With the rapid development of aerospace technology and the continuous increase of China’s high resolution earth observation data acquisition in the past few decades, the era of remote sensing big data has coming now. Developing multi-satellite integrated data processing and application technology has become an important trend. In this paper, we systematically review the technological development process from two aspects, including multi-satellite imaging processing and multi-element information extraction. Then, we analyze the advantages and characteristics of the existing cutting-edge methods, and points out the main challenge of establishing the integrated processing model for imaging processing field and efficient learning model for information extraction field. On this basis, as well as according to the practical application requirements, a novel method of multi-satellite integrated processing and analysis under remote sensing big data is proposed in this paper. We emphatically define the basic concepts, scientific problems, research ideas and solutions of this method. On one hand, in terms of multi-satellite imaging processing, aiming at the problem of difficult estimation of high-dimensional coupled imaging parameters at high resolution, the various errors caused by the whole cycles of electromagnetic waves are calculated quantitatively, including payload error, platform error, data transmission error, atmospheric influence and so on. Then a multi-satellite integrated imaging processing physical model based on the Generative Adversarial Networks (GAN) is constructed to approximate estimate imaging parameters. In this way, we can achieve high-precision geometric corrections and radiation corrections, as well as generate high-quality remote sensing image products. On the other hand, in terms of multi-element information extraction, to solve the problem that the accuracy of the original tasks is difficult to maintain due to the addition of new tasks and requires full sample retraining, we design a multi-task feature sharing model based on few-shot incremental learning. It has a novel memory retention unit and multi-modal joint optimization of convex non-negative matrix factors. Through this model, we can generate parallel and high-precision annotations for multiple objects. Compared with the existing methods, the information between different satellites and payloads, different missions and objects are complementary to each other, leading to the simultaneous improvement of multi-sensor imaging quality and object extraction accuracy. The specific technical approach and preliminary experimental verification are given in detail in this paper. Experiments on multi-satellite imaging processing show that our method can effectively estimate multiple imaging errors. The phase estimation accuracy is within 1 degree when the signal-to-noise ratio is -5dB, which indicates the good performance even under low signal-to-noise ratio condition. At the same time, experiments on multi-element information extraction show that compared to single-modal method, our method, which uses multi-modal data in combination and embedded the novel memory retention unit, improve the extraction accuracy in multi-tasks of object detection and semantic segmentation. In the future, we will pay more attention to the disconnection problem between multi-satellite imaging processing and multi-element information extraction in the field of remote sensing. By establishing a benign mutual feedback mechanism between these two procedures to maximize the benefits of the remote sensing big data.