Abstract
There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image often cannot be extrapolated to a different image. Recently, deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in computer vision and are offering promising results in land cover mapping. This paper analyzes the potential of CNN-based methods for detection of plant species of conservation concern using free high-resolution Google Earth TM images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. Compared to the best performing OBIA-method, the best CNN-detector achieved up to 12% better precision, up to 30% better recall and up to 20% better balance between precision and recall. Besides, the knowledge that CNNs acquired in the first image can be re-utilized in other regions, which makes the detection process very fast. A natural conclusion of this work is that including CNN-models as classifiers, e.g., ResNet-classifier, could further improve OBIA methods. The provided methodology can be systematically reproduced for other species detection using our codes available through (https://github.com/EGuirado/CNN-remotesensing).
Highlights
Changes in land cover and land use are pervasive, rapid, and can have significant impact on humans, the economy, and the environment
From the multiple techniques that we explored, the ones that provided the best detection performance were: (i) Eliminating the background using a threshold based on its typical color or darkness. (ii) Applying an edge-detection method that filters out the objects with an area or perimeter smaller than the minimum size of the target objects
We explored, analyzed and compared two detection methodologies for shrub mapping, the Object-Based Image Analysis (OBIA)-based approach and the Convolutional Neural Networks (CNNs)-based approach
Summary
Changes in land cover and land use are pervasive, rapid, and can have significant impact on humans, the economy, and the environment. Convolutional Neural Networks (CNNs)-based models have demonstrated impressive accuracies in object recognition and image classification in the field of computer vision ([13,14,15,16] and are starting to be used in the field of remote sensing ([17]) This success is due to the availability of larger training datasets, better algorithms, improved network architectures, faster GPUs and improvement techniques such as data-augmentation and transfer-learning, which allow reutilization of the knowledge acquired from a set of images into other new images. This paper analyzes the potential of CNN-based methods for plant species mapping using high-resolution Google EarthTM images and provides an objective comparison with the state-of-the-art OBIA-based methods As case study, it aims to map Ziziphus lotus shrubs, the dominant species of the European priority conservation habitat “Arborescent matorral with Ziziphus”, which is experiencing a serious decline in Europe during recent decades ([19]) (though it is present in North Africa and Middle East).
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