Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach

While small, fragmented wooded elements do not represent a large surface area in agricultural landscape, their role in the sustainability of ecological processes is recognized widely. Unfortunately, landscape ecology studies suffer from the lack of methods for automatic detection of these elements. We propose a hybrid approach using both aerial photographs and ancillary data of coarser resolution to automatically discriminate small wooded elements. First, a spectral and textural analysis is performed to identify all the planted-tree areas in the digital photograph. Secondly, an object-orientated spatial analysis using the two data sources and including a multi-resolution segmentation is applied to distinguish between large and small woods, copses, hedgerows and scattered trees. The results show the usefulness of the hybrid approach and the prospects for future ecological applications

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Combining multiple resolutions into hierarchical representations for kernel-based image classification

Geographic object-based image analysis (GEOBIA) framework has gained increasing interest recently. Following this popular paradigm, we propose a novel multiscale classification approach operating on a hierarchical image representation built from two images at different resolutions. They capture the same scene with different sensors and are naturally fused together through the hierarchical representation, where coarser levels are built from a Low Spatial Resolution (LSR) or Medium Spatial Resolution (MSR) image while finer levels are generated from a High Spatial Resolution (HSR) or Very High Spatial Resolution (VHSR) image. Such a representation allows one to benefit from the context information thanks to the coarser levels, and subregions spatial arrangement information thanks to the finer levels. Two dedicated structured kernels are then used to perform machine learning directly on the constructed hierarchical representation. This strategy overcomes the limits of conventional GEOBIA classification procedures that can handle only one or very few pre-selected scales. Experiments run on an urban classification task show that the proposed approach can highly improve the classification accuracy w.r.t. conventional approaches working on a single scale.Comment: International Conference on Geographic Object-Based Image Analysis (GEOBIA 2016), University of Twente in Enschede, The Netherland

High-resolution optical and SAR image fusion for building database updating

This paper addresses the issue of cartographic database (DB) creation or updating using high-resolution synthetic aperture radar and optical images. In cartographic applications, objects of interest are mainly buildings and roads. This paper proposes a processing chain to create or update building DBs. The approach is composed of two steps. First, if a DB is available, the presence of each DB object is checked in the images. Then, we verify if objects coming from an image segmentation should be included in the DB. To do those two steps, relevant features are extracted from images in the neighborhood of the considered object. The object removal/inclusion in the DB is based on a score obtained by the fusion of features in the framework of Dempster–Shafer evidence theory