No abstract

No abstract

Object recognition from remote sensing systems is a task of immense interest. With the vast deployment of aerial vehicles and space borne sensors for a wide variety of purposes, it is critical to have robust image processing techniques to analyze massive streams of collected data. Herein, we explore the utility of a feature descriptor learning framework, called improved Evolution-COnstructed (iECO) features. Additionally, an investigation into the combination of iECO features with soft features is conducted. Soft features are a deterministic approach to highlighting pertinent information for improving the quality of features extracted specific to the object of interest while iECO is a way to learn from data the relevant information. Experiments are conducted using four-fold (scene based) cross-validation and are reported in terms of target recognition rates and false alarm rates. Results indicate that iECO features are individually best overall and the combination of iECO and soft features can lead to improved results.

No abstract