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

作　　者：Priyanka Darbari Ankush Agarwal Manoj Kumar Priyanka Darbari;Ankush Agarwal;Manoj Kumar(Computer Engineering and Application, GLA University, Mathura, India;Department of Information Technology, GGV, Bilaspur, India)

机构地区：[1]Computer Engineering and Application, GLA University, Mathura, India [2]Department of Information Technology, GGV, Bilaspur, India

出　　处：《Journal of Computer and Communications》2024年第3期68-83,共16页电脑和通信（英文）

摘　　要：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.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.

关 键 词：Brazilian Amazon Sentinel-1 Band Math Transpose CNN Transformation Network 

分 类 号：TN9[电子电信—信息与通信工程]