![]() With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL. Namely, we focus on theories, tools, and challenges for the RS community. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research.We also review recent new developments in the DL field that can be used in DL for RS. ![]() ![]() This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove that this approach can accomplish an average accuracy higher than 97% on the classification of targets in ten categories, which is higher than the traditional CNN results. Secondly, CNN architecture which consist of a feature extraction stage followed by a classification step using a softmax classifier. To correct these errors and extract better features about SAR targets, and obtain better accuracies a two steps algorithm called SAE-CNN-Recognizer(SCR) is proposed: Firstly, a pre-processing step consist of image enhancement is achieved using Sparse Auto-Encoder (SAE) to emphasize some image features for following analysis. Despite these utilities, several factors can affect the accuracy of the classification, such as errors linked with brightness values of the pixels and geometry registered by the satellite sensors. Moreover, unlike optical images, SAR imaging have the advantages of reduced sensitivity to weather conditions, day-night operation, penetration capability through obstacles, etc. Through learning the hierarchy of features automatically from massive training data, learning networks, such as Convolutional Neural Networks (CNN) has recently achieved the state-of-the-art results in many tasks. ![]() This article discusses the issue of automatic target recognition (ATR) on Synthetic Aperture Radar images (SAR). ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |