Pixel-based classification is a complex but well-known process widely used for satellite imagery classification. This paper presents a supervised multi-classifier pipeline that combined multiple Earth Observation (EO) data and different classification approaches to improve specific land cover type identification. The multi-classifier pipeline was tested and applied within the SCO-Live project that aims to use olive tree phenological evolution as a bio-indicator to monitor climate change. To detect and monitor olive trees, we classify satellite images to precisely locate the various olive groves. For that first step we designed a multi-classifier pipeline by the concatenation of a first classifier which uses a temporal Random-Forest model, providing an overall classification, and a second classifier which uses the result from the first classification. IOTA2 process was used in the first classifier, and we compared Multi-layer Perceptron (MLP) and One-class Support Vector Machine (OCSVM) for the second. The multi-classifier pipelines managed to reduce the false positive (FP) rate by approximately 40% using the combination RF/MLP while the RF/OCSVM combination lowered the FP rate by around 13%. Both approaches slightly raised the true positive rate reaching 83.5% and 87.1% for RF/MLP and RF/OCSVM, respectively. The overall results indicated that the combination of two classifiers pipeline improves the performance on detecting the olive groves compared to pipeline using only one classifier.

LIDAR is one of remote sensing technologies to measure the distance between the sensor and objects (e.g. pedestrians, vehicles) with pulsed laser light, accurately. Because of its robustness to dynamic lighting conditions and obtainable high spatial resolution, recognition methods using LIDAR have a good capability in object recognition. This is the reason why utilizing LIDAR for autonomous vehicle and/or supporting safety driving systems. In this paper, we focus on a vehicle detection method using LIDAR, and assess the feature descriptors, including two kinds of our proposal descriptors, for point cloud data. Furthermore, we validate appropriate feature descriptors using Real AdaBoost algorithm.