Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

The European Space Agency Sentinel-2 satellites provide multispectral images with pixel sizes down to 10 m. This high resolution allows for fast and frequent detection, classification and discrimination of various objects in the sea, which is relevant in general and specifically for the vast Arctic environment. We analyze several sets of multispectral image data from Denmark and Greenland fall and winter, and describe a supervised search and classification algorithm based on physical parameters that successfully finds and classifies all objects in the sea with reflectance above a threshold. It discriminates between objects like ships, islands, wakes, and icebergs, ice floes, and clouds with accuracy better than 90%. Pan-sharpening the infrared bands leads to classification and discrimination of ice floes and clouds better than 95%. For complex images with abundant ice floes or clouds, however, the false alarm rate dominates for small non-sailing boats

The utility of Skylab photo-interpreted earth resources data in studies of marine geology and coastal processes in Puerto Rico and the Virgin Islands

The author has identified the following significant results. Three Skylab earth resources passes over Puerto Rico and St. Croix on 6 June and 30 November 1973 and 18 January 1974 resulted in color photography and multispectral photography and scanner imagery. Bathymetric and turbid water features are differentiable by use of the multispectral data. Photography allows mapping of coral reefs, offshore sand deposits, areas of coastal erosion, and patterns of sediment transport. Bottom sediment types could not be differentiated. Patterns of bottom dwelling biologic communities are well portrayed but are difficult to differentiate from bathymetric detail. Effluent discharges and oil slicks are readily detected and are differentiated from other phenomena by the persistence of their images into the longer wavelength multispectral bands

Summary report of work on ten tasks

There are no author-identified significant results in this report

California coast nearshore processes study

There are no author-identified significant results in this report

Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms

Multistage, multiband and sequential imagery to identify and quantify non-forest vegetation resources

Earth Resources photographs from Apollo 6, 7, and 9 and photographs taken during Gemini 4, were used in the research along with high altitude and conventional aerial photography. A unified land use and resource analysis system was devised and used to develop a mapping legend. The natural vegetation, land use, macrorelief, and landforms of northern Maricopa County, Arizona, were analyzed and inventoried. This inventory was interpreted in relation to the critical problem of urban expansion and agricultural production in the study area. The central thrust of the research program has been to develop methods for use of space and small-scale, high-altitude aerial photography to develop information for land use planning and resource allocation decisions

The importance of a coastal embayment for migrating humpback whale mother-calf groups: characterising movement patterns using geospatial methods

Humpback whale populations that migrate along Australian coastlines each year have rapidly increased in population size since modern whaling. This population growth has been associated with increased presence and activity of humpback whales in coastal embayments along the Australian coastlines, particularly mother-calf groups who use the sheltered waters to conserve energy. However, growing numbers in nearshore areas also increases the potential for disturbance from Defence, recreational and commercial activities. The disturbance of resting mothers and calves may have longer term implications for calf growth during key development stages. Jervis Bay is a coastal embayment in which increased numbers of mother-calf groups have been observed in the last two decades and is also an area of significant anthropogenic activity. This thesis aims to assess the significance of Jervis Bay to humpback whale groups using novel survey methods. The movement patterns in the Bay are characterised and compared with that observed for humpback whales migrating south offshore. During the peak timing for humpback whales passing Jervis Bay in 2018, 2019, and 2021, land-based, boat-based, and unoccupied aerial vehicle (UAV) survey methods were conducted. Results showed that a disproportionately high percentage of groups entering the Bay contained a calf and that travel of mother-calf groups in the Bay was significantly slower and less directed than movements of these groups offshore. Resting and nurturing behaviour was observed in aerial footage. These findings support the argument for identifying Jervis Bay as a resting ground for mother-calf humpback whale groups of the east Australia (substock E1) population. With improved understanding of their behaviour and movement in the Bay, there is a need to monitor and manage increased anthropogenic activities during the southern migration season