Region Convolutional Features for Multi-Label Remote Sensing Image Retrieval

Abstract

Conventional remote sensing image retrieval (RSIR) systems usually perform single-label retrieval where each image is annotated by a single label representing the most significant semantic content of the image. This assumption, however, ignores the complexity of remote sensing images, where an image might have multiple classes (i.e., multiple labels), thus resulting in worse retrieval performance. We therefore propose a novel multi-label RSIR approach with fully convolutional networks (FCN). In our approach, we first train a FCN model using a pixel-wise labeled dataset,and the trained FCN is then used to predict the segmentation maps of each image in the considered archive. We finally extract region convolutional features of each image based on its segmentation map.The region features can be either used to perform region-based retrieval or further post-processed to obtain a feature vector for similarity measure. The experimental results show that our approach achieves state-of-the-art performance in contrast to conventional single-label and recent multi-label RSIR approaches.

8 pages

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Fully Convolutional Networks for Semantic Segmentation
• PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval
• Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval

Cited by in corpus (4)

• Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network
• MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images
• Semantics-Consistent Representation Learning for Remote Sensing Image-Voice Retrieval
• A Novel Graph-Theoretic Deep Representation Learning Method for Multi-Label Remote Sensing Image Retrieval