Towards AI Large Model by Pengming Feng (.ePUB)+
File Size: 115.1 MB
Towards AI Large Model: Remote Sensing Image Intelligent Interpretation and Application by Pengming Feng, Yuanwei Chen, Haiyan Lan, Guangjun He, Yang Li, Jian Guan
Requirements: .ePUB, .PDF reader, 115.1 MB | True PDF, True EPUB
Overview: This book starts from the development status of remote sensing image intelligent interpretation and application technology. It systematically introduces the main progress of this field and its application, focusing on remote sensing image intelligent quality improvement, intelligent expansion and sample augmentation, object detection, fine-grained target recognition, semantic segmentation, multimodal remote sensing image joint intelligent interpretation as well as intelligent interpretation and application platform. With the rapid development of large models and Artificial Intelligence (AI) technology, and supported by major projects such as the National Science and Technology Major Project of China High-resolution Earth Observation System, the authors and their team have made a series of research achievements in the field of intelligent interpretation and application technology of remote sensing images. Deep Learning encompasses a diverse array of algorithms and frameworks, including convolutional neural networks (CNNs). CNNs have undeniably emerged as one of the most influential forces in image processing. In the fundamental structure of Artificial Neural Networks (ANNs), each node in a layer is fully connected to every node in the preceding layer. This characteristic has led to ANNs also being referred to as Fully Connected Neural Networks (FCNNs). However, this full connectivity can significantly increase the number of parameters within a deep network’s structure (deep networks are generally defined as ANNs with more than one hidden layer). Fortunately, convolutional neural networks address this challenge through local connections, weight sharing, and pooling operations. These characteristics effectively reduce network model complexity and the total number of weights, thereby mitigating issues like parameter explosion and overfitting. A typical CNN architecture can be broadly divided into five components: the input layer, convolutional layer(s), pooling layer(s), fully connected layer, and output layer.
Genre: Non-Fiction > Tech & Devices
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