The linear array push broom imaging mode is widely used for high resolution optical satellites (HROS). Using double-cameras attached by a high-rigidity support along with push broom imaging is one method to enlarge the field of view while ensuring high resolution. High accuracy image mosaicking is the key factor of the geometrical quality of complete stitched satellite imagery. This paper proposes a high accuracy image mosaicking approach based on the big virtual camera (BVC) in the double-camera system on the GaoFen2 optical remote sensing satellite (GF2). A big virtual camera can be built according to the rigorous imaging model of a single camera; then, each single image strip obtained by each TDI-CCD detector can be re-projected to the virtual detector of the big virtual camera coordinate system using forward-projection and backward-projection to obtain the corresponding single virtual image. After an on-orbit calibration and relative orientation, the complete final virtual image can be obtained by stitching the single virtual images together based on their coordinate information on the big virtual detector image plane. The paper subtly uses the concept of the big virtual camera to obtain a stitched image and the corresponding high accuracy rational function model (RFM) for concurrent post processing. Experiments verified that the proposed method can achieve seamless mosaicking while maintaining the geometric accuracy.

The GaoFen1 (GF1) optical remote sensing satellite is the first in China’s series of high-resolution civilian satellites and is equipped with four wide field of view (WFV) cameras. High accuracy image mosaicking depends on the geometrical quality of the stitched satellite imagery. This letter proposes a high accuracy image mosaicking approach based on combining multiple WFV cameras into a big virtual camera (BVC). In this method, a BVC is created according to the rigorous imaging model of a single camera, from which a final stitched image and corresponding high accuracy rational function model (RFM) can be obtained simultaneously. WFV images from GF1 and reference digital elevation models from ZiYuan3 are used to validate the proposed method. The experimental results validate that seamless mosaicking can be achieved directly from the geometrical relationship of the imaging model of the BVC and WFV camera. The proposed method is able to maintain the absolute and relative internal positioning accuracy of the original images.

To increase the field of view (FOV), combining multiple time-delayed and integrated charge-coupled devices (TDI-CCD) into the camera and the pushbroom imaging modality are traditionally used with high-resolution optical satellites. It is becoming increasingly labor- and cost-intensive to build and maintain a calibration field with high resolution and broad coverage. This paper introduces a simple and feasible on-orbit geometric self-calibration approach for high-resolution multi-TDI-CCD optical satellites based on three-view stereoscopic images. With the aid of the a priori geometric constraint of tie points in the triple-overlap regions of stereoscopic images, as well as tie points between adjacent single TDI-CCD images (STIs), high accuracy calibration of all TDI-CCD detectors can be achieved using a small number of absolute ground control points (GCPs) covering the selected primary STI. This method greatly reduces the demand on the calibration field and thus is more time-, effort- and cost-effective. Experimental results indicated that the proposed self-calibration approach is effective for increasing the relative internal accuracy without the limitations associated with using a traditional reference calibration field, which could have great significance for future super-high-resolution optical satellites.

Satellite attitude accuracy is an important factor affecting the geometric processing accuracy of high-resolution optical satellite imagery. To address the problem whereby the accuracy of the Yaogan-24 remote sensing satellite’s on-board attitude data processing is not high enough and thus cannot meet its image geometry processing requirements, we developed an approach involving on-ground attitude data processing and digital orthophoto (DOM) and the digital elevation model (DEM) verification of a geometric calibration field. The approach focuses on three modules: on-ground processing based on bidirectional filter, overall weighted smoothing and fitting, and evaluation in the geometric calibration field. Our experimental results demonstrate that the proposed on-ground processing method is both robust and feasible, which ensures the reliability of the observation data quality, convergence and stability of the parameter estimation model. In addition, both the Euler angle and quaternion could be used to build a mathematical fitting model, while the orthogonal polynomial fitting model is more suitable for modeling the attitude parameter. Furthermore, compared to the image geometric processing results based on on-board attitude data, the image uncontrolled and relative geometric positioning result accuracy can be increased by about 50%.

There is inconsistency between the existing remote sensing cropland products, whose accuracy of estimated cropland area and spatial positioning needs to be improved. The existing generalized methods of generating synergy cropland products for improving the accuracy of existing products do not consider the overall consistency difference between the different products in each grid cell in the fusion process. To reduce the impact of the abnormal estimated cropland areas of the individual cropland products on the results, this paper proposes a method of generating a synergy cropland product by fusing the multiple existing cropland products, based on the overall consistency difference. In the proposed method, the process of fusing the multiple existing cropland products is based on the overall consistency difference of the estimated cropland area of all the cropland products in each grid cell. The synergy cropland product is then generated after determining the best combination level with the cropland statistics. In this study, we set 2010 as the base year, and used the proposed method to conduct experiments with four remote sensing cropland products: GlobCover 2009, MODIS Cropland, MCD12Q1, and FROM-GLC within China, and national cropland statistics. The results show that the synergy cropland product generated by the proposed method has a higher accuracy of cropland area estimation and spatial positioning than the results obtained by the generalized model, as well as the original products.