HIERARCHICAL CLASSIFICATION OF DIFFERENT CROPS USING MEDIUM AND HIGH SPATIAL RESOLUTION IMAGES Fonseca-Luengo, D., de la Fuente-Sáiz, D., Fuentes-Peñailillo, F., and Ortega-Farías, S. Centro de Investigación y Transferencia en Riego y Agroclimatología (CITRA), Universidad de Talca, Chile. *sortega@utalca.cl dfonseca@utalca.cl 1 Introduction Results and Discussion Pattern recognition for image classification is a key objective that has been followed in the remote sensing area. This is due to the importance of crop identification for tasks such as: cadastral process, management policy, decision making, crop status, landcover evolution, production and social interaction, etc. The major availability of satellite images, among other uses, has allowed for the improvement of land cover maps in order to detect changes in temporal and spatial patterns. In this sense, different classification methods have been developed, which indicated that a combination of individual classifiers, called ensemble, can generate more accurate results than individual classifiers. One of them is the Random Forest (RF) classifier, which is a combination of numerous classification trees that provides a prediction considering the most frequent individual vote by each tree. The main goal of this research was to evaluate a hierarchical classification of agricultural landcover through high and medium spatial resolution satellite images, i.e. Spot 6/7 and Landsat-7 ETM+, in an area belonging to the Ancoa basin, Maule Region, Chile. This was made considering two RF classifiers structured to generate multi-level classes in function of the input image, and using the GEOBIA (Geographic Object Based Image Analysis) approach to improve the extraction of the features from training data. The GEOBIA approach allowed to generate a greater number of samples for training process, because each polygons (manually delineated in the cadastral step) were sub- divided in a group of segments that were used then as new samples. In this sense, a fine segmentation can be helpful to generate a great number of samples. OBB error for level 4 was greater (30%) than for level 3 (10%), which can be explain due that in the level 4 the classes can be more similar between them, generating errors in the separation process. Validation of classified maps was carried out using a confusion matrixes for each experiments. Overall accuracies for all experiments ranged between 70% and 93%, comparing to those classes generated with the validation ROIs delineated in the cadastral process. Comparing maps from L3 (Figure 2 (a)) and L4 (Figure 2 (c)), the main differences can be observed between the classes: Bare Soil and Urban; and Flooded rice and Crop. These classes were misclassificated due to incorporation of other similar spectral responses from additional classes of level 4. It is important to highlight, that in both Spot maps were correctly identified the class Flooded rice, which is an abundant crop in the west-south area of the study site (and confirmed with the cadaster step). Classified map for Landsat image (Figure 2 (b)) shows less details in the class recognition compared with map from degraded Spot images (to 15 meters), despite both have same spatial resolution. which could be attributed to the pansharpening step. In addition, zones with Urban class were misclassificated as Bare soil, and Forest class was misclassified by Natural vegetated areas and Crop. In contrast, classification with Landsat image was able to identify irrigation canals, but as a Natural vegetated due to the interaction between the surfaces of water, concrete and poor vegetation. Materials and Methods Depending on kind of input, specific classes in different levels (hierarchical levels) can be generated, i.e., level 3 for Landsat and level 4 for Spot image (shown in the next figure). Study site. The study site corresponds to an agricultural area of 98,000 ha belonging to the Ancoa basin, fed by the Ancoa reservoir, located in the Maule Region in the central valley of Chile. Satellite images and image processing. Satellite images collected by Landsat 7 ETM+ and Spot 7 sensors were used. Spot image was captured on December 20 of 2015, in the beginning of the highest water demand in the study site. While Landsat image was captured on December 22 of 2015. For the image processing, the spatial resolution of multispectral bands in both satellite images were increased using a pansharpening method, aiming to improve the extraction of the features. In addition, GEOBIA approach was implemented considering segments instead of pixels in the training and implementation processes using Simple Linear Iterative Clustering (SLIC) algorithm for segmentation process. Classifier. Ground true classes on the different bands were used to train a RF model to classify different crops, considering mean and standard deviation for features in segments from: multispectral bands, normalized difference vegetation index (NDVI), Tasseled Cap transformation, and Entropy. Two RF models were generated for both levels considering 100 trees for each one, i.e., Spot image for level 4 and degraded Spot until spatial resolution of Landsat for level 3. Finally, classification with Spot and Landsat images were evaluated. Validation. Accuracy assessment was carried out considering some ground true classes that were not used in the training step. Conclusions The proposed methodology aims to classify land covers with a GEOBIA approach at different hierarchical scales considering a training step using Spot 7 image with pansharpening and degradation treatments. This approach generated a classification model composed by two classifiers for two scale levels. In addition, the implementation of this model for Landsat images made it possible to achieve stable results. Similar results were generated in both maps from Spot images (original and pansharpened), where classes at level 3 were very consistent. Regarding Landsat, some misclassifications were found in classes: Urban, Bare soil, and Forest, which could be generated by a poor incorporation of information from the pansharpening step. Since only features from cadastral process were used, it is important to point out the possible improvements of this proposal considering extractions of multi-temporal features from other scenes in different phenological stages. This future work should aim to integrate the temporal behavior of the training data set in the classification model, which would add important features in the learning model. ACKNOWLEDGEMENTS. The research leading to this report was supported by the A2C2 Program "Adapting Agriculture to Climate Change", Facultad de Ciencias Agrarias, Universidad de Talca, Talca, Chile.