Hyperspectral Remote Sensing Data Recovery via Adaptive Window Matching Method

Zeng, Yiliang; Lan, Jinhui; Han, Chuanzhao; Jiang, L.; Shi, Xiuyong

doi:10.3991/ijoe.v11i1.4235

Cited by 1 publication

(1 citation statement)

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“…Dropout empirically proven to be more efficient. Dropout regularization technique was used to dropout hidden units in the neural network [9]. The probability of retention was set as 0.5 for the hidden layers which seems to work well for a wide range of networks and applications.…”

Section: Dropout For Deep Neural Networkmentioning

confidence: 99%

Cloud Cover Avoidance in Satellite Systems

Narayanan¹,

Jagtap²,

Fonseca³

et al. 2020

Int. J. Onl. Eng.

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<span id="docs-internal-guid-41420025-7fff-e714-8d51-73d40b2b7340"><span>Cloud cover is primarily a major difficulty in the acquisition of optical satellite images and has a negative impact on the efficiency of data scheduling. Along with data scheduling, the computational power required is also increasing. Recent advances in an extensive variety of technologies have resulted in an explosion in the amount of data. Different methodologies have been used for Object detection in remote sensing images but it remains a challenge because of its diversity and complex backgrounds. In this paper, a cloud cover detection technique based on Convolutional Neural Networks is proposed for remote sensing images. The classifying model uses a neural network where the underlying features are used to classify the image as useful or not. Results illustrate that the proposed method outperforms other state of the art methods that exist. Once classified, it will be transmitted from the satellite to the earth giving the researchers only convenient pictures to study. This will help to save a massive amount of computation, expense and time.</span></span>

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Section: Dropout For Deep Neural Networkmentioning

confidence: 99%