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Deep learning background
| Content Provider | Scilit |
|---|---|
| Author | Cresson, Rémi |
| Copyright Year | 2020 |
| Description | Deep learning is becoming increasingly important to solve a number of image processing tasks. Among common algorithms, Convolutional Neural Network (CNN)- and Recurrent Neural Network (RNN)-based systems achieve state-of-the-art results on satellite and aerial imagery in many applications. CNN achieve state-of-the-art results on images. CNNs are designed to extract features in images, enabling image recognition, object detection, and semantic segmentation. RNN are suited for sequential data analysis, such as speech recognition and action recognition tasks. Pooling, and convolutions with stride, can be viewed as a subsampling process, which does modify the output size, and the output physical spacing. Depending on the implementation, it can also keep partially sampled items at borders. There are challenges remaining ahead to process real-world remote sensing images with deep learning. One crucial point is software implementation. Book Name: Deep Learning for Remote Sensing Images with Open Source Software |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2019-0-09527-7&isbn=9781003020851&doi=10.1201/9781003020851-1&format=pdf |
| Ending Page | 8 |
| Page Count | 6 |
| Starting Page | 3 |
| DOI | 10.1201/9781003020851-1 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-07-15 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Deep Learning for Remote Sensing Images with Open Source Software Remote Sensing Implementation Convolutional |
| Content Type | Text |
| Resource Type | Chapter |
