform unsatisfactorily for steep mountainous areas, particularly when residential neighborhoods exist in the proximity of the test areas. In this study, an improved automated filtering method based on progressive TIN (triangulated irregular networks) densification (PTD) is proposed to generate DTMs for steep mountainous areas and adjacent residential areas. Our main improvement on the classic method is the acquisition of seed points with better distribution and reliability to enhance its adaptability to different types of terrain. A rule-based method for detecting ridge points is first applied. The detected points are used as additional seed points. Subsequently, a locally optimized seed point selection method based on confidence interval estimation theory is applied to remove the erroneous points. The experiments on two sets of stereo-matched point clouds indicate that the proposed method performs well for both residential and mountainous areas. The total accuracy values in the form of root-mean-square errors of the generated DTMs by the proposed method are 0.963 and 1.007 m; respectively; which are better than the 1.286 and 1.309 m achieved by the classic PTD method.

Intelligent seamline selection for image mosaicking is an area of active research in the fields of massive data processing, computer vision, photogrammetry and remote sensing. In mosaicking applications for digital orthophoto maps (DOMs), the visual transition in mosaics is mainly caused by differences in positioning accuracy, image tone and relief displacement of high ground objects between overlapping DOMs. Among these three factors, relief displacement, which prevents the seamless mosaicking of images, is relatively more difficult to address. To minimize visual discontinuities, many optimization algorithms have been studied for the automatic selection of seamlines to avoid high ground objects. Thus, a new automatic seamline selection algorithm using a digital surface model (DSM) is proposed. The main idea of this algorithm is to guide a seamline toward a low area on the basis of the elevation information in a DSM. Given that the elevation of a DSM is not completely synchronous with a DOM, a new model, called the orthoimage elevation synchronous model (OESM), is derived and introduced. OESM can accurately reflect the elevation information for each DOM unit. Through the morphological processing of the OESM data in the overlapping area, an initial path network is obtained for seamline selection. Subsequently, a cost function is defined on the basis of several measurements, and Dijkstra’s algorithm is adopted to determine the least-cost path from the initial network. Finally, the proposed algorithm is employed for automatic seamline network construction; the effective mosaic polygon of each image is determined, and a seamless mosaic is generated. The experiments with three different datasets indicate that the proposed method meets the requirements for seamline network construction. In comparative trials, the generated seamlines pass through fewer ground objects with low time consumption.

A new mixed radiometric normalization (MRN) method is introduced in this paper which aims to eliminate the radiometric difference in image mosaicking. The radiometric normalization methods can be classified as the absolute and relative approaches in traditional solutions. Though the absolute methods could get the precise surface reflectance values of the images, rigorous conditions required for them are usually difficult to obtain, which makes the absolute methods impractical in many cases. The relative methods, which are simple and practicable, are more widely applied. However, the standard for designating the reference image needed for these methods is not unified. Moreover, the color error propagation and the two-body problems are common obstacles for the relative methods. The proposed MRN approach combines absolute and relative radiometric normalization methods, by which the advantages of both can be fully used and the limitations can be effectively avoided. First, suitable image after absolute radiometric calibration is selected as the reference image. Then, the invariant feature probability between the pixels of the target image and that of the reference image is obtained. Afterward, an adaptive local approach is adopted to obtain a suitable linear regression model for each block. Finally, a bilinear interpolation method is employed to obtain the radiometric calibration parameters for each pixel. Moreover, the CIELAB color space is adopted to evaluate the results quantitatively. Experimental results of ZY-3, GF-1, and GF-2 data indicate that the proposed method can eliminate the radiometric differences between images from the same or even different sensors.

Pan-sharpening is a fundamental task for remote sensing image processing. It aims at creating a high-resolution multispectral (HRMS) image from a multispectral (MS) image and a panchromatic (PAN) image. In this article, a new band-independent encoder-decoder network is proposed for pan-sharpening. The network takes a single band of the MS (BMS) image, the PAN image, and the low-resolution PAN (LRPAN) image as inputs. The output of the network is the corresponding band of high-resolution MS (HRBMS) image. In this way, the network can process MS images with any number of bands. The overall structure of the network consists of two encoder-decoder modules at low-resolution and high-resolution, respectively. An auxiliary LRPAN image is used to speed up the training and improve the performance. The partly shared network and hierarchical structure for low-resolution and high-resolution enable a better fusion of features extracted from different scales. With a fast fine-tuning strategy, the trained model can be applied to images from different sensors. Experiments performed on different data sets demonstrate that the proposed method outperforms several state-of-the-art pan-sharpening methods in both visual appearance and objective indexes, and the single-band evaluation results further verify the superiority of the proposed method.

Automatic image registration (AIR) has been widely studied in the fields of medical imaging, computer vision, and remote sensing. In various cases, such as image fusion, high registration accuracy should be achieved to meet application requirements. For satellite images, the large image size and unstable positioning accuracy resulting from the limited manufacturing technology of charge-coupled device, focal plane distortion, and unrecorded spacecraft jitter lead to difficulty in obtaining agreeable corresponding points for registration using only area-based matching or feature-based matching. In this situation, a coarse-to-fine matching strategy integrating two types of algorithms is proven feasible and effective. In this paper, an AIR method for application to the fusion of ZY-1-02C satellite imagery is proposed. First, the images are geometrically corrected. Coarse matching, based on scale invariant feature transform, is performed for the subsampled corrected images, and a rough global estimation is made with the matching results. Harris feature points are then extracted, and the coordinates of the corresponding points are calculated according to the global estimation results. Precise matching is conducted, based on normalized cross correlation and least squares matching. As complex image distortion cannot be precisely estimated, a local estimation using the structure of triangulated irregular network is applied to eliminate the false matches. Finally, image resampling is conducted, based on local affine transformation, to achieve high-precision registration. Experiments with ZY-1-02C datasets demonstrate that the accuracy of the proposed method meets the requirements of fusion application, and its efficiency is also suitable for the commercial operation of the automatic satellite data process system.