… Whilst our results have not covered all aspects of neural network image classiŪcation in
remote sensing, they do point to a number of fruitful strategies and implementation techniques …

… On the basis of the Convolutional neural network, … image classification. We also study the
composition of the different Convolutional neural network on the result of image classification. …

… Convolutional neural networks (… neural network renaissance that has seen rapid progression
since 2012. In this review, which focuses on the application of CNNs to image classification …

… classification [9]. In this work, we propose a customized CNN network for lung image patch
classification… ], [4], we designed a fully automatic neural-based machine learning framework to …

… classification accuracy. In this review, which focuses on the application of CNNs to image
classification … recent state-of-the-art (SOAT) network architectures. Along the way, we analyze (1…

… Since artificial neural networks have shown a performance … scene classification, specially
convolutional neural networks (CNN)[4] [5] [6], this work focuses on identifying the best network …

… The 300 images for neural network training, and the 300 images for test on the trained …
classification results for more various images through constructing a hierarchical neural network. …

… image-based intelligent data acquisition and Processing has a very important role and impact.
The use of image … goods), image classification and subjective image quality assessment …

… Given an initial large set of neural networks, our approach is … classification of multisensor
remote-sensing images show that this approach allows one to design effective neural network …

… neural network designs for two decades. Unfortunately, these methods cannot scale well
to the modern deep neural networks … tional neural network to address image classification …