In the last 10 years, development in robotics, computer vision, and sensor technology has provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and high spectral resolution with unmanned aerial vehicles (UAVs). This development has led to a revolution in geospatial data collection in which not only few specialist data providers collect and deliver remotely sensed data, but a whole diverse community is potentially able to gather geospatial data that fit their needs. However, the diversification of sensing systems and user applications challenges the common application of good practice procedures that ensure the quality of the data. This challenge can only be met by establishing and communicating common procedures that have had demonstrated success in scientific experiments and operational demonstrations. In this review, we evaluate the state-of-the-art methods in UAV spectral remote sensing and discuss sensor technology, measurement procedures, geometric processing, and radiometric calibration based on the literature and more than a decade of experimentation. We follow the 'journey' of the reflected energy from the particle in the environment to its representation as a pixel in a 2D or 2.5D map, or 3D spectral point cloud. Additionally, we reflect on the current revolution in remote sensing, and identify trends, potential opportunities, and limitations.

/pdf/quantitative-remote-sensing-at-ultra-high-resolution-with-1fowvgc7qt.pdf

Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

https://cyberleninka.org/article/n/1415891.pdf

Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry

This study conducts an exploratory evaluation of the performance of the newly available Sentinel-2A Multispectral Instrument (MSI) imagery for mapping water bodies using the image sharpening approach. Sentinel-2 MSI provides spectral bands with different resolutions, including RGB and Near-Infra-Red (NIR) bands in 10 m and Short-Wavelength InfraRed (SWIR) bands in 20 m, which are closely related to surface water information. It is necessary to define a pan-like band for the Sentinel-2 image sharpening process because of the replacement of the panchromatic band by four high-resolution multi-spectral bands (10 m). This study, which aimed at urban surface water extraction, utilised the Normalised Difference Water Index (NDWI) at 10 m resolution as a high-resolution image to sharpen the 20 m SWIR bands. Then, object-level Modified NDWI (MNDWI) mapping and minimum valley bottom adjustment threshold were applied to extract water maps. The proposed method was compared with the conventional most related band- (between the visible spectrum/NIR and SWIR bands) based and principal component analysis first component-based sharpening. Results show that the proposed NDWI-based MNDWI image exhibits higher separability and is more effective for both classification-level and boundary-level final water maps than traditional approaches.

Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening

In this study, we propose a novel methodology for automated detection of buildings from single very-high-resolution (VHR) multispectral images The methodology uses the principal evidence of buildings: the shadows that they cast We model the directional spatial relationship between buildings and their shadows using a recently proposed probabilistic landscape approach An effective shadow post-processing step is developed to focus on landscapes that belong to building regions The building regions are detected using an original two-level graph theory approach In the first level, each shadow region is addressed separately, and building regions are identified via iterative graph cuts designed in two-label partitioning The final building regions are characterised in a second level in which the previously labelled building regions are subjected to a single-step multi-label graph optimisation performed over the entire image domain Numerical assessments performed on 16 VHR GeoEye-1 images demonstrate that the proposed approach is highly robust and reliable A distinctive specialty of the proposed approach is its applicability to buildings with diverse characteristics as well as to VHR images with significantly different illumination properties

Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts

Segmentation of images by color features: a survey

Automatic shadow detection is a very important pre-processing step for many remote sensing applications, particularly for images acquired with high spatial resolution. In complex urban environments, shadows may occupy a significant portion of the image. Ignoring these regions would lead to errors in various applications, such as atmospheric correction and classification. To better understand the radiative impact of shadows, a physical study was conducted through the simulation of a synthetic urban canyon scene. Its results helped to explain the most common assumptions made on shadows from a physical point of view in the literature. With this understanding, state-of-the-art methods on shadow detection were surveyed and categorized into six classes: histogram thresholding, invariant color models, object segmentation, geometrical methods, physics-based methods, unsupervised and supervised machine learning methods. Among them, some methods were selected and tested on a large dataset of multispectral and hyperspectral airborne images with high spatial resolution. The dataset chosen contains a large variety of typical occidental urban scenes. The results were compared based on accurate reference shadow masks. In these experiments, histogram thresholding on RGB and NIR channels performed the best with an average accuracy of 92.5%, followed by physics-based methods, such as Richter’s method with 90.0%. Finally, this paper analyzes and discusses the limits of these algorithms, concluding with some recommendations for shadow detection.

Shadow detection in very high spatial resolution aerial images: A comparative study

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Shadow detection in very high spatial resolution aerial images: A comparative study