Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

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

Trying to break new ground in aerial archaeology

Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earth’s atmosphere, the majority of aerial archaeological information is still derived from oblique photographs collected during observer-directed reconnaissance flights, a prospection approach which has dominated archaeological aerial survey for the past century. The resulting highly biased imagery is generally catalogued in sub-optimal (spatial) databases, if at all, after which a small selection of images is orthorectified and interpreted. For decades, this has been the standard approach. Although many innovations, including digital cameras, inertial units, photogrammetry and computer vision algorithms, geographic(al) information systems and computing power have emerged, their potential has not yet been fully exploited in order to re-invent and highly optimise this crucial branch of landscape archaeology. The authors argue that a fundamental change is needed to transform the way aerial archaeologists approach data acquisition and image processing. By addressing the very core concepts of geographically biased aerial archaeological photographs and proposing new imaging technologies, data handling methods and processing procedures, this paper gives a personal opinion on how the methodological components of aerial archaeology, and specifically aerial archaeological photography, should evolve during the next decade if developing a more reliable record of our past is to be our central aim. In this paper, a possible practical solution is illustrated by outlining a turnkey aerial prospection system for total coverage survey together with a semi-automated back-end pipeline that takes care of photograph correction and image enhancement as well as the management and interpretative mapping of the resulting data products. In this way, the proposed system addresses one of many bias issues in archaeological research: the bias we impart to the visual record as a result of selective coverage. While the total coverage approach outlined here may not altogether eliminate survey bias, it can vastly increase the amount of useful information captured during a single reconnaissance flight while mitigating the discriminating effects of observer-based, on-the-fly target selection. Furthermore, the information contained in this paper should make it clear that with current technology it is feasible to do so. This can radically alter the basis for aerial prospection and move landscape archaeology forward, beyond the inherently biased patterns that are currently created by airborne archaeological prospection

Land use / land cover change detection: an object oriented approach, Münster, Germany

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesLand use / land cover (LULC) change detection based on remote sensing (RS) data has been established as an indispensible tool for providing suitable and wide-ranging information to various decision support systems for natural resource management and sustainable development. LULC change is one of the major influencing factors for landscape changes. There are many change detection techniques developed over decades, in practice, it is still difficult to develop a suitable change detection method especially in case of urban and urban fringe areas where several impacts of complex factors are found including rapid changes from rural land uses to residential, commercial, industrial and recreational uses. Although these changes can be monitored using several techniques of RS application, adopting a suitable technique to represent the changes accurately is a challenging task. There are a number of challenges in RS application for analysis of LULC change detection. This study applies objectoriented (OO) method for mapping LULC and performing change detection analysis using post-classification technique.(...

Long-term Prairie Wetlands Extraction and Change Detection with Multi-spatial and Multi-temporal Remote Sensing Data

Prairie wetlands, also called “potholes”, provide both ecological and hydrological functions and have experienced dramatic change over the past century. This research aims to: 1) compare the capacity of Landsat and SPOT in mapping open water and wet areas with advanced classification methods; 2) monitor and quantify the changes in wetlands and drainage channels, between 1948 and 2009, with aerial photography; and 3) evaluate Landsat’s ability to extract historical wetland coverage data across seasons using a variety of methods. Results indicate that Landsat is capable for mapping open water, wet areas and other LULC types in PPR; however only 48.5% of wetland areas are identified as compared with air photos. Historical analysis of air photo generated wetland and drainage channels show that the whole basin’s wetlands rapidly decreased from 1958 to 1990 (24% to 13%) and slowly decreased from 1990 to 2009 (13% to 10%) with the least reduction in sub basin 1. Drainage channels slowly increased from 1958 to 1990 (119 km to 269 km) and dramatically increased from 1990 to 2009 (269 km to 931km). Wetland area is highly correlated with accumulated snowfall in the previous three years in sub basin 2 (r=0.91, p<0.05) due to its memory effect to previous water conditions. For the full basin, however, there were not enough years of data to prove this correlation. Even though the minimum distance algorithm in early spring is optimal for mapping wetlands in the Prairie Pothole Region (PPR), comparing with air photos, SPOT imagery underestimated wetlands smaller than 1200 m2, while Landsat imagery is not able to detect wetlands smaller than 900 m2 and underestimates areas smaller than 1600 m2. Although free-archived Landsat can detect water bodies larger than 900 m2, its ability to detect prairie wetland is limited due to missing numerous small-scale wetlands and misclassification of seasonal wetlands.