Leila Maria Garcia Fonseca

Automatic registration of satellite images

Image registration is one of the basic image processing operations in remote sensing. With the increase in the number of images collected every day from different sensors, automated registration of multi-sensor/multi-spectral images has become an important issue. A wide range of registration techniques has been developed for many different types of applications and data. Given the diversity of the data, it is unlikely that a single registration scheme will work satisfactorily for all different applications. A possible solution is to integrate multiple registration algorithms into a rule-based artificial intelligence system, so that appropriate methods for any given set of multisensor data can be automatically selected. The objective of this paper is to present an automatic registration algorithm which has been developed at INPE. It uses a multiresolution analysis procedure based upon the wavelet transform. The procedure is completely automatic and relies on the grey level information content of the images and their local wavelet transform modulus maxima. The algorithm was tested on SPOT and TM images from forest, urban and agricultural areas. In all cases we obtained very encouraging results.

Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis

Detailed, up-to-date information on intra-urban land cover is important for urban planning and management. Differentiation between permeable and impermeable land, for instance, provides data for surface run-off estimates and flood prevention, whereas identification of vegetated areas enables studies of urban micro-climates. In place of maps, high-resolution images, such as those from the satellites IKONOS II, Quickbird, Orbview and WorldView II, can be used after processing. Object-based image analysis (OBIA) is a well-established method for classifying high-resolution images of urban areas. Despite the large number of previous studies of OBIA in the context of intra-urban analysis, there are many issues in this area that are still open to discussion and resolution. Intra-urban analysis using OBIA can be lengthy and complex because of the processing difficulties related to image segmentation, the large number of object attributes to be resolved and the many different methods needed to classify various image objects. To overcome these issues, we performed an experiment consisting of land-cover mapping based on an OBIA approach using an IKONOS II image of a southern sector of São José dos Campos city (covering an area of 12 km2 with 50 neighbourhoods), which is located in São Paulo State in south-eastern Brazil. This area contains various occupation and land-use patterns, and it therefore contains a wide range of intra-urban targets. To generate the land-cover map, we proposed an OBIA-based processing framework that combines multi-resolution segmentation, data mining and hierarchical network techniques. The intra-urban land-cover map was then evaluated through an object-based error matrix, and classification accuracy indices were obtained. The final classification, with 11 classes, achieved a global accuracy of 71.91%.

Nature-Inspired Framework for Hyperspectral Band Selection

Although hyperspectral images acquired by on-board satellites provide information from a wide range of wavelengths in the spectrum, the obtained information is usually highly correlated. This paper proposes a novel framework to reduce the computation cost for large amounts of data based on the efficiency of the optimum-path forest (OPF) classifier and the power of metaheuristic algorithms to solve combinatorial optimizations. Simulations on two public data sets have shown that the proposed framework can indeed improve the effectiveness of the OPF and considerably reduce data storage costs.

Image Fusion for Remote Sensing Applications

Remote Sensing systems, particularly those deployed on satellites, provide a repetitive and consistent view of the Earth (Schowengerdt, 2007). To meet the needs of different remote sensing applications the systems offer a wide range of spatial, spectral, radiometric and temporal resolutions. Satellites usually take several images from frequency bands in the visual and non-visual range. Each monochrome image is referred to as a band and a collection of several bands of the same scene acquired by a sensor is called multispectral image (MS). A combination of three bands associated in a RGB (Red, Green, Blue) color system produce a color image. The color information in a remote sensing image by using spectral band combinations for a given spatial resolution increases information content which is used in many remote sensing applications. Otherwise, different targets in a single band may appear similar which makes difficult to distinguish them. Different bands can be acquired by a single multispectral sensor or by multiple sensors operating at different frequencies. Complementary information about the same scene can be available in the following cases (Simone et al., 2002):  Data recorded by different sensors;  Data recorded by the same sensor operating in different spectral bands;  Data recorded by the same sensor at different polarization;  Data recorded by the same sensor located on platforms flying at different heights. In general, sensors with high spectral resolution, characterized by capturing the radiance from different land covers in a large number of bands of the electromagnetic spectrum, do not have an optimal spatial resolution, that may be inadequate to a specific identification task despite of its good spectral resolution (Gonzalez-Audicana, 2004). On a high spatial resolution panchromatic image (PAN), detailed geometric features can easily be recognized, while the multispectral images contain richer spectral information. The capabilities of the images can be enhanced if the advantages of both high spatial and spectral resolution can be integrated into one single image. The detailed features of such an integrated image thus can be easily recognized and will benefit many applications, such as urban and environmental studies (Shi et al., 2005).

GeoDMA - A Novel System for Spatial Data Mining

Although a huge amount of remote sensing data has been provided by Earth observation satellites, few data manipulation techniques and information extraction in large data sets have been developed. In this context, the present paper aims to show a new system for spatial data mining, and two test cases applied to land use change in the Brazilian Amazon region. We present the operational environment named GeoDMA, developed to implement such approach.

Combined interpolation—restoration of Landsat images through FIR filter design techniques

Abstract In digital image processing for remote sensing there is often a need to interpolate an image. Examples occur in scale magnification, image registration, geometric correction, etc. On the other hand, this image can be subject to several sources of degradation and it would be interesting to compensate also for this degradation in the interpolation process. Therefore, this article addresses the problem of combining interpolation and restoration in a single operation, thereby reducing the computational effort. This is done by means of two-dimensional, separable, Finite Impulse Response (FIR) filters. The ideal low pass FIR filter for interpolation is modified to account for the restoration process. The Modified Inverse Filter (M1F) and the Wiener Filter (WF) are used for this purpose. The proposed methods are applied to the interpolation-restoration of Landsat-5 Thematic Mapper data. The later process takes into account the degradation due to optics, detector and electronic filtering. A comparison wi...

Convex restriction sets for CBERS-2 satellite image restoration

The main goal of this work is to develop a new efficient image restoration algorithm based on projections onto convex sets technique (POCS), which uses some a priori information about the images in the form of restriction sets. The proposed convex formulation used in this work is the prototype image constraints, which was obtained by the modified inverse filter, limited amplitude and by the modified row‐action projection (MRAP) algorithm. A simulation experiment was performed using a high‐resolution IKONOS image, which was blurred and decimated according to CBERS‐2 CCD camera specifications. In order to allow quantitative analysis, the ISNR and the universal image quality index methodologies were applied. An original CBERS‐2 CCD image was also used to evaluate the proposed restoration method. The restored images show a good visual performance, which can also be observed by the autocorrelation coefficients, which indicate high‐frequency enhancement.

Urban population estimation based on residential buildings volume using IKONOS-2 images and lidar data

ABSTRACT This paper presents a methodological approach to estimation of urban population using the volume of single houses and high-rise residential buildings obtained from an IKONOS-2 ortho-image and light detection and raging (lidar) data. The estimates are directly executed at the finest scale level (i.e. the housing unit) and are then aggregated at the census district level for further validation with the aid of official data supplied by the local and federal governments. Unlike prior works, this study executes a thorough assessment of horizontal and elevation accuracy for the IKONOS-2 and lidar data used in the experiment. The methodological stages are threefold: the construction of a 3D city model, the accuracy assessment of the ortho-image and digital surface models (DSMs), and the quantification of urban population. The validation was accomplished by means of linear regression and associated hypothesis tests, considering the estimated population and the reference data. The results showed that there was a systematic underestimation of population. On average, the conducted estimates assessed 31 fewer inhabitants per district and lie 1.35% below the expected values given by the reference data. In spite of the observed underestimation, the estimated population can be regarded as equivalent to the population provided by the reference data at a 1% level of significance.

A Resegmentation Approach for Detecting Rectangular Objects in High-Resolution Imagery

Image segmentation covers techniques for splitting one image into its components as homogeneous regions. This letter presents a resegmentation approach applied to urban images. Resegmentation represents the set of adjustments from a previous segmentation in which the elements are small regions with a high degree of spectral similarity (a condition known as oversegmentation). The focus of this letter is the house roofs, which are assumed to have a rectangular shape. These regions are merged according to an objective function, which, in the technique presented here, maximizes the rectangularity. With oversegmentation, we create a graph known as a region adjacency graph (RAG) that relates border elements. The main contribution of this letter is a technique, which works with the RAG, to maximize the objective function in a relaxationlike approach that splits and merges oversegmented regions until they form a meaningful object. The results showed that the method was able to detect rectangles according to user-defined parameters, such as the maximum level of the graph depth and the minimum degree of rectangularity for objects of interest.

Projections Onto Convex Sets through Particle Swarm Optimization and its application for remote sensing image restoration

Image restoration attempts to enhance images corrupted by noise and blurring effects. Iterative approaches can better control the restoration algorithm in order to find a compromise of restoring high details in smoothed regions without increasing the noise. Techniques based on Projections Onto Convex Sets (POCS) have been extensively used in the context of image restoration by projecting the solution onto hyperspaces until some convergence criteria be reached. It is expected that an enhanced image can be obtained at the final of an unknown number of projections. The number of convex sets and its combinations allow designing several image restoration algorithms based on POCS. Here, we address two convex sets: Row-Action Projections (RAP) and Limited Amplitude (LA). Although RAP and LA have already been used in image restoration domain, the former has a relaxation parameter (@l) that strongly depends on the characteristics of the image that will be restored, i.e., wrong values of @l can lead to poorly restoration results. In this paper, we proposed a hybrid Particle Swarm Optimization (PSO)-POCS image restoration algorithm, in which the @l value is obtained by PSO to be further used to restore images by POCS approach. Results showed that the proposed PSO-based restoration algorithm outperformed the widely used Wiener and Richardson-Lucy image restoration algorithms.