Optimization of Artificial Neural Networks in Satellite Remote Sensing Data Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Optimization of Artificial Neural Networks in Satellite Remote Sensing Data Analysis write by Tiegeng Ren. This book was released on 2003. Optimization of Artificial Neural Networks in Satellite Remote Sensing Data Analysis available in PDF, EPUB and Kindle.
Neurocomputation in Remote Sensing Data Analysis
Neurocomputation in Remote Sensing Data Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neurocomputation in Remote Sensing Data Analysis write by Ioannis Kanellopoulos. This book was released on 2012-12-06. Neurocomputation in Remote Sensing Data Analysis available in PDF, EPUB and Kindle. A state-of-the-art view of recent developments in the use of artificial neural networks for analysing remotely sensed satellite data. Neural networks, as a new form of computational paradigm, appear well suited to many of the tasks involved in this image analysis. This book demonstrates a wide range of uses of neural networks for remote sensing applications and reports the views of a large number of European experts brought together as part of a concerted action supported by the European Commission.
Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Artificial Neural Networks and Evolutionary Computation in Remote Sensing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Artificial Neural Networks and Evolutionary Computation in Remote Sensing write by Taskin Kavzoglu. This book was released on 2021-01-19. Artificial Neural Networks and Evolutionary Computation in Remote Sensing available in PDF, EPUB and Kindle. Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.
Spatially Explicit Hyperparameter Optimization for Neural Networks
Spatially Explicit Hyperparameter Optimization for Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Spatially Explicit Hyperparameter Optimization for Neural Networks write by Minrui Zheng. This book was released on 2021-10-18. Spatially Explicit Hyperparameter Optimization for Neural Networks available in PDF, EPUB and Kindle. Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is written for researchers of the GIScience field as well as social science subjects.
Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Artificial Neural Networks and Evolutionary Computation in Remote Sensing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Artificial Neural Networks and Evolutionary Computation in Remote Sensing write by Taskin Kavzoglu. This book was released on 2021. Artificial Neural Networks and Evolutionary Computation in Remote Sensing available in PDF, EPUB and Kindle. Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.
