• Corpus ID: 244800748

Multi-Label Noise Robust Collaborative Learning Model for Remote Sensing Image Classification

  title={Multi-Label Noise Robust Collaborative Learning Model for Remote Sensing Image Classification},
  author={Ahmet Aksoy and Mahdyar Ravanbakhsh and Beg{\"u}m Demir},
The development of accurate methods for multilabel classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. Methods based on Deep Convolutional Neural Networks (CNNs) have shown strong performance gains in RS MLC problems. However, CNN-based methods usually require a high number of reliable training images annotated by multiple land-cover class labels. Collecting such data is time-consuming and costly. To address this problem, the publicly available… 


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The proposed Consensual Collaborative Multi-Label Learning (CCML) identifies, ranks and corrects training images with noisy multi-labels through four main modules: 1) discrepancy module; 2) group lasso module; 3) flipping module; and 4) swap module.
A Deep Multi-Attention Driven Approach for Multi-Label Remote Sensing Image Classification
Experiments carried out on BigEarthNet show the effectiveness of the proposed approach in terms of multi-label classification accuracy compared to the state-of-the-art approaches.
Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding
Experimental results obtained in the framework of RS image scene classification problems show that a shallow Convolutional Neural Network architecture trained on the BigEarthNet provides much higher accuracy compared to a state-of-the-art CNN model pre-trained on the ImageNet.
Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method
A semisupervised graph-theoretic method in the framework of multilabel RS image retrieval problems that retrieves images similar to a given query image by a subgraph matching strategy and shows effectiveness when compared with the state-of-the-art RS content-based image retrieval methods.
Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification
  • Yuansheng Hua, Lichao Mou, Xiaoxiang Zhu
  • Computer Science
    ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing
  • 2019
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