With the successful launch of GaoFen4 (GF4), on-orbit high accuracy geometric calibration for the high-resolution geostationary optical satellite will be a new research topic. With the improvement in the geometric resolution from geostationary orbit, it will become more and more difficult to meet the requirements of both high geometric resolution and large coverage for the available reference data. The purpose of this paper is to explore a new self-calibration mode for GF4 and future high-resolution geostationary optical area array cameras based on the fewest ground control points (GCPs). To overcome the problems of overparameterization, strong correlation and lower significance of the traditional rigorous imaging model, the simplified physical internal model is proposed, and its effectiveness in describing and compensating for the camera internal distortion is verified. Based on the simplified physical internal model, the self-calibration method based on two GCPs and evenly distributed tie points of two images is proposed for the high accuracy estimation of the calibration parameters. The GCPs can be used to provide the absolute geographical constraints for scale information, and the tie points can be used to provide the global constraints for optimum estimation. After calibration, the internal distortion is well compensated, and the positioning accuracy with relatively few GCPs is shown to be better than 1.0 pixels for both the panchromatic and near-infrared sensor and the intermediate infrared sensor. This paper will provide a new usable concept and approach for the future higher resolution geostationary area array optical camera to overcome the stringent requirements of both high resolution and a large area of reference data for the traditional calibration method.

Gaofen-1 02/03/04 satellites, the first civilian high resolution optical operational constellation in China, have Earth observation capabilities with panchromatic/multispectral imaging at 2/8 m resolution. Satellite jitter, the fluctuation of satellite points, has a negative influence on the geometric quality of high-resolution optical satellite imagery. This paper presents an improved jitter detection method based on parallax observation of multispectral sensors for Gaofen-1 02/03/04 satellites, which can eliminate the effect of the relative internal error induced by lens distortion, and accurately estimate the parameters of satellite jitter. The relative internal error is estimated by polynomial modelling and removed from the original parallax image generated by pixel-to-pixel image matching between two bands of images. The accurate relative time-varying error and absolute distortion caused by satellite jitter could be estimated by using the sine function. Three datasets of multispectral images captured by Gaofen-1 02/03/04 satellites were used to conduct the experiments. The results show that the relative system errors in both the across- and along-track directions can be modelled with a quadratic polynomial, and satellite jitter with a frequency of 1.1–1.2 Hz in the across-track direction was detected for the first time. The amplitude of the jitter differed in the three datasets. The largest amplitude, from satellite 04, is 1.3 pixels. The smallest amplitude, from satellite 02, is 0.077 pixels. The reliability and accuracy of the detection results were verified by using two groups of band combinations and ortho-images with a 1 m resolution. The comparison results show that the detection accuracy is improved by approximately 30% using the proposed method.

Mapping impervious surface area (ISA) in an accurate and timely manner is essential for a variety of fields and applications, such as urban heat islands, hydrology, waterlogging, and urban planning and management. However, the large and complex urban landscapes pose great challenges in retrieving ISA information. Spaceborne hyperspectral (HS) remote sensing imagery provides rich spectral information with short revisit cycles, making it an ideal data source for ISA extraction from complex urban scenes. Nevertheless, insufficient single-band energy, the involvement of modulation transfer function (MTF), and the low signal-to-noise ratio (SNR) of spaceborne HS imagery usually result in poor image clarity and noises, leading to inaccurate ISA extraction. To address this challenge, we propose a new deep feature fusion-based classification method to improve 10 m resolution ISA mapping by integrating Zhuhai-1 HS imagery with Sentinel-2 multispectral (MS) imagery. We extract deep features that include spectral and spatial features, respectively, from MS and HS imagery via a 2-D convolutional neural network (CNN), aiming to increase feature diversity and improve the model’s recognition capability. The Sentinel-2 imagery is used to enhance the spatial information of the Zhuhai-1 HS image, improving the urban ISA retrieval by reducing the impact of noises. By combining the deep spatial features and deep spectral features, we obtain joint spatial-spectral features, leading to high classification accuracy and robustness. We test the proposed method in two highly urbanized study areas that cover Foshan city and Wuhan city, China. The results reveal that the proposed method obtains an overall accuracy of 96.72% and 96.75% in the two study areas, 18.78% and 8.66% higher than classification results with only HS imagery as input. The final ISA extraction overall accuracy is 95.42% and 95.50% in the two study areas, the highest among the comparison methods.

Superresolution (SR) is an important compensatory mean for geostationary orbit (GEO) satellites because of their low spatial resolution caused by orbital height. The multiple-image SR (MISR) methods generally have better reconstruction effects than single-image SR (SISR) methods. However, MISR methods require multiple images of the same place, which are susceptible to geometric distortion and radiation differences. Gaofen-4 (GF-4) is a GEO satellite equipped with a visible-light and near-infrared (VNIR) sensor and provides images with five bands. Based on the similarity of spatial information between different bands, this paper proposes an SISR method suitable for GF-4 VNIR images. The radiation differences are eliminated by extracting texture images. The high-resolution (HR) texture image is reconstructed by projections onto convex sets. The final SR result is fused by HR texture image and upsampled VNIR image. Tested by real GF-4 data, the reconstructed results of this method have better sharpness and more abundant information than comparable methods.