.png){kind=icon}
返回
Superpixel-Based Long-Range Dependent Network for High-Resolution Remote-Sensing Image Classification
.png){kind=icon}
OA
AI
DOI：10.3390/land11112028.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Data-driven deep neural networks have demonstrated their superiority in high-resolution remote-sensing image (HRSI) classification based on superpixel-based objects. Currently, most HRSI classification methods that combine deep learning and superpixel object segmentation use multiple scales of stacking to satisfy the contextual semantic-information extraction of one analyzed object. However, this approach does not consider the long-distance dependencies between objects, which not only weakens the representation of feature information but also increases computational redundancy. To solve this problem, a superpixel-based long-range dependent network is proposed for HRSI classification. First, a superpixel segmentation algorithm is used to segment HRSI into homogeneous analysis objects as input. Secondly, a multi-channel deep convolutional neural network is proposed for the feature mapping of the analysis objects. Finally, we design a long-range dependent framework based on a long short-term memory (LSTM) network for obtaining contextual relationships and outputting classes of analysis objects. Additionally, we define the semantic range and investigate how it affects classification accuracy. A test is conducted by using two HRSI with overall accuracy (0.79, 0.76) and kappa coefficients (kappa) (0.92, 0.89). Both qualitative and quantitative comparisons are adopted to test the proposed method's efficacy. Findings concluded that the proposed method is competitive and consistently superior to the benchmark comparison method.
Keyword:
remote-sensing image
deep learning
image classification
long-range dependence
semantic scope
期刊
IF:
3.2
论文数:
1.3W
被引数:
2.5W
机构
引用论文
Maximum Likelihood Estimation-Based Joint Sparse Representation for the Classification of Hyperspectral Remote Sensing Images基于最大似然估计的联合稀疏表示用于高光谱遥感图像分类
.png){kind=icon}
PREAI
分享收藏分享收藏Distant regulatory elements in a Sox10‐βGEO BAC transgene are required for expression of Sox10 in the enteric nervous system and other neural crest‐derived tissuesSox10-βgeo BAC转基因中的远距离调控元件是肠神经系统和其他神经源性组织中 Sox10 表达所必需的
OAAI
OAAI分享收藏
查看更多内容