The Brazilian Savanna, also known as Cerrado, is considered a global hotspot for biodiversity conservation. The detailed mapping of vegetation types, called physiognomies, is still a challenge due to their high spectral similarity and spatial variability. There are three major ecosystem groups (forest, savanna, and grassland), which can be hierarchically subdivided into 25 detailed physiognomies, according to a well-known classification system. We used an adapted U-net architecture to process a WorldView-2 image with 2-m spatial resolution to hierarchically classify the physiognomies of a Cerrado protected area based on deep learning techniques. Several spectral channels were tested as input datasets to classify the three major ecosystem groups (first level of classification). The dataset composed of RGB bands plus 2-band enhanced vegetation index (EVI2) achieved the best performance and was used to perform the hierarchical classification. In the first level of classification, the overall accuracy was 92.8%. On the other hand, for the savanna and grassland detailed physiognomies (second level of classification), 86.1% and 85.0% were reached, respectively. As the first work that intended to classify Cerrado physiognomies in this level of detail using deep learning, our accuracy rates outperformed others that applied traditional machine learning algorithms for this task.
Image registration is an important operation in remote sensing applications that basically involves the identification of many control points in the images. As the manual identification of control points may be time-consuming and tedious several automatic techniques have been developed. This paper describes a system for automatic registration and mosaic of remote sensing images under development at the Division of Image Processing (National Institute for Space Research - INPE) and the Vision Lab (Electrical & Computer Engineering department, UCSB). Three registration algorithms, which showed potential for multisensor or temporal image registration, have been implemented. The system is designed to accept different types of data and information provided by the user which speed up the processing or avoid mismatched control points. Based on a statistical procedure used to characterize good and bad registration, the user can stop or modify the parameters and continue the processing. Extensive algorithm tests have been performed by registering optical, radar, multi-sensor, high-resolution images and video sequences. Furthermore, the system has been tested by remote sensing experts at INPE using full scene Landsat, JERS-1, CBERS-1 and aerial images. An online demo system, which contains several examples that can be carried out using web browser, is available.