Adversarial Domain Adaptation Network With Calibrated Prototype and Dynamic Instance Convolution for Hyperspectral Image Classification

doi:10.1109/TGRS.2024.3387990

Adversarial Domain Adaptation Network With Calibrated Prototype and Dynamic Instance Convolution for Hyperspectral Image Classification

2024-01-01

19

PRE

AI

DOI：10.1109/TGRS.2024.3387990

Original

Original request for help

Save

Abstract

En 中文

Recently, the adversarial domain adaptation (ADA) methods have been widely investigated and applied in cross-domain hyperspectral image (HSI) classification. However, most ADA algorithms aim to align the cross-domain distribution without focusing on the class separability of the aligned target features and the information of samples within the domain. To address these issues, a new ADA framework based on calibrated prototype and dynamic instance convolution (CPDIC) is proposed in this article for cross-domain HSI classification. The CPDIC is composed of a generator, a calibrated discriminator (CD), and a classifier. The generator includes a static 3-D convolutional network (SCN) and a dynamic instance convolutional network (DICN), where the SCN is used to extract coarse-grained features of HSI and the DICN can extract sample-specific fine-grained features using instance convolutions generated from dynamic instance convolution kernel generation (DCKG) module. As for the generator, the static and dynamic interactive feature extraction network extracts robust domain-invariant features with discriminability. The CD aligns the marginal distribution between domains and calibrate the predicted pseudo-labels of target domain. For classification, a calibrated prototype loss (CPL) is introduced to align the class distribution across domains. The results of three cross-domain HSI classification tasks show that the proposed CPDIC outperforms existing unsupervised domain adaptation (UDA) algorithms.

Keywords:

Feature extraction

Generators

Convolutional neural networks

Prototypes

Three-dimensional displays

Convolution

Kernel

Adversarial learning

calibrated prototype

domain adaptation (DA)

dynamic instance convolution

hyperspectral image (HSI)

Adversarial Domain Adaptation Network With Calibrated Prototype and Dynamic Instance Convolution for Hyperspectral Image Classification

Abstract

Journal

Organization