Region-growing segmentation algorithms are useful for remote sensing image segmentation. These algorithms need the user to supply control parameters, which control the quality of the resulting segmentation. This letter proposes an objective function for selecting suitable parameters for region-growing algorithms to ensure best quality results. It considers that a segmentation has two desirable properties: each of the resulting segments should be internally homogeneous and should be distinguishable from its neighbourhood. The measure combines a spatial autocorrelation indicator that detects separability between regions and a variance indicator that expresses the overall homogeneity of the regions.

This work describes the development of a simple method of field estimating the sky cloud coverage percentage for several applications at the Brazilian Antarctic Station, Ferraz (62°05′S, 58°23.5′W). The database of this method was acquired by a digital color camera in the visible range of the spectrum. A new algorithm was developed to classify each pixel according to a criteria decision process. The information on the pixel contamination by clouds was obtained from the saturation component of the intensity, hue, and saturation space (IHS). For simplicity, the images were acquired with a limited field of view of 36° pointing to the camera’s zenith to prevent direct sunlight from reaching the internal charge-coupled device (CCD) on the camera. For a priori–classified clear-sky images, the accuracy of the method was superior to 94%. For overcast-sky conditions, the corresponding accuracy was larger than 99%. A comparison test was performed with two human observers and our method. The results for the 29 images collected for several time of days during 50 days in 1999 summer were compared to visual observations of these same digital images by two trained field meteorologists. Correlation coefficients between human observers and the automatic method ranged from 0.84 for clear-sky conditions, and the lowest was 0.09 for undefined-sky conditions.

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Accurate, high-resolution maps of for crop growth monitoring are strongly needed by precision agriculture. The information source for such maps has been supplied by satellite-borne radars and optical sensors, and airborne and drone-borne optical sensors. This article presents a novel methodology for obtaining growth deficit maps with an accuracy down to 5 cm and a spatial resolution of 1 m, using differential synthetic aperture radar interferometry (DInSAR). Results are presented with measurements of a drone-borne DInSAR operating in three bands—P, L and C. The decorrelation time of L-band for coffee, sugar cane and corn, and the feasibility for growth deficit maps generation are discussed. A model is presented for evaluating the growth deficit of a corn crop in L-band, starting with 50 cm height. This work shows that the drone-borne DInSAR has potential as a complementary tool for precision agriculture.

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.