Models for Synthetic Aperture Radar Image Analysis

After reviewing some classical statistical hypothesis commonly used in image processing and analysis, this paper presents some models that are useful in synthetic aperture radar (SAR) image analysis

Evaluation of Digital Classification of Polarimetric SAR Data for Iron-Mineralized Laterites Mapping in the Amazon Region

This study evaluates the potential of C- and L-band polarimetric SAR data for the discrimination of iron-mineralized laterites in the Brazilian Amazon region. The study area is the N1 plateau located on the northern border of the Carajás Mineral Province, the most important Brazilian mineral province which has numerous mineral deposits, particularly the world’s largest iron deposits. The plateau is covered by low-density savanna-type vegetation (campus rupestres) which contrasts visibly with the dense equatorial forest. The laterites are subdivided into three units: chemical crust, iron-ore duricrust, and hematite, of which only the latter two are of economic interest. Full polarimetric data from the airborne R99B sensor of the SIVAM/CENSIPAM (L-band) system and the RADARSAT-2 satellite (C-band) were evaluated. The study focused on an assessment of distinct schemes for digital classification based on decomposition theory and hybrid approach, which incorporates statistical analysis as input data derived from the target decomposition modeling. The results indicated that the polarimetric classifications presented a poor performance, with global Kappa values below 0.20. The accuracy for the identification of units of economic interest varied from 55% to 89%, albeit with high commission error values. In addition, the results using L-band were considered superior compared to C-band, which suggest that the roughness scale for laterite discrimination in the area is nearer to L than to C-band

Classificador por regiões de imagens SAR com base em distâncias estocásticas derivadas da densidade de probabilidade do par de intensidades multi-look

The development of techniques for synthetic aperture radar (SAR) image classification has advanced significantly, especially when one observes the pixel based and contextual classifiers approach. Region based classification using stochastic distances derived from Information Theory may represent a significant improvement with respect to pixel based classification methods, mainly due to effects caused by speckle noise. This work presents a region based classifier for intensity pair SAR images. This classifier uses the Bhattacharyya distance derived from the multi-look intensity pair density function. Two multi-polarized intensity images (HH and HV) from a tropical forest area (Tapajós, PA) were used as data source. The stochastic distance is computed numerically and follows a methodology for obtaining the class of h- divergences. The classifier was implemented using the Interactive Data Language (IDL v. 7.1), and the classification was performed on an image segmented by the software SPRING 5.1.5. As a result, the classified image obtained a kappa coefficient of 0.95, from what can be concluded that it offers a good potential in identifying the land cover classes of the study area.Pages: 8326-833

Carbon budget estimation in Central Amazonia: Successional forest modeling from remote sensing data

The carbon budget resulting from the dynamics of forest vegetation was estimated spatially for a study region with intensive land use change in the Central Amazonia forest. Vegetation height was recovered from airborne SAR interferometry, and was used along with an established relationship between forest height and age for mapping the successional stages of vegetation. A map of forest ages could be generated and validated (age RMSE was 3.5 years). Biomass stocks and annual rates of increment in biomass could be attributed to the forest ages by a comprehensive growth model for forests in the study area. A conceptual model of land use change was developed for the study area that accounts for four different types of land use: primary forest, secondary forest, degraded forest and nonforest. The transition probabilities between those land use types were recovered from internal modeling of available data, from literature sources, and from large-scale remote sensing results. The land use change matrix, area-age densities of secondary forests, and a growth model, yield a spatialized estimate of the carbon budget. The committed emissions from annual land use change were computed. For the year 2000-2001 the carbon balance was negative, on an area of ca. 5700 ha, land use dynamics resulted in a release of approximately 16,000 t of carbon, mainly arising from the cutting of primary forest for agricultural purposes. The secondary forest carbon budget was almost balanced, and forest degradation was revealed less important. © 2004 Elsevier Inc. All rights reserved

Relating Amazon forest biomass to PolInSAR extracted features

The combination of polarimetric SAR (PolSAR) with the interferometry capability (InSAR) enables the extraction of new features that enhances the development of biomass estimation models. This work aims in demonstrating the importance of the simultaneous use of several types of SAR features for estimating forest biomass. The study site is São Gabriel da Cachoeira, located in the Brazilian Amazon. Forest inventory was conducted by INPA and a sample of 29 plots have been used to compute above and below-ground biomass. Polarimetric interferometric X and P band SAR data (PolInSAR) were acquired by the DSG's Amazon Radiography Project. After the initial analysis of the data and feature extraction, analyses on the relationship between biomass and SAR features have been done. Only 4 out of the 122 features extracted presented a significant correlation with biomass and each of them were related to a structural characteristic of the forest. © 2013 IEEE