2021
DOI: 10.3390/rs13030380
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PolSAR Image Classification Using a Superpixel-Based Composite Kernel and Elastic Net

Abstract: The presence of speckles and the absence of discriminative features make it difficult for the pixel-level polarimetric synthetic aperture radar (PolSAR) image classification to achieve more accurate and coherent interpretation results, especially in the case of limited available training samples. To this end, this paper presents a composite kernel-based elastic net classifier (CK-ENC) for better PolSAR image classification. First, based on superpixel segmentation of different scales, three types of features ar… Show more

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Cited by 4 publications
(2 citation statements)
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“…It was acquired by the NASA/JPL AIRSAR system on August 16, 1989 for Flevoland area in the Netherland. It has 15 distinct classes: stem beans, peas, forest, lucerne, wheat, beet, potatoes, bare soil, grass, rapeseed, barley, wheat2, wheat3, water, and buildings [36]. Fig.…”
Section: A Polarimetric Sar Datasetsmentioning
confidence: 99%
“…It was acquired by the NASA/JPL AIRSAR system on August 16, 1989 for Flevoland area in the Netherland. It has 15 distinct classes: stem beans, peas, forest, lucerne, wheat, beet, potatoes, bare soil, grass, rapeseed, barley, wheat2, wheat3, water, and buildings [36]. Fig.…”
Section: A Polarimetric Sar Datasetsmentioning
confidence: 99%
“…The elastic network (ENET) algorithm uses an adjustable convex combination of L1 regular and L2 regular, by introducing an additional parameter α to control the ratio of L1 regular and L2 regular term [62]. Thus, the objective function of defining ENET is as follows:…”
Section: Elastic Network Algorithmmentioning
confidence: 99%