Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields

Abstract

Agricultural field identification is still a difficult issue because of the poor resolution of satellite imagery. Monitoring remote harvest and determining the condition of farmlands rely on the digital approach agricultural applications. Therefore, high-resolution photographs have obtained much more attention since they are more efficient in detecting land cover components. In contrast, because of low-resolution repositories of past satellite images used for time series analysis, wavelet decomposition filter-based analysis, free availability, and economic concerns, low-resolution images are still essential. Using low-resolution Synthetic Aperture Radar (SAR) satellite photos, this study proposes a GAN strategy for locating agricultural regions and determining the crop's cultivation state, linked to the initial or harvesting time. An object detector is used in the preprocessing step of training, followed by a transformation technique for extracting feature information and then the GAN strategy for classifying the crop segmented picture. After testing, the suggested algorithm is applied to the database's SAR images, which are further processed and categorized based on the training results. Using this information, the density between the crops is calculated. After zooming in on SAR photos, the crop condition may be categorized based on crop density and crop distance. The Euclidean distance formula is used to calculate the distance. Finally, the findings are compared to other existing approaches to determine the proposed technique's performance using reliable measures.