Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

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

How to build a 2d and 3d aerial multispectral map?—all steps deeply explained

UIDB/04111/2020 PCIF/SSI/0102/2017 IF/00325/2015 UIDB/00066/2020The increased development of camera resolution, processing power, and aerial platforms helped to create more cost-efficient approaches to capture and generate point clouds to assist in scientific fields. The continuous development of methods to produce three-dimensional models based on two-dimensional images such as Structure from Motion (SfM) and Multi-View Stereopsis (MVS) allowed to improve the resolution of the produced models by a significant amount. By taking inspiration from the free and accessible workflow made available by OpenDroneMap, a detailed analysis of the processes is displayed in this paper. As of the writing of this paper, no literature was found that described in detail the necessary steps and processes that would allow the creation of digital models in two or three dimensions based on aerial images. With this, and based on the workflow of OpenDroneMap, a detailed study was performed. The digital model reconstruction process takes the initial aerial images obtained from the field survey and passes them through a series of stages. From each stage, a product is acquired and used for the following stage, for example, at the end of the initial stage a sparse reconstruction is produced, obtained by extracting features of the images and matching them, which is used in the following step, to increase its resolution. Additionally, from the analysis of the workflow, adaptations were made to the standard workflow in order to increase the compatibility of the developed system to different types of image sets. Particularly, adaptations focused on thermal imagery were made. Due to the low presence of strong features and therefore difficulty to match features across thermal images, a modification was implemented, so thermal models could be produced alongside the already implemented processes for multispectral and RGB image sets.publishersversionpublishe

SpaceNet MVOI: a Multi-View Overhead Imagery Dataset

Detection and segmentation of objects in overheard imagery is a challenging task. The variable density, random orientation, small size, and instance-to-instance heterogeneity of objects in overhead imagery calls for approaches distinct from existing models designed for natural scene datasets. Though new overhead imagery datasets are being developed, they almost universally comprise a single view taken from directly overhead ("at nadir"), failing to address a critical variable: look angle. By contrast, views vary in real-world overhead imagery, particularly in dynamic scenarios such as natural disasters where first looks are often over 40 degrees off-nadir. This represents an important challenge to computer vision methods, as changing view angle adds distortions, alters resolution, and changes lighting. At present, the impact of these perturbations for algorithmic detection and segmentation of objects is untested. To address this problem, we present an open source Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks from a broad range of viewing angles (-32.5 degrees to 54.0 degrees). Each of these images cover the same 665 square km geographic extent and are annotated with 126,747 building footprint labels, enabling direct assessment of the impact of viewpoint perturbation on model performance. We benchmark multiple leading segmentation and object detection models on: (1) building detection, (2) generalization to unseen viewing angles and resolutions, and (3) sensitivity of building footprint extraction to changes in resolution. We find that state of the art segmentation and object detection models struggle to identify buildings in off-nadir imagery and generalize poorly to unseen views, presenting an important benchmark to explore the broadly relevant challenge of detecting small, heterogeneous target objects in visually dynamic contexts.Comment: Accepted into IEEE International Conference on Computer Vision (ICCV) 201

Digital Multispectral Map Reconstruction Using Aerial Imagery

Advances made in the computer vision field allowed for the establishment of faster and more accurate photogrammetry techniques. Structure from Motion(SfM) is a photogrammetric technique focused on the digital spatial reconstruction of objects based on a sequence of images. The benefit of Unmanned Aerial Vehicle (UAV) platforms allowed the ability to acquire high fidelity imagery intended for environmental mapping. This way, UAV platforms became a heavily adopted method of survey. The combination of SfM and the recent improvements of Unmanned Aerial Vehicle (UAV) platforms granted greater flexibility and applicability, opening a new path for a new remote sensing technique aimed to replace more traditional and laborious approaches often associated with high monetary costs. The continued development of digital reconstruction software and advances in the field of computer processing allowed for a more affordable and higher resolution solution when compared to the traditional methods. The present work proposed a digital reconstruction algorithm based on images taken by a UAV platform inspired by the work made available by the open-source project OpenDroneMap. The aerial images are inserted in the computer vision program and several operations are applied to them, including detection and matching of features, point cloud reconstruction, meshing, and texturing, which results in a final product that represents the surveyed site. Additionally, from the study, it was concluded that an implementation which addresses the processing of thermal images was not integrated in the works of OpenDroneMap. By this point, their work was altered to allow for the reconstruction of thermal maps without sacrificing the resolution of the final model. Standard methods to process thermal images required a larger image footprint (or area of ground capture in a frame), the reason for this is that these types of images lack the presence of invariable features and by increasing the image’s footprint, the number of features present in each frame also rises. However, this method of image capture results in a lower resolution of the final product. The algorithm was developed using open-source libraries. In order to validate the obtained results, this model was compared to data obtained from commercial products, like Pix4D. Furthermore, due to circumstances brought about by the current pandemic, it was not possible to conduct a field study for the comparison and assessment of our results, as such the validation of the models was performed by verifying if the geographic location of the model was performed correctly and by visually assessing the generated maps.Avanços no campo da visão computacional permitiu o desenvolvimento de algoritmos mais eficientes de fotogrametria. Structure from Motion (SfM) é uma técnica de fotogrametria que tem como objetivo a reconstrução digital de objectos no espaço derivados de uma sequência de imagens. A característica importante que os Veículos Aérios não-tripulados (UAV) conseguem fornecer, a nível de mapeamento, é a sua capacidade de obter um conjunto de imagens de alta resolução. Devido a isto, UAV tornaram-se num dos métodos adotados no estudo de topografia. A combinação entre SfM e recentes avanços nos UAV permitiram uma melhor flexibilidade e aplicabilidade, permitindo deste modo desenvolver um novo método de Remote Sensing. Este método pretende substituir técnicas tradicionais, as quais estão associadas a mão-de-obra intensiva e a custos monetários elevados. Avanços contínuos feitos em softwares de reconstrução digital e no poder de processamento resultou em modelos de maior resolução e menos dispendiosos comparando a métodos tradicionais. O presente estudo propõe um algoritmo de reconstrução digital baseado em imagens obtidas através de UAV inspiradas no estudo disponibilizado pela OpenDroneMap. Estas imagens são inseridas no programa de visão computacional, onde várias operações são realizadas, incluindo: deteção e correspondência de caracteristicas, geração da point cloud, meshing e texturação dos quais resulta o produto final que representa o local em estudo. De forma complementar, concluiu-se que o trabalho da OpenDroneMap não incluia um processo de tratamento de imagens térmicas. Desta forma, alterações foram efetuadas que permitissem a criação de mapas térmicos sem sacrificar resolução do produto final, pois métodos típicos para processamento de imagens térmicas requerem uma área de captura maior, devido à falta de características invariantes neste tipo de imagens, o que leva a uma redução de resolução. Desta forma, o programa proposto foi desenvolvido através de bibliotecas open-source e os resultados foram comparados com modelos gerados através de software comerciais. Além do mais, devido à situação pandémica atual, não foi possível efetuar um estudo de campo para validar os modelos obtidos, como tal esta verificação foi feita através da correta localização geográfica do modelo, bem como avaliação visual dos modelos criados

Video normals from colored lights

We present an algorithm and the associated single-view capture methodology to acquire the detailed 3D shape, bends, and wrinkles of deforming surfaces. Moving 3D data has been difficult to obtain by methods that rely on known surface features, structured light, or silhouettes. Multispectral photometric stereo is an attractive alternative because it can recover a dense normal field from an untextured surface. We show how to capture such data, which in turn allows us to demonstrate the strengths and limitations of our simple frame-to-frame registration over time. Experiments were performed on monocular video sequences of untextured cloth and faces with and without white makeup. Subjects were filmed under spatially separated red, green, and blue lights. Our first finding is that the color photometric stereo setup is able to produce smoothly varying per-frame reconstructions with high detail. Second, when these 3D reconstructions are augmented with 2D tracking results, one can register both the surfaces and relax the homogenous-color restriction of the single-hue subject. Quantitative and qualitative experiments explore both the practicality and limitations of this simple multispectral capture system

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