Weakly Supervised Classification of Hyperspectral Image Based on Complementary Learning

Abstract

In recent years, supervised learning-based methods have achieved excellent performance for hyperspectral image (HSI) classification. However, the collection of training samples with labels is not only costly but also time-consuming. This fact usually causes the existence of weak supervision, including incorrect supervision where mislabeled samples exist and incomplete supervision where unlabeled samples exist. Focusing on the inaccurate supervision and incomplete supervision, the weakly supervised classification of HSI is investigated in this paper. For inaccurate supervision, complementary learning (CL) is firstly introduced for HSI classification. Then, a new method, which is based on selective CL and convolutional neural network (SeCL-CNN), is proposed for classification with noisy labels. For incomplete supervision, a data augmentation-based method, which combines mixup and Pseudo-Label (Mix-PL) is proposed. And then, a classification method, which combines Mix-PL and CL (Mix-PL-CL), is designed aiming at better semi-supervised classification capacity of HSI. The proposed weakly supervised methods are evaluated on three widely-used hyperspectral datasets (i.e., Indian Pines, Houston, and Salinas datasets). The obtained results reveal that the proposed methods provide competitive results compared to the state-of-the-art methods. For inaccurate supervision, the proposed SeCL-CNN has outperformed the state-of-the-art method (i.e., SSDP-CNN) by 0.92%, 1.84%, and 1.75% in terms of OA on the three datasets, when the noise ratio is 30%. And for incomplete supervision, the proposed Mix-PL-CL has outperformed the state-of-the-art method (i.e., AROC-DP) by 1.03%, 0.70%, and 0.82% in terms of OA on the three datasets, with 25 training samples per class.

Agreement and Disagreement-Based Co-Learning with Dual Network for Hyperspectral Image Classification with Noisy Labels

“…[54] proposed a novel adaptive multi-feature collaborative representation classifier to correct the labels of uncertain samples. (4) Hybrid approach [21,23,26,51,[55][56][57][58]. Both [51] and [55] introduced unsupervised method into label noise leaning.…”

“…In [55], an unsupervised method was combined with domain adaptation for HSI classification. In addition, complementary learning was combined with deep learning for HSI classification [26] and RS scene classification [56]. Recently, Ref.…”

“…[25] investigated the robustness of several loss functions to convolutional neural networks and proposed an HSI pixel-to-image sampling method to prevent overfitting on label noise. To address the inaccurate supervision caused by label noise, selective complementary learning was introduced into convolutional neural networks for HSI classification with noisy labels [26].…”

An Improved 3D-2D Convolutional Neural Network Based on Feature Optimization for Hyperspectral Image Classification

“…The phenomenon of small samples is an inherent characteristic of the mapping of the real world to the digital world, and hyperspectral data processing also faces such a challenge all the time. For the lack of samples, there are currently two main solutions: 1) data augmentation (sample expansion) [29], [30], [31], [32]; 2) adaptation to small sample learning [33], [34], [35]. Generally speaking, the larger the number of training samples in deep learning, the more representative the extracted data features, the smaller the sample size, and the less general the feature expression.…”

An assessment of image classifiers for generating machine-learning training samples for mapping the invasive Campuloclinium macrocephalum (Less.) DC (pompom weed) using DESIS hyperspectral imagery

“…For instance, Melgani and Bruzzone (2004) against a traditional CNN model. However, acquiring thousands of accurate reference training samples using traditional field data collection methods such as the Global Navigation Satellite System (GNSS), may be impractical due to time constraints, logistical limitations, and terrain inaccessibility (Fang et al, 2020;Huang et al, 2021).…”