Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

Abstract

This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object level by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the α-Gaussian mean kernel. It allows one to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in support vector machines for the supervised classification of grasslands from southwest France. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for the classification of grasslands' management practices, while an inter-annual NDVI time series of Formosat-2 is used for old and young grasslands' discrimination. Results are compared to other existing pixel-and object-based approaches in terms of classification accuracy and processing time. The proposed method is shown to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints, and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands.

Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDAR Data

“…Based on Mücher et al [46] collecting more images over the growing season and incorporation of vegetation information from LiDAR data might be helpful in grasses differentiation. Multi-temporal analyses using satellite data were used by researchers to identify invasive species [50,51] or grasslands [33,52]. Separability of grassland classes were supported by characteristic phenological development of individual habitat classes as greenness or colouring in the blooming phase using optical RapidEye data and vegetation height and structure from radar backscatter using TerraSAR-X data [33].…”

Mapping Grassland Frequency Using Decadal MODIS 250 m Time-Series: Towards a National Inventory of Semi-Natural Grasslands

“…For example, the European CORINE Land Cover layer, which is produced every 6 years at a 1:100,000 scale by visual analysis of high spatial resolution Landsat or Sentinel images [21], was used to model farms with high nature value in Europe using a 1 × 1 km grid [7] and semi-natural grasslands in France using a 5 × 5 km grid [22]. Beyond these broad-scale maps based on the CORINE Land Cover layer, many studies based on automatic and fine-scale analyses have demonstrated the contribution of multi-temporal and high-spatial-resolution satellite data in discriminating grasslands from other LULC types [16,[23][24][25], characterizing forage quality [20], identifying agricultural practices [26] and mapping floristic variation in semi-natural grasslands [27][28][29]. However, discriminating semi-natural and temporary grasslands accurately remains a concern [23,30] due to the lack of temporal depth in remote sensing time-series and because a one-year observation is insufficient to discriminate between semi-natural and temporary grasslands [31].…”

“…In contrast, LULC mapping from MODIS data has been widely studied: as examples, global LULC maps have been produced annually since 2001 from MODIS MCD43A4 products [40] but computed at twice the spatial resolution (500 m); or a pan-European LULC map was derived for 2009 from an annual NDVI MODIS 250 m time series but without discriminating between permanent and temporary grasslands [41]. In addition, using MODIS data to monitor grasslands in heterogeneous landscapes is controversial: some authors argue that MODIS's spatial resolution (250 m) is too low compared to parcel sizes [1,16,24], while others stress that MODIS data can be used to identify areas dominated by grasslands but do not demonstrate it [36,39].…”

Learning a Multi-Branch Neural Network from Multiple Sources for Knowledge Adaptation in Remote Sensing Imagery

“…First, the earliest works in this regard tend to classify image pixels [3][4][5], typically by handling raw spectral values along with neighbouring attributes. The second approach is based on scene-level recognition [6][7][8], which has received interest recently, thanks to its property of offering broader semantic information.…”