Probability Density Components Analysis: A New Approach to Treatment and Classification of SAR Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 6; no. 4; pp. 2989 - 3019
• Main Authors: de Carvalho, Osmar Abilio, de Moraes, Luz Marilda, de Carvalho, Ana Paula Ferreira, Guimaraes, Renato Fontes, Silva, Cristiano Rosa, Gomes, Roberto Arnaldo Trancoso, Silva, Nilton Correia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2014
• Subjects: ALOS; ALOS (satellite); Amazon forest; Classification; Density; frequency histogram; Image classification; Land use; Landsat; Noise; Noise levels; Noise reduction; PALSAR; Radar; Rainforests; Remote sensing; Spatial analysis; Synthetic aperture radar
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs6042989

Speckle noise (salt and pepper) is inherent to synthetic aperture radar (SAR), which causes a usual noise-like granular aspect and complicates the image classification. In SAR image analysis, the spatial information might be a particular benefit for denoising and mapping classes characterized by a statistical distribution of the pixel intensities from a complex and heterogeneous spectral response. This paper proposes the Probability Density Components Analysis (PDCA), a new alternative that combines filtering and frequency histogram to improve the classification procedure for the single-channel synthetic aperture radar (SAR) images. This method was tested on L-band SAR data from the Advanced Land Observation System (ALOS) Phased-Array Synthetic-Aperture Radar (PALSAR) sensor. The study area is localized in the Brazilian Amazon rainforest, northern Rondônia State (municipality of Candeias do Jamari), containing forest and land use patterns. The proposed algorithm uses a moving window over the image, estimating the probability density curve in different image components. Therefore, a single input image generates an output with multi-components. Initially the multi-components should be treated by noise-reduction methods, such as maximum noise fraction (MNF) or noise-adjusted principal components (NAPCs). Both methods enable reducing noise as well as the ordering of multi-component data in terms of the image quality. In this paper, the NAPC applied to multi-components provided large reductions in the noise levels, and the color composites considering the first NAPC enhance the classification of different surface features. In the spectral classification, the Spectral Correlation Mapper and Minimum Distance were used. The results obtained presented as similar to the visual interpretation of optical images from TM-Landsat and Google Maps.