Automatic mapping of linear woody vegetation features in agricultural landscapes using very high resolution imagery

Cataloged from PDF version of article.Automatic mapping and monitoring of agricultural landscapes using remotely sensed imagery has been an important research problem. This paper describes our work on developing automatic methods for the detection of target landscape features in very high spatial resolution images. The target objects of interest consist of linear strips of woody vegetation that include hedgerows and riparian vegetation that are important elements of the landscape ecology and biodiversity. The proposed framework exploits the spectral, textural, and shape properties of objects using hierarchical feature extraction and decision-making steps. First, a multifeature and multiscale strategy is used to be able to cover different characteristics of these objects in a wide range of landscapes. Discriminant functions trained on combinations of spectral and textural features are used to select the pixels that may belong to candidate objects. Then, a shape analysis step employs morphological top-hat transforms to locate the woody vegetation areas that fall within the width limits of an acceptable object, and a skeletonization and iterative least-squares fitting procedure quantifies the linearity of the objects using the uniformity of the estimated radii along the skeleton points. Extensive experiments using QuickBird imagery from three European Union member states show that the proposed algorithms provide good localization of the target objects in a wide range of landscapes with very different characteristics

RegGAN: An End-to-End Network for Building Footprint Generation with Boundary Regularization

Accurate and reliable building footprint maps are of great interest in many applications, e.g., urban monitoring, 3D building modeling, and geographical database updating. When compared to traditional methods, the deep-learning-based semantic segmentation networks have largely boosted the performance of building footprint generation. However, they still are not capable of delineating structured building footprints. Most existing studies dealing with this issue are based on two steps, which regularize building boundaries after the semantic segmentation networks are implemented, making the whole pipeline inefficient. To address this, we propose an end-to-end network for the building footprint generation with boundary regularization, which is termed RegGAN. Our method is based on a generative adversarial network (GAN). Specifically, a multiscale discriminator is proposed to distinguish the input between false and true, and a generator is utilized to learn from the discriminator’s response to generate more realistic building footprints. We propose to incorporate regularized loss in the objective function of RegGAN, in order to further enhance sharp building boundaries. The proposed method is evaluated on two datasets with varying spatial resolutions: the INRIA dataset (30 cm/pixel) and the ISPRS dataset (5 cm/pixel). Experimental results show that RegGAN is able to well preserve regular shapes and sharp building boundaries, which outperforms other competitors

Noise-Tolerant Deep Neighborhood Embedding for Remotely Sensed Images With Label Noise

Recently, many deep learning-based methods have been developed for solving remote sensing (RS) scene classification or retrieval tasks. Most of the adopted loss functions for training these models require accurate annotations. However, the presence of noise in such annotations (also known as label noise) cannot be avoided in large-scale RS benchmark archives, resulting from geo-location/registration errors, land-cover changes, and diverse knowledge background of annotators. To overcome the influence of noisy labels on the learning process of deep models, we propose a new loss function called noise-tolerant deep neighborhood embedding which can accurately encode the semantic relationships among RS scenes. Specifically, we target at maximizing the leave-one-out K-NN score for uncovering the inherent neighborhood structure among the images in feature space. Moreover, we down-weight the contribution of potential noisy images by learning their localized structure and pruning the images with low leave-one-out K-NN scores. Based on our newly proposed loss function, classwise features can be more robustly discriminated. Our experiments, conducted on two benchmark RS datasets, validate the effectiveness of the proposed approach on three different RS scene interpretation tasks, including classification, clustering, and retrieval. The codes of this article will be publicly available from https://github.com/jiankang1991

Superpixel segmentation based on anisotropic edge strength

Superpixel segmentation can benefit from the use of an appropriate method to measure edge strength. In this paper, we present such a method based on the first derivative of anisotropic Gaussian kernels. The kernels can capture the position, direction, prominence, and scale of the edge to be detected. We incorporate the anisotropic edge strength into the distance measure between neighboring superpixels, thereby improving the performance of an existing graph-based superpixel segmentation method. Experimental results validate the superiority of our method in generating superpixels over the competing methods. It is also illustrated that the proposed superpixel segmentation method can facilitate subsequent saliency detection

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

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Flying Target Detection and Recognition by Feature Fusion

This paper presents a near-realtime visual detection and recognition approach for flying target detection and recognition. Detection is based on fast and robust background modeling and shape extraction, while recognition of target classes is based on shape and texture fused querying on a-priori built real datasets. Main application areas are passive defense and surveillance scenarios