基于多方向自适应感知网络的高光谱遥感图像分类

Hyperspectral remote sensing image classification based on multidirectional adaptive aware network

高光谱遥感能够同步获取目标场景的光谱数据和空间图像,满足对目标物体成分组成和形貌特征的探测需求,已被广泛应用于精准农业、地质勘察、环境监测和生物医学等领域。近几年,得益于优秀的空谱信息表征能力,基于卷积神经网络的高光谱图像分类方法取得了优于传统方法的分类精度。然而,基于一定规则和固定方形窗口的卷积运算无法满足不同对象、不同分布的空谱特征提取需求,当像素位于类别边缘时该模式无法避免其他类别的无关信息,导致类别间的信息扩散以及跨类别像素的误分和错分。针对上述问题,本文提出了一种基于多方向自适应感知的高光谱图像空谱联合分类方法,并通过联合不同邻域范围的空谱上下文信息来改善模型的局部空谱建模能力。具体研究过程如下:首先,将常规卷积网络的全窗口滤波器拆分为不同方向的半窗滤波器,从而设计出侧窗卷积以捕捉具有方向性的空谱特征;然后,在此基础上进一步将不同方向的侧窗滤波核整合到统一的卷积架构中构造空谱分离多方向卷积,并设计方向自适应感知模块,以密集连接构建基于高光谱图像的多方向自适应感知分类网络,赋予模型自适应学习多种空谱结构特征的能力,弥补常规卷积和侧窗卷积只能建模单一方向空谱关系的不足,提升模型对复杂空谱结构的刻画能力。在3个公开数据集上的实验结果表明,本文提出的多方向自适应感知分类方法较常规卷积方法具有更优的分类性能,能增强边缘样本的表征准确性,改善分类中的边缘混淆现象。

Hyperspectral remote sensing can realize the simultaneous acquisition of spectral data and spatial images for the observation scene to satisfy the detection requirements for the composition and morphology of the target object,which is widely used in the fields of precision agriculture,geological survey,environmental monitoring,biology,and medicine.In recent years,benefiting from the powerful representation capabilities of spectral-spatial information,the HyperSpectral Image(HSI)classification methods based on convolutional neural networks have demonstrated superior classification performances to traditional methods.However,the convolutional operation,performed within fixed square windows following certain rules,encounters difficulties in adapting spectral-spatial feature extraction for different objects and spatial distributions and introduces irrelevant information from other categories,leading to information diffusion between different categories and misclassification of edge pixels.This paper proposes a hyperspectral image classification method based on multidirectional adaptive awareness to address this problem.This method integrates the spatial contextual information of different neighborhood ranges to improve the local spectral-spatial modeling capability of the model.First,the full-window filter of the regular convolution is split into side-window filters with different orientations to design the Side-Window Convolution(SWC)acquiring the directional spectral-spatial features.Therefore,multiple side-window filter kernels of different directions are integrated into a unified convolution architecture to construct the spectral-spatial separable multidirectional convolution(S3MDC).The direction-adaptive aware module(DAAM)is designed to assist S3MDC and build a multidirectional adaptive aware network(MDAAN)with dense connections for HSI classification.MDAAN can adaptively learn multidirectional spatial-spectral features,improve the representation of complex spectralspatial structures,and compensate for the deficiency that SWC only captures spectral-spatial relationships in a single direction.Experiments were conducted on three public datasets,including Indian Pines,University of Pavia,and Kennedy Space Center.MDAAN achieves the overall highest classification accuracy of 97.67%(Indian pines,5%/class),99.40%(University of Pavia,1%/class),and 99.64%(Kennedy Space Center,5%/class),which is superior to other deep learning methods,verifying the effectiveness of the proposed model.First,compared with other deep learning methods,MDAAN can provide better classification performance.Second,MDAAN generalizes better with the small number of training samples to prove the stability of its classification performance.Finally,the ablation analysis of different convolutional models and DAAM demonstrates the effectiveness and necessity of S3MDC and the adaptive aware mechanisms for multidirectional features.