Geo-parcel-based crop classification in very-high-resolution images via hierarchical perception

The basic application of remote sensing is classifying surface objects in images. Traditional pixel-based or object-based classification methods are poorly suited to very high-resolution (VHR) images captured by remote sensors with high spatial resolutions. In the field of computer vision, deep learning has recently achieved great advances in natural image processing. Inspired by this, we propose a methodology guided by hierarchical perception to classify crops in VHR images based on geo-parcels. Geo-parcel-based crop classification is used in agriculture and in refined farmland management. The proposed methodology can be divided into three steps: zoning, location and quality. In the first step, the image is divided into blocks based on the road network. In the second step, geographical entities are extracted from every block defined in the zoning step. In the last step, the geographical entity types are identified based on the texture information. These steps provide mutual constraints. In each step, the information is extracted by neural networks that have been adapted to the VHR images. The experimental results indicate that our methodology performs well, with a precision greater than 90%. Furthermore, our methodology combines deep learning techniques and theory regarding image perception by humans, providing a valuable method for processing remote sensing information.