Since the beginning of the twenty-first century, several countries have made great efforts to develop space remote sensing for building a high-resolution earth observation system. Under the great attention of the government and the guidance of the major scientific and technological project of the high-resolution earth observation system, China has made continuous breakthroughs and progress in high-resolution remote sensing imaging technology. The development of domestic high-resolution remote sensing satellites shows a vigorous trend, and consequently, a relatively stable and perfect high-resolution earth observation system has been formed. The development of high-resolution remote sensing satellites has greatly promoted and enriched modern mapping technologies and methods. In this paper, the de velopment status, along with mapping modes and applications of China’s high-resolution remote sensing satellites are reviewed, and the development trend in high-resolution earth observation system for global and ground control-free mapping is discussed, providing a reference for the subsequent development of high-resolution remote sensing satellites in China.

Object detection in high spatial resolution remote sensing images (HSRIs) is an important part of image information automatic extraction, analysis, and understanding. The region of interest (ROI) scale of object detection and the object feature representation are two vital factors in HSRI object detection. With respect to these two issues, this article presents a novel HSRI object detection method based on convolutional neural networks (CNNs) with suitable object scale features. First, the suitable ROI scale of object detection is obtained by compiling statistics for the scale range of objects in HSRIs. Then, a CNN framework for object detection in HSRIs is designed using a suitable ROI scale of object detection. The object features obtained using a CNN have good universality and robustness. Finally, a CNN framework with a suitable ROI scale of object detection is trained and tested. Using the WHU-RSONE data set, the proposed method is compared with the faster region-based CNN (Faster-RCNN) framework. The experimental results show that the proposed method outperforms the Faster-RCNN framework and provides good object detection results in HSRIs.

On-orbit geometric calibration is a key technology to guarantee the geometric quality of high-resolution optical satellite imagery. In this paper, we present an approach for the on-orbit geometric calibration of high-resolution optical satellite imagery, focusing on two core problems: constructing an on-orbit geometric calibration model and proposing a robust calculation method. First, a rigorous geometric imaging model is constructed based on the analysis of the major error sources. Second, we construct an on-orbit geometric calibration model through performing reasonable optimizing and parameter selection of the rigorous geometric imaging model. On this basis, the calibration parameters are partially calculated with a stepwise iterative method by dividing them into two groups: external and internal calibration parameters. Furthermore, to verify the effectiveness of the proposed calibration model and methodology, on-orbit geometric calibration experiments for ZY1-02C panchromatic camera and ZY-3 three-line array camera are conducted using the reference data of the Songshan calibration test site located in the Henan Province, China. The experimental results demonstrate a certain deviation of the on-orbit calibration result from the initial design values of the calibration parameters. Therefore, on-orbit geometric calibration is necessary for optical satellite imagery. On the other hand, by choosing multiple images, which cover different areas and are acquired at different points in time to verify their geometric accuracy before and after calibration, we find that after on-orbit geometric calibration, the geometric accuracy of these images without ground control points is significantly improved. Additionally, due to the effective elimination of the internal distortion of the camera, greater geometric accuracy was achieved with less ground control points than before calibration.

Remote sensing image change detection is extensively utilized in various applications in the field of remote sensing, particularly in the realm of cropland conservation, where it plays a critical role in protecting the agro-ecosystem and ensuring global food security. However, the progressive improvement in resolution and size of remote sensing imagery has led to a ’scale gap’ challenge in the detection of small building changes in cropland areas. To address this challenge, an innovative multi-scale feature fusion change detection network (M-Swin) based on transformer using hierarchical windows is proposed. In order to obtain clearer edges and better separation of the change results, a novel saimese transformer encoder (MSW encoder) is proposed, which can better capture the change information in small building through hierarchical windows and fuse the multi-scale feature obtained from different windows. To effectively reduce missed and misdetected small-area of changing buildings, a novel bi-temporal image feature fusion module (BFFM) is proposed, which can enhance the features based on a priori guidance, thus improving the saliency of change regions. Additionally, a new remote sensing image change detection dataset for cropland, called LuojiaSET-CLCD, has been proposed. Experimentally demonstrates that M-Swin has good potential for highly accurate change detection of small buildings within cropland areas and outperforms several newly existing methods in three datasets (LEVIR, WHU-CD and LuojiaSET-CLCD). Our dataset will be publicly available at https://github.com/RSIIPAC/LuojiaSET-CLCD.

This paper proposes a new satellite-based framework for global-scale remote sensing that is integrated with on-orbit cloud computing and AI ( artificial intelligence ) services. These spaced-based services cover the entire earth surfaces using massive LEO satellite constellation. Global-scale sensing of earth resources must be supported by massive number of LEO satellites equipped with cloud/AI computing services in real time. New satellite computer architectural features are presented along with some satellite constellation deployment topologies. We design satellite-based computers to support on-orbit remote sensing and AI scene analysis. This demands real-time performance without transmitting the sensed data back to earth for delayed processing. Notable space data services include on-orbit data sensing of large areas, machine learning from earth resources data, earth scene/event analysis, geomorphology observation, smart city management, disaster relief, global healthcare IoT, and environmental ecology protection, etc. We attempt to achieve high-efficiency earth resources utilization along with green energy, low cost, and robustness in real-life services.

With the advent of very-high-resolution remote sensing images, semantic change detection (SCD) based on deep learning has become a research hotspot in recent years. SCD aims to observe the change in the Earth’s land surface and plays a vital role in monitoring the ecological environment, land use and land cover. Existing research mainly focus on single-task semantic change detection; the problem they face is that existing methods are incapable of identifying which change type has occurred in each multi-temporal image. In addition, few methods use the binary change region to help train a deep SCD-based network. Hence, we propose a dual-task semantic change detection network (GCF-SCD-Net) by using the generative change field (GCF) module to locate and segment the change region; what is more, the proposed network is end-to-end trainable. In the meantime, because of the influence of the imbalance label, we propose a separable loss function to alleviate the over-fitting problem. Extensive experiments are conducted in this work to validate the performance of our method. Finally, our work achieves a 69.9% mIoU and 17.9 Sek on the SECOND dataset. Compared with traditional networks, GCF-SCD-Net achieves the best results and promising performances.