摘要
高光谱图像凭借其“图谱合一”的特点逐渐在军事、环境、农业等方面发挥出重要作用。但是,由于传感器空间分辨率的限制以及地物分布的复杂多样性,高光谱遥感图像中通常存在大量的混合像元,严重制约了高光谱遥感的应用范围。目前,处理混合像元问题最有效的分析方法是混合像元分解(解混)。近年来,深度学习的发展对高光谱遥感产生了重大影响,也催生出一系列基于深度学习的解混方法。现有基于深度学习的解混方法在隐藏信息挖掘方面表现出极大的潜力和优势,通常情况下能够取得更加准确的结果。然而,这些方法大多只考虑了地物的光谱信息而忽略空间分布规律,导致在复杂场景中估算结果可能并不理想,逐渐难以满足工程应用的实际需求。为进一步发掘和利用空间信息提升解混的准确性,本文构建了一种新的深度学习网络来实现高光谱图像解混。新提出的解混网络采用卷积层来获取先验信息,利用高斯核函数的特性来协助区分物质属性,并且通过分配中心像元与邻域像元间的权重来增进丰度平滑性。在新网络中,本文使用Softmax作为丰度对应层的激活函数来约束丰度的输出。此外,在Softmax中,本文采用了L1/2正则化来避免节点出现过拟合而影响最终结果,进一步强化了网络性能,最终形成了一种双约束强化的深度卷积自编码网络来实现无监督的解混。为了验证新方法的有效性和优势,本文将新提出的方法与同类解混方法应用在一系列高光谱数据(包括模拟图像和真实图像)中进行测试,均达到了预期效果。本文的研究成果能够为处理混合像元问题提供了新的技术支撑和理论参考。
Hyperspectral images play an important role in the environment, military, agriculture, etc. Imaging spectrometers can acquire the continuous spectrum of each pixel in the image while acquiring images. However, due to the limitation of sensor spatial resolution and the complex diversity of ground feature distribution, many pixels may be mixed by several materials(mixed pixels) in hyperspectral images. However, these mixed pixels seriously restrict the application scope of hyperspectral remote sensing. Nowadays, hyperspectral unmixing is the most effective analytical way to deal with the mixed pixels problem. Recently, the development of deep learning has brought a significant impact on hyperspectral remote sensing and has fostered many deep learning-based unmixing algorithms. Autoencoder is an unsupervised learning tool commonly used in deep learning, and it has been widely used in the research of hyperspectral unmixing methods for constructing deep networks under its good scalability. Existing deep learning-based unmixing methods show significant advantages in hidden information mining and can usually achieve more accurate results. However, most of these methods only consider the spectral information of the ground features and ignore the spatial distribution pattern, which leads to the poor smoothness of the estimated abundance from the complex scenes and makes it challenging to meet the practical needs of engineering applications. This paper proposes a deep double-constraints convolution network(DDCCN) that can further explore and utilize spatial information to improve unmixing performances and accuracies. The new proposed method uses a convolutional network to obtain a priori information. By utilizing the properties of the Gaussian kernel, the method may better distinguish different materials and assign weights between the central image pixels. Meanwhile, this strategy can improve the smoothness of abundance. In the new network structure, we adopt the softmax as the activation function of the abundance counterpart layer to constrain the abundance estimation. In addition, the L1/2 regularization is used in the softmax to avoid overfitting the nodes. Moreover, the L1/2 regularization can further enhance the sparsity of the abundances. To evaluate the effectiveness and advantages of our method, we use a series of hyperspectral data(including synthetic data sets and real images) to test our approach, compared with other state-of-the-art unmixing methods. From the comparisons with other methods, it is observed that the proposed DDCCN demonstrates competitive performance. The work can provide new technical support and theoretical reference for dealing with the mixed pixels problem.
作者
朱治青
苏远超
李朋飞
白晋颖
刘英
刘峰
ZHU Zhiqing;SU Yuanchao;LI Pengfei;BAI Jinying;LIU Ying;LIU Feng(Xi’an University of Science and Technology,College of Geomatics,Xi’an,Shaanxi 710054,China;Xi’an Piesat Information Technology Company Limited,Xi’an,Shaanxi 710199,China)
出处
《信号处理》
CSCD
北大核心
2023年第1期128-142,共15页
Journal of Signal Processing
基金
国家自然科学基金青年基金项目(42001319)
陕西省教育厅科研计划项目(21JK0762)。
关键词
高光谱遥感
混合像元分解
深度学习空谱解混
自动编码器
卷积神经网络
hyperspectral remote sensing
hyperspectral unmixing
spectral-spatial hyperspectral unmixing deep learning
autoencoder
convolutional neural network
