Earth Observation Mission of a 6U CubeSat with a 5-Meter Resolution for Wildfire Image Classification Using Convolution Neural Network Approach

Azami, M. H.; Örger, Necmi Cihan; Schulz, Victor Hugo; Oshiro, Takashi; Cho, Mengu

doi:10.3390/rs14081874

Cited by 20 publications

(13 citation statements)

References 41 publications

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“…The KITSUNE uses a Sony IMX342 color sensor that generates the images fed into the DL model for classification. Using this setup, the group was able to achieve classification accuracy of above 95% using networks like ShallowNet and LeNet [96,97]. Another recent example is Orora Technologies, which specifically designs 3U CubeSats furnished with IR cameras for finding and monitoring wildfires from space [98].…”

Section: Satellite Surveillancementioning

confidence: 99%

Early Wildfire Detection Technologies in Practice—A Review

Mohapatra

Trinh

2022

Sustainability

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As fires grow in intensity and frequency each year, so has the resistance from their anthropic victims in the form of firefighting technology and research. Although it is impossible to completely prevent wildfires, the potential devastation can be minimized if fires are detected and precisely geolocated while still in their nascent phases. Furthermore, automated approaches without human involvement are comparatively more efficient, accurate and capable of monitoring extremely remote and vast areas. With this specific intention, many research groups have proposed numerous approaches in the last several years, which can be grouped broadly into these four distinct categories: sensor nodes, unmanned aerial vehicles, camera networks and satellite surveillance. This review paper discusses notable advancements and trends in these categories, with subsequent shortcomings and challenges. We also describe a technical overview of common prototypes and several analysis models used to diagnose a fire from the raw input data. By writing this paper, we hoped to create a synopsis of the current state of technology in this emergent research area and provide a reference for further developments to other interested researchers.

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Section: Satellite Surveillancementioning

confidence: 99%

Early Wildfire Detection Technologies in Practice—A Review

Mohapatra

Trinh

2022

Sustainability

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“…In this section, a small sample is studied as an active subject. It is motivated to classify it into an image representation phase, a data expansion phase, and a learning phase, and the schematic diagram of the smallsample learning algorithm is shown in Figure 3 [20]. e motivation of representation learning is to transform the raw data into feature domains through representation learning.…”

Section: Algorithm Flowmentioning

confidence: 99%

Image Classification Learning Method Incorporating Zero-Sample Learning and Small-Sample Learning

Sun

Diao

2022

Mathematical Problems in Engineering

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At present, artificial intelligence algorithms based on deep learning have achieved good results in image classification, biometric recognition, medical diagnosis, and other fields. However, in practice, many times researchers are unable to obtain a large number of samples due to many limitations or high sampling costs. Therefore, image sorting zero-sampling order research algorithms have become the central engine of intelligent processing and a hot spot for current research. Because of the need for the development of deep learning prediction capability, coupled with the emergence of time and technical-level drawbacks, the advantages of zero-sample and small-sample are gradually emerging, so this paper chooses to fuse the learning methods of both for image recognition research. This paper mainly introduces the current situation of zero-sample and small-sample learning and summarizes the learning of zero-sample and small-sample. And the meaning of zero-sample learning and small-sample learning and the classification of the main learning methods are introduced and compared and outlined, respectively. Finally, the methods of zero-sample and small-sample learning are fused, the design is introduced and analyzed, and the future research directions are prospected according to the current research problems.

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“…Nanosatellites have a wide range of applications, from interplanetary missions to space observations and communications [5][6][7]. A special place is occupied by remote sensing of Earth (ERS) [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A High Gain Deployable L/S Band Conical Helix Antenna Integrated With Optical System for Earth Observation CubeSats

et al. 2023

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In this study, a new spiral dual-band antenna integrated with an optical system for a small CubeSat spacecraft was proposed. It is challenging to design an antenna with a limited space on a CubeSat platform. To improve the space utilization, an antenna was designed based on the concept of not occupying the sides of the CubeSat. A feature of the proposed antenna system is its compactness, dual-band operation in the L-and S-bands, and possibility of integration with spacecraft cameras. As a result of the study, reflection coefficients of -18 dB and -23 dB and gain of 6.8 dBi and 7.4 dBi, respectively, were achieved at resonant frequencies of 1.7 GHz and 2.45 GHz. The conical shape of the spiral antenna allows the optical system to increase the coverage area, and a simple deployment system with feedback ensures the mission safety. These properties make the proposed antenna suitable for CubeSat systems.INDEX TERMS CubeSat antenna, dual-band antenna, Earth observation, helix antenna

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Earth Observation Mission of a 6U CubeSat with a 5-Meter Resolution for Wildfire Image Classification Using Convolution Neural Network Approach

Cited by 20 publications

References 41 publications

Early Wildfire Detection Technologies in Practice—A Review

Early Wildfire Detection Technologies in Practice—A Review

Image Classification Learning Method Incorporating Zero-Sample Learning and Small-Sample Learning

A High Gain Deployable L/S Band Conical Helix Antenna Integrated With Optical System for Earth Observation CubeSats

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