Journal ArticleDOI
Learning Multiscale Deep Features for High-Resolution Satellite Image Scene Classification
TLDR
A multiscale deep feature learning method for high-resolution satellite image scene classification by warp the original satellite image into multiple different scales and developing a multiple kernel learning method to automatically learn the optimal combination of such features.Abstract:
In this paper, we propose a multiscale deep feature learning method for high-resolution satellite image scene classification. Specifically, we first warp the original satellite image into multiple different scales. The images in each scale are employed to train a deep convolutional neural network (DCNN). However, simultaneously training multiple DCNNs is time-consuming. To address this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since different SPP-nets have the same number of parameters, which share the identical initial values, and only fine-tuning the parameters in fully connected layers ensures the effectiveness of each network, thereby greatly accelerating the training process. Then, the multiscale satellite images are fed into their corresponding SPP-nets, respectively, to extract multiscale deep features. Finally, a multiple kernel learning method is developed to automatically learn the optimal combination of such features. Experiments on two difficult data sets show that the proposed method achieves favorable performance compared with other state-of-the-art methods.read more
Citations
More filters
Journal ArticleDOI
Remote Sensing Image Fusion With Deep Convolutional Neural Network
Zhenfeng Shao,Jiajun Cai +1 more
TL;DR: Based on the deep convolutional neural network, a remote sensing image fusion method that can adequately extract spectral and spatial features from source images is proposed and provides better results compared with other classical methods.
Journal ArticleDOI
Change detection techniques for remote sensing applications: a survey
Anju Asokan,J. Anitha +1 more
TL;DR: This study attempts to provide a comprehensive review of the fundamental processes required for change detection with a brief account of the main techniques of change detection and discusses the need for development of enhanced change detection methods.
Journal ArticleDOI
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
TL;DR: A novel object detection network [(context-aware detection network (CAD-Net)] that exploits attention-modulated features as well as global and local contexts to address the new challenges in detecting objects from remote sensing images is presented.
Journal ArticleDOI
Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture
TL;DR: A deep learning architecture to combine information coming from S1 and S2 time series, namely TWINNS (TWIn Neural Networks for Sentinel data), able to discover spatial and temporal dependencies in both types of SITS is proposed.
Journal ArticleDOI
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
TL;DR: Zhang et al. as mentioned in this paper presented a novel object detection network (CAD-Net) that exploits attention-modulated features as well as global and local contexts to address the new challenges in detecting objects from remote sensing images.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.