More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification

Classification and identification of the materials lying over or beneath the Earth's surface have long been a fundamental but challenging research topic in geoscience and remote sensing (RS) and have garnered a growing concern owing to the recent advancements of deep learning techniques. Although deep networks have been successfully applied in single-modality-dominated classification tasks, yet their performance inevitably meets the bottleneck in complex scenes that need to be finely classified, due to the limitation of information diversity. In this work, we provide a baseline solution to the aforementioned difficulty by developing a general multimodal deep learning (MDL) framework. In particular, we also investigate a special case of multi-modality learning (MML) -- cross-modality learning (CML) that exists widely in RS image classification applications. By focusing on "what", "where", and "how" to fuse, we show different fusion strategies as well as how to train deep networks and build the network architecture. Specifically, five fusion architectures are introduced and developed, further being unified in our MDL framework. More significantly, our framework is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs). To validate the effectiveness and superiority of the MDL framework, extensive experiments related to the settings of MML and CML are conducted on two different multimodal RS datasets. Furthermore, the codes and datasets will be available at https://github.com/danfenghong/IEEE_TGRS_MDL-RS, contributing to the RS community

Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images

Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above‐ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above‐ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5 ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment

Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images

Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above‐ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above‐ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5 ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Multiple approaches for assessing mangrove biophysical and biochemical variables using in situ and remote sensing techniques

Mangrove forests are important ecosystems and play a key role in maintaining the equilibrium in coastal lagoons and estuaries. However, in recent years, there has been a considerable loss of mangrove extension due to anthropogenic activities. Recent studies suggest that multiple in situ and remote sensing approaches must be carried out to understand the dynamics in these complex ecosystems. Therefore, the objective for this PhD dissertation is to develop multiple techniques for monitoring the seasonal biophysical and biochemical conditions of the mangrove forests. Particular objectives will include: i. Test the feasibility of using a Chlorophyll Content Index from a CCM-200 unit as an estimator of the variation of leaf pigments (chlorophyll-a, chlorophyll-b) content for a range of mangrove species. ii. Assess changes in chlorophyll-a, leaf area, leaf length, and Leaf Area Index between the dry and rainy seasons in a variety of mangrove classes. iii. Assess the seasonal importance of in situ hyperspectral measurements (e.g. 450-1000 nm) for chlorophyll-a determination in a variety of mangrove species. And finally, iv. Determine whether an object-based image analysis approach can provide an accurate classification of mangroves from spaceborne Synthetic Aperture Radar data. The results from these studies could provide reliable information regarding seasonal ecological assessments of mangrove forests using in situ and remote sensing methods

Structure, Aboveground Biomass, and Soil Characterization of Avicennia marina in Eastern Mangrove Lagoon National Park, Abu Dhabi

Mangrove forests are national treasures of the United Arab Emirates (UAE) and other arid countries with limited forested areas. Mangroves form a crucial part of the coastal ecosystem and provide numerous benefits to society, economy, and especially the environment. Mangrove trees, specifically Avicennia marina, are studied in their native habitat in order to characterize their population structure, aboveground biomass, and soil properties. This study focused on Eastern Mangrove Lagoon National Park in Abu Dhabi, which was the first mangrove protected area to be designated in UAE. In situ measurements were collected to estimate Avicennia marina status, mortality rate (%), height (m), crown spread (m), stem number, diameter at breast height (cm), basal area (m), and aboveground biomass (t ha-1). Small-footprint aerial light detection and ranging (LIDAR) data acquired by UAE were processed to characterize mangrove canopy height and aboveground biomass density. This included extraction of LIDAR-derived height percentile statistics, segmentation of the forest into structurally homogenous units, and development of regression relationships between in situ reference and remote sensing data using a machine learning approach. Anin situ soil survey was conducted to examine the soils\u27 physical and chemical properties, fertility status, and organic matter. The data of soil survey were used to create soil maps to evaluate key characteristics of soils, and their influence on Avicennia marina in Eastern Mangrove Lagoon National Park. The results of this study provide new insights into Avicennia marina canopy population, structure, aboveground biomass, and soil properties in Abu Dhabi, as data in such arid environments is lacking. This valuable information can help in managing and preserving this unique ecosystem

The adoption of the marketing concept by the United Kingdom and Ghanaian banks.

SIGLEAvailable from British Library Document Supply Centre-DSC:DX188668 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Sea ice segmentation in SAR images using Deep Learning

Sea ice covers over seven percent of the world's oceans. Due to the effect of global warming, Arctic's ice extent has decreased significantly in the past decades. This reduction in sea ice cover is opening new pathways for the international shipping community through the Arctic. Due to the lengthening of the open water season, the Canadian Arctic has also observed a three-fold increase in the shipping traffic in the past few years. Although the ice extent has reduced, the risks and hazards involved in shipping through these regions are still significant. To promote safe and efficient maritime activities in the Canadian Arctic, Canadian Ice Service (CIS) provides information about ice in Canada's navigable waters. CIS uses Synthetic Aperture Radar (SAR) images as one of the prominent sources to gain insights about the ice conditions in Canadian waters. Automated SAR image interpretation is a complex task and requires algorithms to learn complex and rich features. Convolutional neural networks (CNNs) have demonstrated their ability to learn such features and have been used in various image classification, segmentation and object detection tasks. In this thesis, we first propose a method to detect marginal ice zones (MIZs) in SAR images. This method uses transfer learning combined with a multi-scale patch technique to detect the MIZs. The multi-scale patch technique involves generating the segmentation masks over different patch sizes for the same image. These masks are later stacked together and thresholded to generate the final MIZ prediction mask for an image. Later we dive deep into the MIZs and focus on segmenting sea ice floes. We propose a segmentation model optimized for the task of ice floe segmentation in SAR images. The model is based on a fully convolutional architecture with residual connections. In addition to this, a conditional random field is also used as a post-processing step. The whole network is trained end-to-end using a dual loss function. Qualitative and quantitative analysis suggests that our model beats the conventional segmentation architectures for the task of ice floe detection

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports