Remote sensing means acquiring and measuring information about an object or phenomenon via a device that is not in physical or direct contact with what is being studied (Colwell, 1983).To collect remotely sensed data, a platform – an instrument that carries a remote sensing sensor – is deployed. From the mid 1800’s to the early 1900’s, various platforms such as balloons, kites, and pigeons carried mounted cameras to collect visual data of the world below. Today, aircraft (both manned and unmanned) and satellites collect the majority of remotely sensed data. The sensors typically deployed on these platforms include film and digital cameras, light-detection and ranging (LiDAR) systems, synthetic aperture radar (SAR) systems, and multi-spectral and hyper-spectral scanners. Many of these instruments can be mounted on land-based platforms, such as vans, trucks, tractors, and tanks. In this chapter, we will explore the different types of platforms and their resulting remote sensing applications.
The Landsat series of satellites have collected the longest and continuous earth observation data. Earth surface data collected since 1972 are providing invaluable data for managing natural resources, monitoring changes, and disaster response. After the US Geological Survey (USGS) opened the entire archive to users, the number of monitoring and mapping applications have increased several folds. Currently, Landsat data can be obtained from the USGS and other private entities. The sensors onboard these Landsat satellites have improved over time resulting in changes to the spatial, spectral, radiometric, and temporal resolutions of the images they have collected. Data recorded by the sensors in the form of pixels can be converted to reflectance values. Recently, USGS has reprocessed the entire Landsat data archive and is releasing them as collections. This section provides an overview of the Landsat program and remotely sensed data characteristics, followed by the description of various sensors onboard and data collected by the past and current sensors.
Object detection, the task of identifying and localizing objects within images or video, is a rapidly growing field that has become fundamental to numerous applications, including autonomous vehicles, surveillance systems, agricultural monitoring, and infrastructure assessment. While traditional visible-light cameras operating in the Red-Green-Blue (RGB) wavelengths excel at providing detailed color and texture information under good lighting conditions, they become ineffective in challenging conditions, such as low light, nighttime, or adverse weather. Long-Wave Infrared (LWIR) sensors complement these capabilities by detecting thermal radiation naturally emitted by objects, enabling detection regardless of ambient lighting conditions. Because of their complementary strengths, the fusion of RGB and LWIR modalities creates detection systems that maintain robust performance across diverse operational scenarios. Modern object detection leverages deep learning approaches, such as Convolutional Neural Networks (CNNs) and emerging transformer architectures, that have revolutionized the extraction of features and automatic classification of objects. This entry provides an overview of RGB and LWIR sensor technologies, neural network-based object detection methods, and their combined applications across domains, including autonomous driving, precision agriculture, infrastructure monitoring, maritime surveillance, and defense applications.