.png){kind=icon}
返回
A Lightweight Convolutional Neural Network for Hyperspectral Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2020.3014313.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the hyperspectral image, each pixel corresponds to a small area on the Earth's surface and represents the intrinsic characteristic of objects, which can be applied for recognition of land covers. Nevertheless, hyperspectral image processing should face some critical issues, and a small sample set problem may be the most challenging one in the research. Deep learning (DL), which has successfully been applied in many fields, has also been introduced for hyperspectral image classification. However, the large gap between the massive parameters to be tuned and limited labeled samples can lead to overfitting scenario, inevitably deteriorating the generalization ability of the DL model. In this article, a lightweight convolutional neural network (LWCNN) is proposed for hyperspectral image classification to mainly tackle the small sample set problem. Especially, spatial-spectral Schroedinger eigenmaps (SSSE) feature extraction is first adopted to obtain the joint spatial-spectral information, and the compressed dimensionality could significantly reduce the number of parameters in the following DL model. Second, a dual-scale convolution (DSC) module is carefully designed to address the SSSE features from a 1-D vector viewpoint (the number of parameters is further decreased), and the DSC procedure is successively employed to obtain the hierarchical structure description that could represent data distribution from different aspects. Subsequently, the feature vectors from all DSC layers are separately filtered by a new bichannel fusion (BCF) module, which could well encode both the intrinsic and contextual information inside DSC features. Finally, the filtered features are concatenated together and imported into a global average pooling classifier to achieve the predicted probability of each category. Experimental results on three famous hyperspectral image data sets illustrate that the developed LWCNN approach is advantageous in both the efficiency and robustness sides for hyperspectral image classification tasks and outperforms other state-of-the-art methods (both traditional-based and DL-based) with very limited labeled samples.
Keyword:
Deep learning (DL)
hyperspectral imagery
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
.png){kind=icon}
分享收藏Density and Thermal Conductivity Measurements for Silicon Melt by Electromagnetic Levitation under a Static Magnetic Field在静态磁场下通过电磁悬浮测量硅熔体的密度和热导率
PREAI
分享收藏Cascade Superpixel Regularized Gabor Feature Fusion for Hyperspectral Image Classification级联超像素正则化Gabor特征融合高光谱图像分类
PREAI
分享收藏Three-Dimensional Local Binary Patterns for Hyperspectral Imagery Classification用于高光谱图像分类的三维局部二值模式
PREAI
分享收藏Collaborative Representation-Based Multiscale Superpixel Fusion for Hyperspectral Image Classification基于协同表示的多尺度超像素融合高光谱图像分类
PREAI
分享收藏
查看更多内容