SiCTT.net: Satellite Image Clustering Transpose Transformation Deep neural network for Mixed Pixel Deforestation Analysis in Optical Satellite for Land Use Land Cover Application

Priyanka Darbari, Ankush Agarwal, Manoj Kumar

Deforestation monitoring in Brazil for Land use land cover application is the combination of up-to-date monitoring and accuracy. For detailed observation on time, need a Sentinel-2 multi-spectral satellite imagery which is a combination of multiple bands of different frequency for better analysis. Sentinel-2 Multispectral Images are the combination of high resolution and low-resolution images which create problem for classifying the object due to mixed pixel problem. Due to mixed pixel problem in optical satellite detection of the object is difficult which effects the accuracy. To identify the mixed pixel problem to combine multiple bands using Band Math to create a new band for detecting mixed pixel and to analyse the pixel using segmentation and clustering. To classify the Brazil Amazon Forest deforestation between 2019 and 2023 proposing a Satellite Image clustering Transpose Transformation Deep Neural Networking (SiCTT.net). To compare the CNN and Transpose CNN transformation with the help of accuracy and based on the results, proposed network gives better accuracy and helps to detect mixed pixel problem.