深度学习驱动的高光谱图像特征提取与分类方法
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"本文提出了一种基于卷积神经网络（CNN）的高光谱图像正则化深度特征提取方法，用于解决高光谱图像（HSI）分类的问题。该方法利用多个卷积和池化层从HSI中提取非线性、判别性和不变性的深度特征，有助于图像分类和目标检测。同时，为了解决高光谱图像分类中常见的高维性和训练样本有限之间的不平衡问题，研究了L2正则化和dropout策略，防止过拟合。此外，还提出了一个结合正则化的3-D CNN特征提取模型，以提取更有效的空间-光谱特征。"
在本文中，作者深入探讨了如何利用深度学习技术，特别是卷积神经网络，来改进高光谱图像的特征提取和分类性能。高光谱图像具有丰富的光谱信息，但处理这种数据集时通常会面临两个主要挑战：一是高维度，二是训练样本数量有限。为了解决这些问题，作者提出了一种新的方法。
首先，他们设计了一个CNN架构，该架构由多个卷积层和池化层组成。这些层能够逐层学习HSI中的抽象特征，从原始像素级信息逐渐提取到更高级别的语义特征。这些深度特征不仅包含HSI的光谱信息，还包含了空间信息，使得特征具有非线性、判别性和不变性，这对于区分不同类别的地物至关重要。
其次，为了缓解高维和有限训练样本之间的不平衡，作者采用了L2正则化。L2正则化通过在损失函数中添加权重的平方和来惩罚模型的复杂度，从而减少过拟合的风险。此外，dropout策略也被引入，这是一种在训练过程中随机“丢弃”一部分神经元的方法，以强制模型学习更多冗余特征，提高泛化能力。
最后，他们进一步发展了一个3-D CNN模型，利用3-D卷积来同时考虑HSI的空间和光谱信息。结合正则化，这个模型能够提取出更有效的空间-光谱特征，这对于高光谱图像的分类至关重要，因为这类图像的特点是光谱和空间信息紧密交织。
这项工作展示了深度学习在高光谱图像处理领域的潜力，为解决高维性和样本不足问题提供了新的思路，并通过3-D CNN加强了特征的提取。这些方法的实现和应用对于提高HSI的分类准确性和鲁棒性具有重要意义。
CHEN et al.: DEEP FEATURE EXTRACTION AND CLASSIFICATION OF HYPERSPECTRAL IMAGES 6235
Fig. 3. Architecture of deep CNN with spectral FE of HSI.
and classification. In the FE p rocedure, LR is taken into account
to adjust the weights and biases in the back-propagation. After
the training, the learned features can be used in conjunction
with classifiers such as LR, K-nearest neighbor (KNN), and
SVMs [1].
The proposed architecture is shown in Fig. 3. The input of the
system is a pixel vector of hyperspectral data, and the output of
the system is the label of the pixel vector. It consists of several
convolutional and pooling layers and an LR layer. In Fig. 3, as
an example, the flexible CNN model includes two convolution
layers and two pooling layers. There are three feature m aps in
the first convolution layer and six feature maps in the second
convolution layer.
After several layers of convolution and pooling, the in-
put pixel vector can be converted into a feature vector,
which captures the spectral information in the input pixel
vector. Finally, we use LR or other classifiers to fulfill the
classification step.
The power of CNN depends on the connections (weights) of
the network; hence, it is very important to find a set of proper
weights. Gradient back-propagation is the core fundamental
algorithm for all kinds of neural networks. In this paper, the
model parameters are initialized randomly and trained by an
error back-propagation algorithm.
Before setting an updating r ule for the weights, one needs
to properly set an “error” measure, i.e., a cost function. There
are several ways to define such a cost function. In our imple-
mentation, a mini-batch update strategy is adopted, which is
suitable for large data set processing, and the cost is computed
on a mini-batch of inputs [37]
c
0
= −
1
m
m
i=1
[x
i
log(z
i
)+(1− x
i
) log(1 − z
i
)] . (4)
Here, m denotes the mini-batch size. Two variables x
i
and
z
i
denote the ith predicted label and the label in the mini-
batch, respectively. The i summation is done over the whole
mini-batch. Our hope turns to optimize (4) using mini-batch
stochastic gradient descent.
LR is a type of probabilistic statistical classification model.
It measures the relation b etween a categorical variable and the
input variables using probability scores as the predicted values
of the input variables.
To pe rform classificatio n b y utilizing the learned features
from the CNN, we employ an LR classifier, which uses soft-
max as its output-layer activation. Softmax ensures that the
activation of each output unit sums to 1 so that we can deem
the output as a set of conditional probabilities. For given input
vector R, the probability that the input belongs to category i can
be estimated as follows:
P (Y = i|R, W, b)=s(WR+ b)=
e
W
i
R+b
i
j
e
W
j
R+b
j
(5)
where W and b are the weights and biases of the LR layer, and
the summation is done over all the output units.
In the LR, the size of the output layer is set to be the same
as the total number of classes defined, and the size of the input
layer is set to be the same as the size of the output layer of
the CNN. Since the LR is implemented as a single-layer neural
network, it can be merged with the former layers of networks to
form a d eep classifier.
D. L2 Regularization of CNN
Overfitting is a common pro blem of neural network ap-
proaches, which means that the classification results can be very
good on the training data set but poor on the test data set. In this
case, HSI will be classified with low accuracy. The number of
training samples is limited in HSI classification, which of ten
leads to the problem of overfitting.
To avoid overfitting, it is necessary to adopt additional tech-
niques such as regularization. In this section, we introduce L2
regularization in the proposed model, which is a penalizing
model with extreme parameter values [41].
L2 regularization encourages the sum of the squares of
the parameters to be small, which can be added to learning
algorithms that minimize a cost function. Equation (4) is then
modified to
c = c
0
+
λ
2m
N
j=1
w
2
j
(6)
where m denotes the mini-batch size, N is the number of
weights, and λ is a free parameter that needs to be tuned
empirically. In addition, the coefficient, 1/2, is used to simplify
the process of the derivation.
In (6), one can see that L2 regularization can make w small.
In most cases, it can h elp with the reduction of the bias of the
model to mitigate the overfitting problem.
Authorized licensed use limited to: Xi'an Univ of Posts & Telecom. Downloaded on June 14,2020 at 13:59:55 UTC from IEEE Xplore. Restrictions apply.
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