IISER MOHALI

Abstract: This talk will be a brief compilation of different research publications/works which presentsa diverse array of innovative methodologies and applications in the fields of remote sensing dataanalysis for environmental monitoring and agriculture. Notable contributions include a novel MutualInformation-based Segmented Stacked Autoencoder approach for hyperspectral data classification, acomprehensive evaluation of Feature Selection and Feature Extraction Techniques on Multi-TemporalLandsat-8 Images for enhanced crop classification accuracy, and the introduction of a PartialInformational Correlation Based Band Selection strategy for improved hyperspectral imageclassification. Additionally, studies on predicting canopy-averaged chlorophyll content usingConvolutional Auto-Encoder, an evaluation framework for Landsat 8-based Actual EvapotranspirationEstimates in data-sparse catchments, and the transformation of multispectral data to quasi-hyperspectraldata using Convolutional Neural Network Regression offer valuable insights into advancing utilizationof machine/deep learning algorithms for agricultural/remote sensing applications. The compilation alsodelves into topics such as the spatio-temporal dependency of vegetation dynamics on climatic variables,flood concepts, and challenges, alongside an unsupervised annual change detection methodology fromoptical-SAR fused satellite image time-series using 3D-CAE, collectively contributing to the forefrontof research in these domains. Current problem statement also deals with monitoring of regenerativeagricultural practices using remote sensing data.\n\nMeeting ID: 944 2649 3601\nPasscode: 070134\n