Aerospace science and technology (S&T) is a direct indicator of comprehensive national power and S&T strength. Satellite remote sensing is one of the most immediate and realistic productivities transformed from aerospace S&T

which is composed of two broad procedures:remote sensing data acquisition and dissemination and data processing and information extraction. On the one hand

given the steady promotion of China's Civil Space Infrastructure

the capacity for satellite image acquisition has been enhanced largely in terms of quality and quantity. On the other hand

an image processing platform is a considerable infrastructure for satellite remote sensing application. Platform development is increasingly becoming an important factor restricting the application of satellite remote sensing and the development of spatial information-related business. This paper reviews state-of-the-art status and analyzes the future trend of acquisition capacity and processing platform of satellite remote sensing image.In terms of data acquisition and dissemination

international open remote sensing satellites and sensors

such as Terro/Aqua-MODIS

Landsat

and Sentinel

have largely broadened and deepened applications of satellite remote sensing imagery. Data sharing policy and regularized senior image product enable the use of these commonweal data to analyze a long-term geographical phenomenon in a large region. Large commercial satellites

such as WorldView

Pleiades

and Radarsat

are operated by sizeable commercial remote sensing firms. The imagery obtained by big satellites helps promote the commercial value of satellite imagery in traditional industry applications

whereas small commercial satellite constellations

such as Flock

SkySat

and BlackSky

lower the barrier for more generalized and common applications. With the steady promotion of China's Civil Space Infrastructure

China's 27 civil remote sensing satellites in orbit can be broadly categorized into land

ocean

and atmosphere observation satellites. Land observation satellites are composed of GaoFen

HuanJing

and ZiYuan series

and sensors onboard can acquire high-resolution visible-near infrared

hyperspectral

thermal

and synthetic aperture radar (SAR) imagery. Over 30 small commercial satellites in orbit include BeiJing

GaoJing

JiLin

and ZhuHai series. Despite the substantial progress of China's capacity for remote sensing data acquisition and dissemination

distinct generation gaps remain

especially in terms of new type sensors such as polarization and electromagnetic monitoring sensors. Policy barrier

data regularity

and quality as well as data sharing model in big data era are the main challenges encountered in data sharing. Several efforts

including project on big earth data science engineering

have been made to accelerate the development of satellite remote sensing data sharing.In terms of satellite remote sensing processing platforms

well-known platforms such as ERDAS IMAGINE

ENVI

and PCI Geomatic lead the development worldwide. The leadership can be characterized by four advantages. First

platform expandability is reflected by flexible deployment environment

powerful secondary development capability

and seamless integration with GIS platforms. Second

these platforms support the processing of multi-source and multi-format remote sensing data. Multi-source data refer to optical

SAR

LiDAR

and hyperspectral data

while multi-format data refer to image

point cloud

and video data. Third

ERDAS IMAGINE and ENVI start to develop modules based on deep learning algorithms

such as Faster RCNN. The introduction of deep learning is based on its overwhelming accuracy compared with traditional machine learning algorithms

such as SVM. Finally

algorithms and hardware

such as GPU

DSP

and FPGA

are integrated more tightly to continue promoting data processing efficiency. In China

common platforms such as IRSA

ImageInfo

Titan Image

and PIE pay more attention to satellite optical imagery processing despite limited support for multi-source data. Specialized software

including HypEYE and CAESAR

are developed to fulfil the demands of hyperspectral and SAR image processing. In the last 10 years

cloud computing technology has been introduced into remote sensing imagery processing platforms because of its advantage of providing one-step geospatial service by integrating remote sensing data

information product

application software

and computing and storage resources. Google Earth Engine

Data Cube

ENVI Service Engine

and ERDAS APOLLO are some of the successful platforms

and several similar platforms are available in China. China's self-developed remote sensing imagery processing platforms are not competitive with their international counterparts owing to backwardness caused by a lack of independent innovation and steady profit model.Four evident trends are observed in satellite remote sensing in the era of big data and artificial intelligence. First

small satellite constellations accelerate the industrialization and popularization of satellite remote sensing

while the improvement of geometric and radial accuracy remains the bottleneck. Second

autonomous and intelligent satellites with capabilities of adaptive optimization of imaging parameters

onboard thorough perception of object

and environment are future directions of remote sensing. The intelligent satellite will be an essential component of collaborative unmanned systems. Third

the transformation from meaningless DN value to semantic object information based on artificial intelligence techniques will certainly improve the information provided by satellite remote sensing in terms of quantity and quality. Finally

the integration of position and navigation

time

remote sensing

and communication platforms and signals will magnify remote sensing capability by providing application-oriented solutions.