Jessica da Silva Costa

Pan-sharpening is the process of combining high-resolution panchromatic and low-resolution multispectral images to generate a high-resolution multispectral image. However, some pan-sharpening methods have poor preservation of spectral and spatial information. In this context, the aim of this study is to improve the pan-sharpening method based on a convolutional autoencoder and a guided filter (CAE-GF), by developing a new Convolutional Autoencoder (CAE) network. A CAE network was designed and trained to generate original panchromatic images from their spatially degraded versions. The trained network was used to improve the spatial details of the intensity component of the multispectral image obtained through the adaptive intensity-hue-saturation (AIHS) method. The pan-sharpening process is achieved by applying the multi-scale guided filter to improve the original PAN image using the enhanced intensity component and by injecting the detail map into the multispectral image. To analyze the preservation of the spectral and spatial information in the proposed method, full-reference and no-reference quality indices were calculated, and a visual analysis was performed. These analyses were compared with traditional pan-sharpening methods of component substitution. The results showed the developed method’s efficacy in generating Pan-sharpened QuickBird images.

Pan-sharpening (PS) consists of combining a high spatial resolution (HR) panchromatic image (PAN) and a low spatial resolution (LR) multispectral image (MS) to generate an MS-HR image. However, some PS methods have spectral and spatial distortions that influence subsequent analyses. Thus, this study aimed to develop a PS method based on convolutional autoencoder (CAE) for Landsat 8 images and evaluate its performance in calculating spectral indices. In the PS process, we trained a CAE network and used a multiscale-guided filter. The performance of the proposed method was analyzed using the Kolmogorov-Smirnov (K-S) statistic of the empirical cumulative distribution function (eCDF) between the values of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Moisture Index (NDMI) of the PS and MS-LR images. The results show that the proposed method is effective for calculating the indices. Therefore, we conclude that it has great potential for preserving the spatial information of the PAN image and the spectral information of the MS-LR image during the PS process for calculating spectral indices.

The Santa Catarina Southern Plateau is located in Southern Brazil and is a region that has gained considerable attention due to the rapid conversion of the typical landscape of natural grasslands and wetlands into agriculture, reforestation, pasture, and more recently, wind farms. This study’s main goal was to characterize the polarimetric attributes of the experimental quad-polarization acquisition mode of the Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR-2) for mapping seven land cover classes. The polarimetric attributes were evaluated alone and combined with SENTINEL-2A using a supervised classification method based on the Support Vector Machine (SVM) algorithm. The results showed that the intensity backscattering alone reached an overall classification accuracy of 37.48% and a Kappa index of 0.26. Interestingly, the addition of polarimetric features increased to 71.35% and 0.66, respectively. It shows that the use of polarimetric decomposition features was relatively efficient in discriminating land cover classes. SENTINEL-2A data alone performed better and achieved a weighted overall accuracy and Kappa index of 85.56% and 0.82. This increase was also significant for the Z-test. However, the addition of ALOS/PALSAR-2 derived features to SENTINEL-2A slightly improved accuracy and was marginally significant at a 95% confidence level only when all features were considered. Possible implications for that performance are the accumulated precipitation prior to SAR data acquisition, which coincides with the rainy season period. The experimental quad-polarization mode of ALOS/PALSAR- 2 shall be evaluated in the near future over different seasonal conditions to confirm results. Alternatively, further studies are then suggested by focusing on additional features derived from SAR data such as texture and interferometric coherence to increase classification accuracy. These measures would be an interesting data source for monitoring specific land cover classes such as the threatened grasslands and wetlands during periods of frequent cloud coverage. Future investigations could also address multitemporal approaches employing either single or multifrequency SAR.

This study aimed to diagnose the environmental infractions that occurred in the northern region of Santa Catarina State (Southern Brazil). The environmental infractions were classified by category and by violation activities concerning the hydrographic regions of Canoinhas Plateau and Baixada Norte from 2014 to 2018 to assess the growth and causes of a diagnosis regarding the environmental damages occurrences. Results indicated that the major illegal environmental practices registered in both region are related to environmental violations regarding the damage caused in native vegetation, and these damages are caused without authorization of the competent environmental authority. In addition, it was also important to note the activity of maintaining birds in captivity and of functioning of potentially pollutant activities or establishments that do not have a license of the competent regional environmental agencies. Therefore, efforts from the Environmental Policy should be directed to implement a forest monitoring system using remote sensing technology apart from starting a stronger cooperation with municipal authorities from Santa Catarina State by promoting Environmental Monitoring Programs (EMP).