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Ortega Adarme, M., Doblas Prieto, J., Queiroz Feitosa, R. and De Almeida, C.A. (2022) Improving Deforestation Detection on Tropical Rainforests Using Sentinel-1 Data and Convolutional Neural Networks. Remote Sensing, 14, Article 3290.
https://doi.org/10.3390/rs14143290

has been cited by the following article:

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

  AUTHORS: Priyanka Darbari, Ankush Agarwal, Manoj Kumar

  KEYWORDS: Brazilian Amazon, Sentinel-1, Band Math, Transpose CNN Transformation Network

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.3, March 15, 2024

  ABSTRACT: 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.