GEOBIA at the Terapixel Scale: Toward Efficient Mapping of Small Woody Features from Heterogeneous VHR Scenes

Abstract

Land cover mapping has benefited a lot from the introduction of the Geographic Object-Based Image Analysis (GEOBIA) paradigm, that allowed to move from a pixelwise analysis to a processing of elements with richer semantic content, namely objects or regions. However, this paradigm requires to define an appropriate scale, that can be challenging in a large-area study where a wide range of landscapes can be observed. We propose here to conduct the multiscale analysis based on hierarchical representations, from which features known as differential attribute profiles are derived over each single pixel. Efficient and scalable algorithms for construction and analysis of such representations, together with an optimized usage of the random forest classifier, provide us with a semi-supervised framework in which a user can drive mapping of elements such as Small Woody Features at a very large area. Indeed, the proposed open-source methodology has been successfully used to derive a part of the High Resolution Layers (HRL) product of the Copernicus Land Monitoring service, thus showing how the GEOBIA framework can be used in a big data scenario made of more than 38,000 Very High Resolution (VHR) satellite images representing more than 120 TB of data.

Classification of Remote Sensing Data With Morphological Attribute Profiles: A decade of advances

“…The experiments described in the previous section follow the same approach of other experiments in the literature: the training and testing features are obtained from the same tree computed on the whole data (or on the principal components of the data). This approach is reasonable when the aim is to completely classify the pixels of an image whose annotated pixels are evenly spread across this image [12]. That was the case of the training sets considered previously, which allowed us to obtain an improvement of more than 30% in terms of classification accuracy using APs with respect to spectral pixel values.…”

“…Furthermore, APs can be generated efficiently through quasi-linear [9] and parallel [10] algorithms, through their input's tree-based hierarchical representation [11], thus equipping them with a high level of scalability; an invaluable property in remote sensing, where gigapixel images are becoming the norm. In fact, [12] presents an application of AP at a terapixel scale! It is thus not surprising that since their introduction ten years ago, a great number of AP related publications have appeared (4540 at Google Scholar as of June 2020), tackling various aspects of remote sensing image analysis.…”

Broceliande: A Comparative Study of Attribute Profiles and Feature Profiles From Different Attributes

“…Recently, area attribute has been used for the classification of small woody features (Merciol et al, 2019). In this study, we will use a similar data, the high resolution Podgorica image, plus the Reykjavik dataset to compare the different attributes with the FPs.…”

“…The threshold values adopted in the filtering stage were selected manually. For the area filtering, we considered the same set of threshold values used in the computation of FPs in (Pham et al, 2017a) for the Pavia data, and the threshold values used in (Merciol et al, 2019) for the Podgorica data:…”

Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation

“…The OBIA methods usually use segmentation algorithm to obtain the objects first and then use the objects for subsequent image analysis. Currently, the OBIA methods are widely applied in multi-scale research [15,16], change detection [17] and landslide detection [18]. To better understand ecological patterns, it is also expanded to the species-level mapping of vegetation [19].…”