Abstract:GaoFen-2 (GF-2) is a civilian optical satellite self-developed by China equipped with both multispectral and panchromatic sensors, and is the first satellite in China with a resolution below 1 m. Because the pan-sharpening methods on GF-2 imagery have not been a focus of previous works, we propose a novel pan-sharpening method based on guided image filtering and compare the performance to state-of-the-art methods on GF-2 images. Guided image filtering was introduced to decompose and transfer the details and structures from the original panchromatic and multispectral bands. Thereafter, an adaptive model that considers the local spectral relationship was designed to properly inject spatial information back into the original spectral bands. Four pairs of GF-2 images acquired from urban, water body, cropland, and forest areas were selected for the experiments. Both quantitative and visual inspections were used for the assessment. The experimental results demonstrated that for GF-2 imagery acquired over different scenes, the proposed approach consistently achieves high spectral fidelity and enhances spatial details, thereby benefitting the potential classification procedures.
Accurate interpretation of high spatial resolution multispectral (MS) imagery relies on the extraction and fusion of information obtained from both spectral and spatial domains. Feature extraction from one or several fixed windows uses inaccurate description of pixel contexts and produces blurred object boundaries and low classification accuracy. In order to accurately characterize the spatial context properties of pixels, this paper presents a hierarchical-segmentation-based classification system. The system consists of two main modules: 1) hierarchical segmentation and 2) context-based classification. The segmentation module involves an optimization procedure to prevent undersegmentation of the land objects of interest and a scale selection procedure to find the most representative segmentation layers for modeling pixel contexts. The classification module couples a context-driven multilevel feature extraction methodology with a support vector machine classifier to get classification result. The proposed system is validated on three high spatial resolution MS data sets. Compared with state-of-the-art classification methods based on the similar concept, the proposed method demonstrates superior performance on both the classification accuracy and the quality of classification maps.
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