aj1365/DeepCNN_Polsar
This code is for the paper "PolSAR Image Classification based on Deep Convolutional Neural Networks and Wavelet Transformation" that is published in the IEEE Geoscience and Remote Sensing Letters journal.
This project helps remote sensing scientists classify different types of land cover from Polarimetric Synthetic Aperture Radar (PolSAR) images. By applying advanced deep learning techniques, it takes raw PolSAR imagery as input and outputs a classified map, delineating various land features like forests, water bodies, or urban areas. It is designed for researchers and analysts working with satellite imagery for environmental monitoring or geographical mapping.
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Use this if you need to accurately classify land cover types from PolSAR satellite images for research or mapping purposes.
Not ideal if your primary data source is optical imagery or if you require real-time processing for dynamic environmental changes.
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Apache-2.0
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Aug 29, 2022
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