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分层多尺度卷积神经网络在高光谱图像分类中的应用。

Hierarchical Multi-Scale Convolutional Neural Networks for Hyperspectral Image Classification.

机构信息

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.

Department of Mathematics, University of Iowa, Iowa City, IA 52242, USA.

出版信息

Sensors (Basel). 2019 Apr 10;19(7):1714. doi: 10.3390/s19071714.

Abstract

Deep learning models combining spectral and spatial features have been proven to be effective for hyperspectral image (HSI) classification. However, most spatial feature integration methods only consider a single input spatial scale regardless of various shapes and sizes of objects over the image plane, leading to missing scale-dependent information. In this paper, we propose a hierarchical multi-scale convolutional neural networks (CNNs) with auxiliary classifiers (HMCNN-AC) to learn hierarchical multi-scale spectral-spatial features for HSI classification. First, to better exploit the spatial information, multi-scale image patches for each pixel are generated at different spatial scales. These multi-scale patches are all centered at the same central spectrum but with shrunken spatial scales. Then, we apply multi-scale CNNs to extract spectral-spatial features from each scale patch. The obtained multi-scale convolutional features are considered as structured sequential data with spectral-spatial dependency, and a bidirectional LSTM is proposed to capture the correlation and extract a hierarchical representation for each pixel. To better train the whole network, weighted auxiliary classifiers are employed for the multi-scale CNNs and optimized together with the main loss function. Experimental results on three public HSI datasets demonstrate the superiority of our proposed framework over some state-of-the-art methods.

摘要

深度学习模型结合光谱和空间特征已被证明对高光谱图像(HSI)分类有效。然而,大多数空间特征集成方法仅考虑单一输入空间尺度,而不考虑图像平面上各种形状和大小的目标,导致缺少与尺度相关的信息。在本文中,我们提出了一种具有辅助分类器的分层多尺度卷积神经网络(HMCNN-AC),用于学习 HSI 分类的分层多尺度光谱-空间特征。首先,为了更好地利用空间信息,针对每个像素在不同的空间尺度上生成多尺度图像补丁。这些多尺度补丁都以相同的中心光谱为中心,但空间尺度缩小了。然后,我们应用多尺度 CNN 从每个尺度补丁中提取光谱-空间特征。获得的多尺度卷积特征被视为具有光谱-空间依赖性的结构化顺序数据,并提出了双向 LSTM 来捕获相关性并为每个像素提取分层表示。为了更好地训练整个网络,我们为多尺度 CNN 采用了加权辅助分类器,并与主要损失函数一起进行优化。在三个公共 HSI 数据集上的实验结果表明,我们提出的框架优于一些最先进的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9965/6480716/de08a7122c37/sensors-19-01714-g001.jpg

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