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Pointwise approach for texture analysis and characterization from very high resolution remote sensing images

Abstract : This thesis work proposes a novel pointwise approach for texture analysis in the scope of very high resolution (VHR) remote sensing imagery. This approach takes into consideration only characteristic pixels, not all pixels of the image, to represent and characterize textural features. Due to the fact that increasing the spatial resolution of satellite sensors leads to the lack of stationarity hypothesis in the acquired images, such an approach becomes relevant since only the interaction and characteristics of keypoints are exploited. Moreover, as this technique does not need to consider all pixels inside the image like classical dense approaches, it is more capable to deal with large-size image data offered by VHR remote sensing acquisition systems. In this work, our pointwise strategy is performed by exploiting the local maximum and local minimum pixels (in terms of intensity) extracted from the image. It is integrated into several texture analysis frameworks with the help of different techniques and methods such as the graph theory, the covariance-based approach, the geometric distance measurement, etc. As a result, a variety of texture-based applications using remote sensing data (both VHR optical and radar images) are tackled such as image retrieval, segmentation, classification, and change detection, etc. By performing dedicated experiments to each thematic application, the effectiveness and relevance of the proposed approach are confirmed and validated.
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Submitted on : Tuesday, September 26, 2017 - 8:45:27 AM
Last modification on : Friday, September 25, 2020 - 3:35:58 AM
Long-term archiving on: : Wednesday, December 27, 2017 - 12:34:35 PM


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  • HAL Id : tel-01464333, version 2


Minh Tân Pham. Pointwise approach for texture analysis and characterization from very high resolution remote sensing images. Image Processing [eess.IV]. Ecole Nationale Supérieure des Télécommunications de Bretagne - ENSTB, 2016. English. ⟨NNT : 2016TELB0403⟩. ⟨tel-01464333v2⟩



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