Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Cybernetic basis and system practice of remote sensing and spatial information science

Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level

Atmospheric Science and Remote Sensing Laboratory

During the contract year, scientific research on lightning and lightning hazards was carried out for the Atmospheric Electricity Group in the MSFC Remote Sensing Branch (ED43). These tasks included research on modeling the interaction of lightning optical pulses and cloud particles, estimating lightning hazard threats to the STS system, a small field project to determine the charge structure of winter and stratiform thunderstorms, and analysis of optical pulse data. These activities were performed in conjunction with the ED43 mission to develop a lightning mapper to be placed on one of the GOES-next operational satellites

Remote Sensing Information Sciences Research Group, Santa Barbara Information Sciences Research Group, year 3

Research continues to focus on improving the type, quantity, and quality of information which can be derived from remotely sensed data. The focus is on remote sensing and application for the Earth Observing System (Eos) and Space Station, including associated polar and co-orbiting platforms. The remote sensing research activities are being expanded, integrated, and extended into the areas of global science, georeferenced information systems, machine assissted information extraction from image data, and artificial intelligence. The accomplishments in these areas are examined

Problems in merging Earth sensing satellite data sets

Satellite remote sensing systems provide a tremendous source of data flow to the Earth science community. These systems provide scientists with data of types and on a scale previously unattainable. Looking forward to the capabilities of Space Station and the Earth Observing System (EOS), the full realization of the potential of satellite remote sensing will be handicapped by inadequate information systems. There is a growing emphasis in Earth science research to ask questions which are multidisciplinary in nature and global in scale. Many of these research projects emphasize the interactions of the land surface, the atmosphere, and the oceans through various physical mechanisms. Conducting this research requires large and complex data sets and teams of multidisciplinary scientists, often working at remote locations. A review of the problems of merging these large volumes of data into spatially referenced and manageable data sets is presented

Remote Sensing in Agriculture

Remote sensing is defined as the art and science of gathering information about objects or areas from a distance without having physical contact with objects/areas being investigated. Remote sensing is the science and technology of making inferences about material objects from measurement made at a distance without coming into physical contact with the object under study. Remote sensing is a tool to monitor the earth's resources using space technology in addition to ground observations. Remote sensing is the science and technology of making inferences about material objects from measurement made at a distance without coming into physical contact with the object under study. Spectral signature of any object that detect by remote sensing is the main principle. Remote sensing technology uses the visible, infrared and microwave regions of radiation to collect information about the various objects on the earth surface. The responses of the objects of different regions of the electromagnetic spectrum are different. The typical responses are used to distinguish object such as vegetation, water, bare soil, concert and other similar features. Remote sensing is two types viz, active and passive remote sensing. Passive remote sensing: It makes use of seasons that detects the reflected/emitted electromagnetic radiation natural sources. Active remote sensing: It makes the use of seasons that detects reflected responses from object that are irradiated from artificially generated energy sources, such as radar. There are three types of platforms-air based, ground based and satellite based. The various applications of remote sensing in agriculture are- crop condition monitoring, detection of plant stress, vegetative indices, canopy transmission and crop stress, cropping system analysis, application on forestry, drought monitoring and its assessment, flood mapping and its assessment, ground water exploration, storm and flood warning, water availability and location of canals, wildlife inventory and fire surveillance etc

The Penn State ORSER system for processing and analyzing ERTS and other MSS data

The author has identified the following significant results. The office for Remote Sensing of Earth Resources (ORSER) of the Space Science and Engineering Laboratory at the Pennsylvania State University has developed an extensive operational system for processing and analyzing ERTS-1 and similar multispectral data. The ORSER system was developed for use by a wide variety of researchers working in remote sensing. Both photointerpretive techniques and automatic computer processing methods have been developed and used, separately and in a combined approach. A remote Job Entry system permits use of an IBM 370/168 computer from any compatible remote terminal, including equipment tied in by long distance telephone connections. An elementary cost analysis has been prepared for the processing of ERTS data

NASA Laser Remote Sensing Technology Needs for Earth Science in the Next Decade and Beyond

In late 2005 the NASA Earth Science Technology Office convened a working group to review decadal-term technology needs for Earth science active optical remote sensing objectives. The outcome from this effort is intended to guide future NASA investments in laser remote sensing technologies. This paper summarizes the working group findings and places them in context with the conclusions of the National Research Council assessment of Earth science needs, completed in 2007