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Remote Sens. 2016, 8(5), 368; doi:10.3390/rs8050368

Texture Retrieval from VHR Optical Remote Sensed Images Using the Local Extrema Descriptor with Application to Vineyard Parcel Detection

1
Institut Telecom, Telecom Bretagne, CNRS UMR 6285 Lab-STICC/CID, 29238 Brest Cedex 3, France
2
I-Sea (SAS), 33702 Mérignac Cedex, France
3
The French Space Agency (CNES), DCT/SI/AP-BPI 1219, 31401 Toulouse Cedex 09, France
*
Authors to whom correspondence should be addressed.
Academic Editors: Guoqing Zhou and Prasad S. Thenkabail
Received: 15 February 2016 / Revised: 31 March 2016 / Accepted: 25 April 2016 / Published: 28 April 2016
View Full-Text   |   Download PDF [5685 KB, uploaded 28 April 2016]   |  

Abstract

In this article, we develop a novel method for the detection of vineyard parcels in agricultural landscapes based on very high resolution (VHR) optical remote sensing images. Our objective is to perform texture-based image retrieval and supervised classification algorithms. To do that, the local textural and structural features inside each image are taken into account to measure its similarity to other images. In fact, VHR images usually involve a variety of local textures and structures that may verify a weak stationarity hypothesis. Hence, an approach only based on characteristic points, not on all pixels of the image, is supposed to be relevant. This work proposes to construct the local extrema-based descriptor (LED) by using the local maximum and local minimum pixels extracted from the image. The LED descriptor is formed based on the radiometric, geometric and gradient features from these local extrema. We first exploit the proposed LED descriptor for the retrieval task to evaluate its performance on texture discrimination. Then, it is embedded into a supervised classification framework to detect vine parcels using VHR satellite images. Experiments performed on VHR panchromatic PLEIADES image data prove the effectiveness of the proposed strategy. Compared to state-of-the-art methods, an enhancement of about 7% in retrieval rate is achieved. For the detection task, about 90% of vineyards are correctly detected. View Full-Text
Keywords: very high resolution (VHR) images; feature extraction; local extrema-based descriptor (LED); texture retrieval; supervised classification; vineyard cultivation very high resolution (VHR) images; feature extraction; local extrema-based descriptor (LED); texture retrieval; supervised classification; vineyard cultivation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pham, M.-T.; Mercier, G.; Regniers, O.; Michel, J. Texture Retrieval from VHR Optical Remote Sensed Images Using the Local Extrema Descriptor with Application to Vineyard Parcel Detection. Remote Sens. 2016, 8, 368.

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