Earth Resources Laboratory research and technology

The accomplishments of the Earth Resources Laboratory's research and technology program are reported. Sensors and data systems, the AGRISTARS project, applied research and data analysis, joint research projects, test and evaluation studies, and space station support activities are addressed

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

Automatic registration of multi-modal airborne imagery

This dissertation presents a novel technique based on Maximization of Mutual Information (MMI) and multi-resolution to design an algorithm for automatic registration of multi-sensor images captured by various airborne cameras. In contrast to conventional methods that extract and employ feature points, MMI-based algorithms utilize the mutual information found between two given images to compute the registration parameters. These, in turn, are then utilized to perform multi-sensor registration for remote sensing images. The results indicate that the proposed algorithms are very effective in registering infrared images taken at three different wavelengths with a high resolution visual image of a given scene. The MMI technique has proven to be very robust with images acquired with the Wild Airborne Sensor Program (WASP) multi-sensor instrument. This dissertation also shows how wavelet based techniques can be used in a multi-resolution analysis framework to significantly increase computational efficiency for images captured at different resolutions. The fundamental result of this thesis is the technique of using features in the images to enhance the robustness, accuracy and speed of MMI registration. This is done by using features to focus MMI on places that are rich in information. The new algorithm smoothly integrates with MMI and avoids any need for feature-matching, and then the applications of such extensions are studied. The first extension is the registration of cartographic maps and image datum, which is very important for map updating and change detection. This is a difficult problem because map features such as roads and buildings may be mis-located and features extracted from images may not correspond to map features. Nonetheless, it is possible to obtain a general global registration of maps and images by applying statistical techniques to map and image features. To solve the map-to-image registration problem this research extends the MMI technique through a focus-of-attention mechanism that forces MMI to utilize correspondences that have a high probability of being information rich. The gradient-based parameter search and exhaustive parameter search methods are also compared. Both qualitative and quantitative analysis are used to assess the registration accuracy. Another difficult application is the fusion of the LIDAR elevation or intensity data with imagery. Such applications are even more challenging when automated registrations algorithms are needed. To improve the registration robustness, a salient area extraction algorithm is developed to overcome the distortion in the airborne and satellite images from different sensors. This extension combines the SIFT and Harris feature detection algorithms with MMI and the Harris corner label map to address difficult multi-modal registration problems through a combination of selection and focus-of-attention mechanisms together with mutual information. This two-step approach overcomes the above problems and provides a good initialization for the final step of the registration process. Experimental results are provided that demonstrate a variety of mapping applications including multi-modal IR imagery, map and image registration and image and LIDAR registration

UAV-Multispectral Sensed Data Band Co-Registration Framework

Precision farming has greatly benefited from new technologies over the years. The use of multispectral and hyperspectral sensors coupled to Unmanned Aerial Vehicles (UAV) has enabled farms to monitor crops, improve the use of resources and reduce costs. Despite being widely used, multispectral images present a natural misalignment among the various spectra due to the use of different sensors. The variation of the analyzed spectrum also leads to a loss of characteristics among the bands which hinders the feature detection process among the bands, which makes the alignment process complex. In this work, we propose a new framework for the band co-registration process based on two premises: i) the natural misalignment is an attribute of the camera, so it does not change during the acquisition process; ii) the speed of displacement of the UAV when compared to the speed between the acquisition of the first to the last band, is not sufficient to create significant distortions. We compared our results with the ground-truth generated by a specialist and with other methods present in the literature. The proposed framework had an average back-projection (BP) error of 0.425 pixels, this result being 335% better than the evaluated frameworks.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorDissertação (Mestrado)A agricultura de precisão se beneficiou muito das novas tecnologias ao longo dos anos. O uso de sensores multiespectrais e hiperespectrais acoplados aos Veículos Aéreos Não Tripulados (VANT) permitiu que as fazendas monitorassem as lavouras, melhorassem o uso de recursos e reduzissem os custos. Apesar de amplamente utilizadas, as imagens multiespectrais apresentam um desalinhamento natural entre os vários espectros devido ao uso de diferentes sensores. A variação do espectro analisado também leva à perda de características entre as bandas, o que dificulta o processo de detecção de atributos entre as bandas, o que torna complexo o processo de alinhamento. Neste trabalho, propomos um novo framework para o processo de alinhamento entre as bandas com base em duas premissas: i) o desalinhamento natural é um atributo da câmera, e por esse motivo ele não é alterado durante o processo de aquisição; ii) a velocidade de deslocamento do VANT, quando comparada à velocidade entre a aquisição da primeira e a última banda, não é suficiente para criar distorções significativas. Os resultados obtidos foram comparados com o padrão ouro gerado por um especialista e com outros métodos presentes na literatura. O framework proposto teve um back-projection error (BP) de 0, 425 pixels, sendo este resultado 335% melhor aos frameworks avaliados

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