Multisource Region Attention Network for Fine-Grained Object Recognition in Remote Sensing Imagery (original) (raw)

Remote sensing scene classification using an attention consistent network

International journal of health sciences

Image scene classification in the remotely sensed (RS) society is an interesting subject that aims to allocate land use / cover semantic information. A big amount of Convolutional neural classification models for RS images have been proposed by the authors due to the massive behaviour of CNNs in features extracted. Despite their impressive results, there are still opportunities for advancement. To begin, local characteristics are just as important as global ones in identifying RS images. The CNNs' hierarchical organizational structure and multidimensional suitable capabilities make them good at trying to capture spatial information [1]. It's not uncommon for the feature maps to be overlooked, moreover. First and foremost, the ranges among RS image pairs must be minimised or maximised in order to achieve satisfying classifier performance. Despite this, the importance of these focuses in classification tasks is undervalued. We propose a new CNN called focus high quality infras...

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Remote Sensing

Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed ...