MsFireD-Net: A lightweight and efficient convolutional neural network for flame and smoke segmentation

Hossain, F.M. Anim; Zhang, Youmin

doi:10.1016/j.jai.2023.08.003

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…The ongoing research on deforestation, RDSTC-Reversed Depth wise Separable Transposed Convolution faces problem of segmenting and classifying mixed pixel and unable to identify early stage of fire detection [14].…”

Section: Introductionmentioning

confidence: 99%

SIT.net: SAR Deforestation Classification of Amazon Forest for Land Use Land Cover Application

Darbari,

Agarwal,

Kumar

2024

JCC

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The process of turning forest area into land is known as deforestation or forest degradation. Reforestation as a fraction of deforestation is extremely low. For improved qualitative and quantitative classification, we used Sentinel-1 dataset of State of Para, Brazil to precisely and closely monitor deforestation between June 2019 and June 2023. This research aimed to find out suitable model for classification called Satellite Imaging analysis by Transpose deep neural transformation network (SIT-net) using mathematical model based on Band math approach to classify deforestation applying transpose deep neural network. The main advantage of proposed model is easy to handle SAR images. The study concludes that SAR satellite gives high-resolution images to improve deforestation monitoring and proposed model takes less computational time compared to other techniques.

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Section: Introductionmentioning

confidence: 99%