Earth observation brain (EOB): an intelligent earth observation system

Li, Deren; Wang, Mi; Dong, Zhipeng; Shen, Xin; Shi, Lite

doi:10.1080/10095020.2017.1329314

Cited by 42 publications

(15 citation statements)

References 18 publications

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“…Remote sensing images are a valuable source of data that can be utilizable to determine and visualize detailed information on the Earth's cover. The exponential increase of remote sensing images is due to improvements in satellite and sensor technologies [20] [21], and this has prompted the need for intelligent earth observations [22] [23]. The corresponding effects are improvements in remote sensing images quality spatial resolutions because of the sensor technologies advances.…”

Section: A Remote Sensing Image Scene Classificationmentioning

confidence: 99%

Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification

Tombe

Viriri

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing scene classification has numerous applications on land cover land use. However, classifying the scene images into their correct categories is a challenging task. This challenge is attributable to the diverse semantics of remote sensing images. This nature of remote sensing images makes the task of effective feature extraction and Learning Complex. Effective image-feature-representation is essential in image analysis and interpretation for accurate scene image classification with machine learning algorithms. The recent literature shows that convolutional neural networks are mighty in feature extraction for remote sensing scene classification. Additionally, recent literature shows that classifier-fusion attain superior results than individual classifiers. This paper proposes the adaptive deep coaccordance feature learning (ADCFL). The ADCFL method utilizes a convolutional neural network to extracts spatial feature information from an image in a co-occurrence manner with filters, and then this information is fed to the multigrain forest for feature learning and classification through majority votes with ensemble classifiers. An evaluation of the effectiveness of ADCFL is conducted on the public datasets Resisc45 and Ucmerced. The classification accuracy results attained by the ADCFL demonstrate that the proposed method achieves improved results.

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Section: A Remote Sensing Image Scene Classificationmentioning

confidence: 99%

Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification

Tombe

Viriri

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In this procedure, background information from NWP is used. For on-board processing, the renewal of NWP information can be achieved in a future up-link routine [29,30]; for the simplicity of algorithms, in this research, circular medium filtering without NWP was used in wind direction inversion. The accuracy of this processing chain is evaluated in the following experiment section.…”

Section: On-board Wind Inversionmentioning

confidence: 99%

On-Board Wind Scatterometry

Dong

Xie

2020

Remote Sensing

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Real-time (RT) ocean surface wind can make key improvements to disaster alarmingand safety of maritime navigation to avoid loss in property and human lives. Wind scatterometry is a well-acquainted way of obtaining good quality ocean surface winds, and it has been in application for decades. Existing wind-obtaining chains employ ground stations for receiving observations and can, at best, provide products in around 30 minutes for limited regions. In recent years, a satellite information-obtaining and transmission network is the new trend of Earth observation. In this research, on-board wind retrieval environment and procedures, which are different from traditional wind-obtaining chains, are proposed. First, the establishment of the on-board environment is instructed. Structures of each module are provided. The ground simulation system is been established based on this. After that, existing observing and processing routines of wind scatterometry are described, and then an on-board processing chain proposed and described. Modifications to existing satellite-ground chains are highlighted. The proposed method is validated in Level 0 data from the Chinese–French Oceanic SATellite (CFOSAT). Experiments indicate that the proposed on-board processing procedure can provide comparable results to ground-processed wind products. The root-mean-square error (RMSE) of wind speed for a track of data used in the experiment was about 0.26 m/s, and it was about 0.8° for wind direction. By decreasing wind field result quality, calculation time can be lessened in the on-board environment. However, it is found that in the whole chain of on-board wind generation, the most time-consuming procedure is observation-obtaining. The proposed on-board processing method can achieve good wind accuracy while meeting RT applications with good processing time. This provides a good complement to existing on-board-observing-ground-processing chains for RT applications.

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“…Due to technological advancements in the last decade, the technique of on-board processing of satellite data [ 21 , 29 , 30 , 31 ] and information extraction is becoming a research hotspot. Researchers are looking into the feasibility of enhanced hardware-software blending for meeting end application needs.…”

Section: Introductionmentioning

confidence: 99%

On-Board Ship Detection for Medium Resolution Optical Sensors

Ghosh

Konugurthi

Singupurapu

et al. 2021

Sensors

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In recent years there has been an increased interest in ocean surveillance. The activity includes control and monitoring of illegal fisheries, manmade ocean pollution and illegal sea traffic surveillance, etc. The key problem is how to identify ships and ship-like objects accurately and in a timely manner. In this context, currently, many solutions have been proposed based on high resolution optical and radar remote sensing systems. Most often, these systems suffer from two major limitations viz., limited swath, thereby requiring multiple satellites to cover the region of interest and huge volumes of data being transmitted to ground, even though effective per-pixel information content is minimal. Another limitation is that the existing systems are either simulated on ground or built using the non-space qualified/Commercial Of-The-Shelf (COTS) components. This paper proposes an efficient on-board ship detection system/package connected with medium resolution wide swath optical camera. The methodology adopted has three major components, viz., onboard data processing for improving the radiometric fidelity, followed by a ship detection using modified Constant False Alarm Rate algorithm (CFAR) and a false alarm suppression module to mask false identifications. Finally, the package outputs only the locations of the ships, which is transmitted to the ground. The proposed system reduces the effective volume of data to be transmitted and processed on ground and also significantly cuts down the turnaround time for achieving the end objective. The system is built on radiation hardened Field Programmable Gate Array (FPGA) devices to meet the various engineering constraints such as real-time performance, limited onboard power, radiation hardness, handling of multiple custom interfaces etc. The system is tested with one of the medium resolution Multispectral Visual and Near Infra-Red (MX-VNIR) sensor having a spatial resolution of around 50 m and swath of around 500 Kms, which would be flown with one of the upcoming satellites. The systems performance is also verified on ground with Indian Remote Sensing (IRS) Satellite’s Resourcesat’s Advanced Wide Field Sensor (AWiFS) data and the results are found to be quite encouraging as well as meeting the mission objectives.

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Earth observation brain (EOB): an intelligent earth observation system

Cited by 42 publications

References 18 publications

Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification

Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification

On-Board Wind Scatterometry

On-Board Ship Detection for Medium Resolution Optical Sensors

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