Auto‐encoder‐based shared mid‐level visual dictionary learning for scene classification using very high resolution remote sensing images
Open Access
- 1 October 2015
- journal article
- research article
- Published by Institution of Engineering and Technology (IET) in IET Computer Vision
- Vol. 9 (5), 639-647
- https://doi.org/10.1049/iet-cvi.2014.0270
Abstract
Effective representation and classification of scenes using very high resolution (VHR) remote sensing images cover a wide range of applications. Although robust low-level image features have been proven to be effective for scene classification, they are not semantically meaningful and thus have difficulty to deal with challenging visual recognition tasks. In this study, the authors propose a new and effective auto-encoder-based method to learn a shared mid-level visual dictionary. This dictionary serves as a shared and universal basis to discover mid-level visual elements. On the one hand, the mid-level visual dictionary learnt using machine learning technique is more discriminative and contains rich semantic information, compared with the traditional low-level visual words. On the other hand, the mid-level visual dictionary is more robust to occlusions and image clutters. In the authors' scene-classification scheme, they use discriminative mid-level visual elements, rather than individual pixels or low-level image features, to represent images. This new image representation is able to capture much of the high-level meaning and contents of the image, facilitating challenging remote sensing image scene-classification tasks. Comprehensive evaluations on a challenging VHR remote sensing images data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of their study.Keywords
Funding Information
- National Natural Science Foundation of China (91120005, 61473231, 61333017)
- China Postdoctoral Science Foundation (2014M552491)
This publication has 38 references indexed in Scilit:
- Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVMIEEE Transactions on Geoscience and Remote Sensing, 2014
- Feature Learning for Image Classification Via Multiobjective Genetic ProgrammingIEEE Transactions on Neural Networks and Learning Systems, 2013
- Object detection in remote sensing imagery using a discriminatively trained mixture modelISPRS Journal of Photogrammetry and Remote Sensing, 2013
- Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSAInternational Journal of Remote Sensing, 2012
- Sparselet Models for Efficient Multiclass Object DetectionLecture Notes in Computer Science, 2012
- Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machineInternational Journal of Remote Sensing, 2010
- Reducing the Dimensionality of Data with Neural NetworksScience, 2006
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004
- Learning representations by back-propagating errorsNature, 1986
- Updating quasi-Newton matrices with limited storageMathematics of Computation, 1980