Remote Sensing Image Scene Classification with Noisy Label Distillation

Abstract

The widespread applications of remote sensing image scene classification-based Convolutional Neural Networks (CNNs) are severely affected by the lack of large-scale datasets with clean annotations. Data crawled from the Internet or other sources allows for the most rapid expansion of existing datasets at a low-cost. However, directly training on such an expanded dataset can lead to network overfitting to noisy labels. Traditional methods typically divide this noisy dataset into multiple parts. Each part fine-tunes the network separately to improve performance further. These approaches are inefficient and sometimes even hurt performance. To address these problems, this study proposes a novel noisy label distillation method (NLD) based on the end-to-end teacher-student framework. First, unlike general knowledge distillation methods, NLD does not require pre-training on clean or noisy data. Second, NLD effectively distills knowledge from labels across a full range of noise levels for better performance. In addition, NLD can benefit from a fully clean dataset as a model distillation method to improve the student classifier’s performance. NLD is evaluated on three remote sensing image datasets, including UC Merced Land-use, NWPU-RESISC45, AID, in which a variety of noise patterns and noise amounts are injected. Experimental results show that NLD outperforms widely used directly fine-tuning methods and remote sensing pseudo-labeling methods.

Embedded Self-Distillation in Compact Multibranch Ensemble Network for Remote Sensing Scene Classification

“…The discriminative modality distillation approach is introduced in [47], the teacher is trained on multimodal data, and then, the student model learns from the teacher model to improve the performance of the remote sensing image classifications. To address the problem of network overfitting due to noisy data, a novel noisy label distillation method (NLD) is proposed in [48].…”

Embedded Self-Distillation in Compact Multi-Branch Ensemble Network for Remote Sensing Scene Classification

“…Discriminative modality distillation approach is introduced in [48], the teacher is trained on multimodal data and then the student model learns from the teacher model to improve the performance of the RS image classifiction. To address the problem of network overfitting due to noisy data, a novel noisy label distillation method (NLD) is proposed in [49].…”

Unsupervised Adversarial Domain Adaptation with Error-Correcting Boundaries and Feature Adaption Metric for Remote-Sensing Scene Classification

“…In the early stage of development, traditional machine learning methods have been used for scene classification tasks, such as support vector machine and bag of words [2,3]. Recently, deep learning methods have been proven to be effective for extracting image features [4][5][6][7][8], and many studies have demonstrated effective scene classification performance with the help of deep learning from various novel perspectives including self-supervised learning [9], data augmentation [10], feature fusion [11][12][13][14][15], reconstructing networks [16][17][18][19][20][21][22][23], integration of spectral and spatial information [24], balancing global and local features, refining feature maps through encoding method [25], adding a new mechanism [26,27], as well as introducing a new network [28], open set problem [29], and noisy label distillation [30]. However, a lack of annotated data has restricted the development of deep learning methods in scene classification due to the high cost of annotating data.…”