Saurabh Prasad – författare

Advances in Machine Learning and Image Analysis for GeoAI presents recent advances in applications and algorithms that are at the intersection of Geospatial imaging and Artificial Intelligence (GeoAI). The book covers algorithmic advances in geospatial image analysis, sensor fusion across modalities, few-shot open-set recognition, explainable AI for Earth Observations, self-supervised learning, image superresolution, Visual Question Answering, and spectral unmixing, among other topics. This book offers a comprehensive resource for graduate students, researchers, and practitioners in the area of geospatial image analysis. It provides detailed descriptions of the latest techniques, best practices, and insights essential for implementing deep learning strategies in GeoAI research and applications.

Covers the latest machine learning and signal processing techniques that can effectively leverage multimodal geospatial imagery at scaleChapters cover a variety of algorithmic frameworks pertaining to GeoAI, including superresolution, self-supervised learning, data fusion, explainable AI, among othersPresents cutting-edge deep learning architectures optimized for a wide array of geospatial imagery

This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding.

This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding.

Optical remote sensing relies on exploiting multispectral and hyper spectral imagery possessing high spatial and spectral resolutions respectively. These modalities, although useful for most remote sensing tasks, often present challenges that must be addressed for their effective exploitation. This book presents current state-of-the-art algorithms that address the following key challenges encountered in representation and analysis of such optical remotely sensed data.Challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, pattern classification and target recognition, visualization of high dimensional imagery.

Optical remote sensing relies on exploiting multispectral and hyper spectral imagery possessing high spatial and spectral resolutions respectively. These modalities, although useful for most remote sensing tasks, often present challenges that must be addressed for their effective exploitation. This book presents current state-of-the-art algorithms that address the following key challenges encountered in representation and analysis of such optical remotely sensed data.Challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, pattern classification and target recognition, visualization of high dimensional imagery.