Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images
- 20 February 2015
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 53 (8), 4238-4249
- https://doi.org/10.1109/tgrs.2015.2393857
Abstract
Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elementsoriented land-use classification method based on “partlets,” which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed “sparselets,” from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an L0-norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR landuse data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper.Keywords
Funding Information
- National Science Foundation of China (61401357, 61473231, 61333017, 61202185)
- China Postdoctoral Science Foundation (2014M552491)
This publication has 37 references indexed in Scilit:
- Learning Object-to-Class Kernels for Scene ClassificationIEEE Transactions on Image Processing, 2014
- Weakly-Supervised Cross-Domain Dictionary Learning for Visual RecognitionInternational Journal of Computer Vision, 2014
- Learning Deep and Wide: A Spectral Method for Learning Deep NetworksIEEE Transactions on Neural Networks and Learning Systems, 2014
- Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVMIEEE Transactions on Geoscience and Remote Sensing, 2014
- Semisupervised Classification of Remote Sensing Images With Active QueriesIEEE Transactions on Geoscience and Remote Sensing, 2012
- Online dictionary learning for sparse codingPublished by Association for Computing Machinery (ACM) ,2009
- Extracting and composing robust features with denoising autoencodersPublished by Association for Computing Machinery (ACM) ,2008
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Matching pursuits with time-frequency dictionariesIEEE Transactions on Signal Processing, 1993
- Learning representations by back-propagating errorsNature, 1986