Satellite snowcover and runoff monitoring in central Arizona

The author has identified the following significant results. Although the very high resolution experimental LANDSAT imagery permits rapid snow cover mapping at low cost, only one observation is available very 9 days. In contrast, low resolution operational imagery acquired by the ITOS and SMS/GOES satellites provide the daily synoptic observations necessary to monitor the rapid changes in snow covered areas in the entire Salt-Verde watershed. Geometric distortions in meteorological satellite imagery require specialized optical equipment or digital image processing for snow cover mapping

Towards Automatic SAR-Optical Stereogrammetry over Urban Areas using Very High Resolution Imagery

In this paper we discuss the potential and challenges regarding SAR-optical stereogrammetry for urban areas, using very-high-resolution (VHR) remote sensing imagery. Since we do this mainly from a geometrical point of view, we first analyze the height reconstruction accuracy to be expected for different stereogrammetric configurations. Then, we propose a strategy for simultaneous tie point matching and 3D reconstruction, which exploits an epipolar-like search window constraint. To drive the matching and ensure some robustness, we combine different established handcrafted similarity measures. For the experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR imagery is generally feasible with 3D positioning accuracies in the meter-domain, although the matching of these strongly hetereogeneous multi-sensor data remains very challenging. Keywords: Synthetic Aperture Radar (SAR), optical images, remote sensing, data fusion, stereogrammetr

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Offshore Metallic Platforms Observation Using Dual-Polarimetric TS-X/TD-X Satellite Imagery: A Case Study in the Gulf of Mexico

Satellite-based synthetic aperture radar (SAR) has been proven to be an effective tool for ship monitoring. Offshore platforms monitoring is a key topic for both safety and security of the maritime domain. However, the scientific literature oriented to the observation of offshore platforms using SAR imagery is very limited. This study is mostly focused on the analysis and understanding of the multipolarization behavior of platforms’ backscattering using dual-polarization X-band SAR imagery. This study is motivated by the fact that under low incidence angle and moderate wind conditions, copolarized channels may fail in detecting offshore platforms even when fine-resolution imagery is considered. This behavior has been observed on both medium- and high-resolution TerraSAR-X/TanDEM-X SAR imagery, despite the fact that platforms consist of large metallic structures. Hence, a simple multipolarization model is proposed to analyze the platform backscattering. Model predictions are verified on TerraSAR-X/TanDEM-X SAR imagery, showing that for acquisitions under low incidence angle, the platforms result in a reduced copolarized backscattered intensity even when fine resolution imagery is considered. Finally, several solutions to tackle this issue are proposed with concluding remark that the performance of offshore observation

Automated crop plant counting from very high-resolution aerial imagery

Knowing before harvesting how many plants have emerged and how they are growing is key in optimizing labour and efficient use of resources. Unmanned aerial vehicles (UAV) are a useful tool for fast and cost efficient data acquisition. However, imagery need to be converted into operational spatial products that can be further used by crop producers to have insight in the spatial distribution of the number of plants in the field. In this research, an automated method for counting plants from very high-resolution UAV imagery is addressed. The proposed method uses machine vision—Excess Green Index and Otsu’s method—and transfer learning using convolutional neural networks to identify and count plants. The integrated methods have been implemented to count 10 weeks old spinach plants in an experimental field with a surface area of 3.2 ha. Validation data of plant counts were available for 1/8 of the surface area. The results showed that the proposed methodology can count plants with an accuracy of 95% for a spatial resolution of 8 mm/pixel in an area up to 172 m2. Moreover, when the spatial resolution decreases with 50%, the maximum additional counting error achieved is 0.7%. Finally, a total amount of 170 000 plants in an area of 3.5 ha with an error of 42.5% was computed. The study shows that it is feasible to count individual plants using UAV-based off-the-shelf products and that via machine vision/learning algorithms it is possible to translate image data in non-expert practical information.</p

Deep-sea image processing

High-resolution seafloor mapping often requires optical methods of sensing, to confirm interpretations made from sonar data. Optical digital imagery of seafloor sites can now provide very high resolution and also provides additional cues, such as color information for sediments, biota and divers rock types. During the cruise AT11-7 of the Woods Hole Oceanographic Institution (WHOI) vessel R/V Atlantis (February 2004, East Pacific Rise) visual imagery was acquired from three sources: (1) a digital still down-looking camera mounted on the submersible Alvin, (2) observer-operated 1-and 3-chip video cameras with tilt and pan capabilities mounted on the front of Alvin, and (3) a digital still camera on the WHOI TowCam (Fornari, 2003). Imagery from the first source collected on a previous cruise (AT7-13) to the Galapagos Rift at 86°W was successfully processed and mosaicked post-cruise, resulting in a single image covering area of about 2000 sq.m, with the resolution of 3 mm per pixel (Rzhanov et al., 2003). This paper addresses the issues of the optimal acquisition of visual imagery in deep-seaconditions, and requirements for on-board processing. Shipboard processing of digital imagery allows for reviewing collected imagery immediately after the dive, evaluating its importance and optimizing acquisition parameters, and augmenting acquisition of data over specific sites on subsequent dives.Images from the deepsea power and light (DSPL) digital camera offer the best resolution (3.3 Mega pixels) and are taken at an interval of 10 seconds (determined by the strobe\u27s recharge rate). This makes images suitable for mosaicking only when Alvin moves slowly (≪1/4 kt), which is not always possible for time-critical missions. Video cameras provided a source of imagery more suitable for mosaicking, despite its inferiority in resolution. We discuss required pre-processing and imageenhancement techniques and their influence on the interpretation of mosaic content. An algorithm for determination of camera tilt parameters from acquired imagery is proposed and robustness conditions are discussed

Very high resolution UV and X-ray spectroscopy and imagery of solar active regions

A scientific investigation of the physics of the solar atmosphere, which uses the techniques of high resolution soft X-ray spectroscopy and high resolution UV imagery, is described. The experiments were conducted during a series of three sounding rocket flights. All three flights yielded excellent images in the UV range, showing unprecedented spatial resolution. The second flight recorded the X-ray spectrum of a solar flare, and the third that of an active region. A normal incidence multi-layer mirror was used during the third flight to make the first astronomical X-ray observations using this new technique

Concepts for on-board satellite image registration. Volume 2: IAS prototype performance evaluation standard definition

Problems encountered in testing onboard signal processing hardware designed to achieve radiometric and geometric correction of satellite imaging data are considered. These include obtaining representative image and ancillary data for simulation and the transfer and storage of a large quantity of image data at very high speed. The high resolution, high speed preprocessing of LANDSAT-D imagery is considered

Automatic detection and segmentation of orchards using very high-resolution imagery

Cataloged from PDF version of article.Spectral information alone is often not sufficient to distinguish certain terrain classes such as permanent crops like orchards, vineyards, and olive groves from other types of vegetation. However, instances of these classes possess distinctive spatial structures that can be observable in detail in very high spatial resolution images. This paper proposes a novel unsupervised algorithm for the detection and segmentation of orchards. The detection step uses a texture model that is based on the idea that textures are made up of primitives (trees) appearing in a near-regular repetitive arrangement (planting patterns). The algorithm starts with the enhancement of potential tree locations by using multi-granularity isotropic filters. Then, the regularity of the planting patterns is quantified using projection profiles of the filter responses at multiple orientations. The result is a regularity score at each pixel for each granularity and orientation. Finally, the segmentation step iteratively merges neighboring pixels and regions belonging to similar planting patterns according to the similarities of their regularity scores and obtains the boundaries of individual orchards along with estimates of their granularities and orientations. Extensive experiments using Ikonos and QuickBird imagery as well as images taken from Google Earth show that the proposed algorithm provides good localization of the target objects even when no sharp boundaries exist in the image data. © 2012 IEEE