DocumentCode :
3682967
Title :
Improving Spatial Feature Representation from Aerial Scenes by Using Convolutional Networks
Author :
Keiller Nogueira;Waner O. Miranda;Jefersson A. Dos Santos
Author_Institution :
Dept. of Comput. Sci., Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil
fYear :
2015
Firstpage :
289
Lastpage :
296
Abstract :
The performance of image classification is highly dependent on the quality of extracted features. Concerning high resolution remote image images, encoding the spatial features in an efficient and robust fashion is the key to generating discriminatory models to classify them. Even though many visual descriptors have been proposed or successfully used to encode spatial features of remote sensing images, some applications, using this sort of images, demand more specific description techniques. Deep Learning, an emergent machine learning approach based on neural networks, is capable of learning specific features and classifiers at the same time and adjust at each step, in real time, to better fit the need of each problem. For several task, such image classification, it has achieved very good results, mainly boosted by the feature learning performed which allows the method to extract specific and adaptable visual features depending on the data. In this paper, we propose a novel network capable of learning specific spatial features from remote sensing images, with any pre-processing step or descriptor evaluation, and classify them. Specifically, automatic feature learning task aims at discovering hierarchical structures from the raw data, leading to a more representative information. This task not only poses interesting challenges for existing vision and recognition algorithms, but also brings huge opportunities for urban planning, crop and forest management and climate modelling. The propose convolutional neural network has six layers: three convolutional, two fully-connected and one classifier layer. So, the five first layers are responsible to extract visual features while the last one is responsible to classify the images. We conducted a systematic evaluation of the proposed method using two datasets: (i) the popular aerial image dataset UCMerced Land-use and, (ii) a multispectral high-resolution scenes of the Brazilian Coffee Scenes. The experiments show that the proposed method outperforms state-of-the-art algorithms in terms of overall accuracy.
Keywords :
"Feature extraction","Neurons","Remote sensing","Mathematical model","Biological neural networks","Visualization","Robustness"
Publisher :
ieee
Conference_Titel :
Graphics, Patterns and Images (SIBGRAPI), 2015 28th SIBGRAPI Conference on
Electronic_ISBN :
1530-1834
Type :
conf
DOI :
10.1109/SIBGRAPI.2015.39
Filename :
7314576
Link To Document :
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