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Remote sensing has been widely employed for monitoring land cover and usage by change detection techniques. In this paper, we cope with the early detection of the first signs of deforestation, which is the gateway for illegal activities, such as unauthorized urban sprawl and grazing use. In recent years, object-based approaches have emerged as a more suitable alternative than pixel-based methods for change detection in remote sensing images. Even though several classifiers have been tested, there was little effort in selecting appropriated features for the classification of detected changes. After a deep analysis of the existing segmentation, feature extraction, and classification approaches, we propose an object-based methodology that consists of: 1) segmenting multitemporal Xband high-resolution synthetic aperture radar (SAR) images into superpixels employing the simple linear iterative clustering algorithm; 2) extracting features using the object correlation images framework and with the gray-level cooccurrence matrix; and 3) classifying areas into unchanged, deforestation, and other changes by means of a multilayer perceptron supervised learning technique. Experiments were performed using high-resolution SAR images obtained by the airborne sensor OrbiSAR-2 from BRADAR in challenging scenarios of the Brazilian Atlantic Forest, including a wide variety of vegetation, rivers, sea coasts, urban, harvest and open areas, and humidity changes. We perform an extensive experimental analysis of the results, comparing the proposed method with a state-of-the-art approach. The results demonstrate that our method yields an improvement of over 10% in the accuracy while detecting changes and classifying deforested areas. [ABSTRACT FROM PUBLISHER]

There are basically two methods for retrieving the ground and the canopy height from interferometric synthetic aperture radar (InSAR) in forested areas. The first method is based on the difference between InSAR height estimations from dual-band (DB) systems, typically operating at X- and P-bands HH. The second method is based on the modeling of the polarimetric (Pol) InSAR response along the forest vertical structure, typically at L- or P-band. This paper proposes the combination of both methods, so that the ground and total tree height estimations are improved and become available alongside the interferometric forest height with the usage of polarimetric data and the RVoG model adoption in both bands. In the proposed method (DB-PolInSAR), first, the ground phase is retrieved from the RVoG inversion through a straightforward line fit of the P-Band polarimetric data in the complex plane. Fixing the ground height coming from the previous P-band inversion and applying the RVoG model to the X-band interferometric data, we estimate the total tree height. Repeat-pass dual-polarimetric (HH and HV) P-band data and single-pass three-baseline HH X-band data acquired with the airborne OrbiSAR sensor of Bradar over the Amazon region of Urucu are used to demonstrate the proposed method. Comparisons between the dual-band PolInSAR and the dual-band single-polarization cases are performed for five different P-band single-baseline configurations. Better ground estimation over range is obtained with the proposed method. Furthermore, the three-antenna single-pass X-band data enabled a robust RVoG total tree height inversion. [ABSTRACT FROM PUBLISHER]

We present in this work a first assessment of the imaging and topographic mapping capabilities of the InSAeS4 system, which is a single-pass interferometric airborne X-Band Synthetic Aperture Radar (SAR). In particular, we first provide a brief description of the InSAeS4 sensor. Then, we discuss the results of our analysis on the SAR and interferometric SAR products relevant to the first flight-test campaign. More specifically, we have exploited as reference the GPS measurements relevant to nine Corner Reflectors (CRs) deployed over the illuminated area during the campaign and a laser scanner Digital Elevation Model (DEM). From the analysis carried out on the CRs we achieved a mean geometric resolution, for the SAR products, of about 0.14 m in azimuth and 0.49 m in range, a positioning misalignment with standard deviation of 0.07 m in range and 0.08 m in azimuth, and a height error with standard deviation of 0.51 m. From the comparison with the laser scanner DEM we estimated a height error with standard deviation of 1.57 m. [ABSTRACT FROM AUTHOR]

Digital elevation model (DEM) generation through interferometric processing of synthetic aperture radar (SAR) data requires the calculation of a constant phase offset present in the unwrapped interferograms. This operation is usually carried out by exploiting the external information provided by GPS measurements in correspondence of corner reflectors (CRs) properly deployed over the illuminated area. This is, however, expensive in terms of cost and time. Moreover, deployment of CRs along with the corresponding in situ GPS measurements can be difficult (if not impossible) in unfriendly areas or in natural disaster scenarios. To circumvent these limitations, we address in this work the estimation of the required phase offset by exploiting a low-accuracy external DEM, without using CRs. More specifically, a two-step approach is proposed. The first step exploits the synthetic phase computed by means of the external DEM and represents a straightforward extension of the procedure that is usually applied in the presence of CRs. Subsequently, in order to refine the achieved solution, a second step is introduced. It is based on a least squares approach that properly exploits the difference between the available low-accuracy DEM and the interferometric DEM generated by means of the phase offset value roughly estimated through the first step. The presented approach is very easy to implement and allows us to achieve an accurate and fast estimate of the needed phase offset, even in the presence of an externalDEM affected by a vertical bias and/or a planar shift. The algorithm performances improve in the presence of a large variation of the look angle, as it generally happens in airborne systems. On the other side, the effectiveness of the algorithm may be impaired by the possible presence of artifacts in the unwrapped interferograms, such as those due to the residual motion errors typical of repeat-pass airborne SAR scenarios. Accordingly, the proposed solution is particularly suitable for single-pass interferometric airborne SAR systems, as demonstrated through the presented experimental results achieved on real data. [ABSTRACT FROM AUTHOR]

Differential synthetic aperture radar interferometry (DInSAR) has been widely applied since the pioneering space-borne experiment in 1989, and subsequently with the launch of the ERS-1 program in 1992. The DInSAR technique is well assessed in the case of space-borne SAR data, whereas in the case of data acquired from aerial platforms, such as airplanes, helicopters, and drones, the effective application of this technique is still a challenging task, mainly due to the limited accuracy of the information provided by the navigation systems mounted onboard the platforms. The first airborne DInSAR results for measuring ground displacement appeared in 2003 using L- and X-bands. DInSAR displacement results with long correlation time in P-band were published in 2011. This letter presents a SAR system and, to the best of our knowledge, the first accuracy assessment of the DInSAR technique using a drone-borne SAR in L-band. A deformation map is shown, and the accuracy and resolution of the methodology are presented and discussed. In particular, we have obtained an accuracy better than 1 cm for the measurement of the observed ground displacement. It is in the same order as that achieved with space-borne systems in C- and X-bands and the airborne systems in X-band. However, compared to these systems, we use here a much longer wavelength. Moreover, compared to the satellite experiments available in the literature and aimed at assessing the accuracy of the DInSAR technique, we use only two flight tracks with low time decorrelation effects and not a big data stack, which helps in reducing the atmospheric effects. [ABSTRACT FROM AUTHOR]