A meta-methodology for improving land cover and land use classification with SAR imagery

• Autor(a) principal: Soares, Marinalva Dias
• Data de Publicação: 2020
• Outros Autores: Dutra, Luciano Vieira, Costa, Gilson Alexandre Ostwald Pedro da, Feitosa, Raul Queiroz, Negri, Rogério Galante [UNESP], Diaz, Pedro M. A.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/rs12060961; http://hdl.handle.net/11449/198665
• topic: GEOBIA; LULC classification; Meta-methodologies; Region-based classification; SAR classification; SAR data segmentation; Segmentation tuning

Per-point classification is a traditional method for remote sensing data classification, and for radar data in particular. Compared with optical data, the discriminative power of radar data is quite limited, for most applications. A way of trying to overcome these diculties is to use Region-Based Classification (RBC), also referred to as Geographical Object-Based Image Analysis (GEOBIA). RBC methods first aggregate pixels into homogeneous objects, or regions, using a segmentation procedure. Moreover, segmentation is known to be an ill-conditioned problem because it admits multiple solutions, and a small change in the input image, or segmentation parameters, may lead to significant changes in the image partitioning. In this context, this paper proposes and evaluates novel approaches for SAR data classification, which rely on specialized segmentations, and on the combination of partial maps produced by classification ensembles. Such approaches comprise a meta-methodology, in the sense that they are independent from segmentation and classification algorithms, and optimization procedures. Results are shown that improve the classification accuracy from Kappa = 0.4 (baseline method) to a Kappa = 0.77 with the presented method. Another test site presented an improvement from Kappa = 0.36 to a maximum of 0.66 also with radar data.