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.
Highlights
Exploiting satellite image data to understand and monitor the land cover and land use from the Earth’s surface in general, in agriculture, is one of the most significant tasks of remote sensing
The reason is that we have focused on characterizing local textural features within very high resolution (VHR) images and taken them into account during the construction of our proposed local extrema-based descriptor (LED) descriptor
Needs to be small enough to ensure sufficiently dense local extrema to support the computation of LED, we propose to fix it to 3 × 3 pixels in this work
Summary
Exploiting satellite image data to understand and monitor the land cover and land use from the Earth’s surface in general, in agriculture, is one of the most significant tasks of remote sensing. We carry out a study of vineyard cultivation by detecting vine parcels using VHR optical remotely-sensed images. Our motivation is to perform a supervised classification algorithm to distinguish vineyard parcels from other items present from the image content, such as forest zones, bare soils, early grown grasses, urban areas, etc. In order to to that, we first propose a novel descriptor to characterize structural and textural features from the image. A retrieval process is proposed to validate and confirm the performance of this novel descriptor. A supervised classification process is carried out to detect vine fields among other classes
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