Drawing a spectrally homogeneous region of interest in a remotely sensed image is a common task for an image analyst when performing, for instance, atmospheric correction or end-member selection. Manually selecting a homogeneous sample of pixels can be tedious and error prone due to the limits of human perception and data visualization. I present a region shrinkage method that automates the extraction

The surveillance of large areas to ensure local security requires remote sensors with high temporal and spatial resolution. Captive balloons with infrared and visible sensors, like ALTAVE captive balloon system, can perform a long-term day–night surveillance and provide security of large areas by monitoring people and vehicles, but it is an exhaustive task for a human. In order to provide a more efficient

Evapotranspiration (ET) quantification improves the comprehension of the water, heat, and carbon interactions and the feedback to the climate, which is essential for global change research. We aimed to model ET using artificial neural networks (ANNs) based on Landsat-8 and reanalysis data from the National Centers for Environmental Prediction over the grasslands of the Pampa biome. The output variable

The advanced baseline imager (ABI) instrument onboard Geostationary Operational Environmental Satellite (GOES)-16 is the first of National Oceanic and Atmospheric Administration (NOAA’s) new-generation geostationary earth orbiting (GEO) imagers that provides high-quality calibrated and geolocated Earth observations in six reflective solar bands (RSBs). The spectral similarity between the Visible Infrared

The advanced baseline imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-R Series is a great improvement compared to the legacy GOES imager. For example, there are more spectral bands at improved spatial resolution and more frequent imagery. The vast majority of the images generated by the ABIs are free of visual defects, well calibrated, and produced in a timely fashion.

Ordinary multispectral (MS) image change detection (CD) techniques have not excavated the inherent information of MS images. Deep learning methods are a type of effective method that grabs the changed and unchanged information by deep feature extraction. However, a larger number of labeled samples are difficult to obtain for CD in MS images. An unsupervised MS image CD method is proposed based on a

Water vapor is an important greenhouse gas. The atmospheric water vapor content is monitored by remote methods, which require accurate spectroscopic information about H2O absorption line parameters. The problems of accuracy of accounting the selective water vapor absorption in the simulation of atmospheric transfer of solar radiation are considered in this work. The atmospheric spectra are simulated

The main objective in our study was to derive an accurate wetland inventory of the Dınàgà Wek’èhodì region, Northwest Territories, while also enhancing our previously established wetland mapping workflow. Our methods used multidate optical and radar satellite imagery and fused these data with ArcticDEM topographic variables. Additionally, few studies to date have assessed the ArcticDEM for wetland

In coastal waters, colored dissolved organic matter (CDOM) is often present in moderate to high concentrations due to input from nearby landmasses and rivers. CDOM absorbs light sharply in the UV-blue region, causing waters to appear yellow or brown. This has a significant effect on optical and biogeochemical processes in coastal waters, as it inhibits the penetration of light within the water. It

The dust storm is one of the severe natural disasters that has been recently threatening the Middle East region due to climate changes and human activities. This phenomenon has become a national crisis in some countries in this region in previous years, especially in spring and summer. This research aims to detect and monitor the areas covered by the seasonal and occasional dust storm from (Moderate

The unmanned aerial vehicle (UAV) is an emerging technology applied recently in land cover classification, owing to its ability to acquire very high-resolution spatial data, that has provided an effective means for detailed land cover mapping, especially for a small island area. Selecting suitable UAV-acquired data and exploring the combined use of UAV multitype data are of significance for island

Impervious surfaces (ISs) are significant indicators of the overall environmental health of watersheds. Monitoring the IS spatial patterns of watersheds is helpful for improving water management and regional pollution assessment. A random forest regression (RFR) approach was developed to estimate the subpixel IS percentage (ISP) from Chinese GF-5 hyperspectral imagery in 30 m pixels in the Nansi Lake

The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) instrument has been successfully operating on orbit since November 28, 2017. The day–night band (DNB) onboard NOAA-20 VIIRS is a panchromatic channel covering wavelengths from 0.5 to 0.9 μm, capable of observing the Earth scene in visible/near-infrared spectral range at a spatial resolution of 750 m. The DNB operates at low-, mid-, or high-radiometric

We propose a method for detecting multiple scatterers (targets) in the elevation direction for synthetic aperture radar tomography. The proposed method can resolve closely spaced targets through a two-step procedure. In the first step, coarse detection is performed with a successive cancellation scheme in which possible locations of targets are marked. Then, in the second step, by searching in the

LiDAR remote sensing data combined with machine learning (ML) techniques have presented great potential for large-scale modeling of tropical forest attributes. However, the large amount of information that can be derived from an aerial LiDAR survey, summed with the intrinsic heterogeneity of tropical environments (e.g., the Amazon), makes it a challenge to accurately estimate forest biophysical variables

Optical trapezoid model (OPTRAM) as a method has been proposed to retrieve soil moisture from remote sensing data. It is based on the assumption that a trapezoidal shape would be derived from plotting of vegetation index (VI) versus shortwave-infrared transformed reflectance (STR) data assuming a linear relationship between VI and STR. A literature review and the present study indicate that the relationship

The accurate estimation of global chlorophyll-a (Chla) concentration from the large remote sensing data in a timely manner is crucial for supporting various applications. Moderate resolution imaging spectroradiometer (MODIS) is one of the most widely used earth observation data sources, which has the characteristics of global coverage, high spectral resolution, and short revisit period. So the estimation

Spectral mixture analysis (SMA) is an effective means of finding a unique spectral signature of constituents called endmembers and approximating their proportion of presence (abundance fractions). In the literature of SMA, the challenging task of endmember extraction from the hyperspectral imagery is approached by different methods. The majority of the endmember extraction algorithms are developed

Cotton root rot (CRR) is a persistent soil-borne fungal disease that is devastating to cotton crops in certain fields, predominantly in Texas. Research has shown that CRR can be prevented or mitigated by applying fungicide during planting, but fungicide application is expensive. The potentially infected area within a field has been shown to be consistent, so it is possible to apply the fungicide only

Images from high-resolution inverse synthetic aperture radar (ISAR) can provide more information about the targets. Multiband fusion imaging techniques can achieve higher range resolution without increasing hardware costs. A multiband fusion imaging algorithm based on variational Bayesian inference (VBI) is proposed to improve the range resolution of ISAR images. First, a multiband fusion ISAR imaging

Our work aims to address the problem of estimating the parameters of constant-amplitude, time-unsynchronized linear frequency-modulated (LFM) signals that have single or multiple components, which is a crucial task in electronic countermeasure techniques. A method for estimating the parameters, center frequency f0, and chirp rate μ of an LFM signal is proposed; the method is referred to as the Wigner–Ville

Hyperspectral images (HSIs) contain a significant amount of spectral and spatial information, together with underlying redundancy and noise, causing difficulty in HSI-processing tasks. State-of-the-art deep learning methods have obtained unprecedented performance in HSI classification and analysis. However, these architectures face challenges of declining accuracy and lengthy training times. We propose

An increasing number of applications require land cover information from remote sensing images, thereby resulting in an urgent demand for automatic land use and land cover classification. Therefore, effectively improving the accuracy of land cover classification is a main objective in remote sensing image processing. We propose a land cover classification postprocessing framework based on iterative

A sparse representation-based bistatic inverse synthetic aperture radar (ISAR) imaging method can achieve a high-resolution image of a target with sparse aperture data. However, the bistatic ISAR system is more sensitive to noise than the monostatic one because of its nonmirror reflection geometry. To overcome this drawback, we propose the sparse aperture bistatic ISAR imaging method based on joint

Terrain classification is an important application for polarimetric synthetic radar (PolSAR) image processing. Inspired by the popular deep belief network (DBN), a PolSAR classification method is proposed, which is called multilayer Wishart restricted Boltzmann machine (MWRBM). For PolSAR data, the traditional DBN is limited by binary value distribution, which is not suitable for PolSAR image classification

Since its launch in December 1999, Terra Moderate Resolution Imaging Spectroradiometer (MODIS) has successfully operated for more than 20 years, with its observations generating a broad range of science data products that have greatly enabled the remote sensing community and users worldwide in their studies of many key geophysical parameters of the Earth’s systems. MODIS collects data in 36 spectral

The fusion of a high-spatial-resolution (HSR) panchromatic band and several multispectral bands with a relative low spatial resolution has become a research focus with the development of HSR remote sensing technology. Previous studies have demonstrated that fused spectra of mixed pixels (MPs) remain mixed, which considerably contributes to spectral distortions observed in fused images produced by most

Evapotranspiration (ET) is an important part of the hydrological cycle. It is essential to evaluating the accuracy of different ET products in practical applications. The operational simplified surface energy balance (SSEBop) ET products are widely used because of their high resolution. The SSEBop products were validated at the site and basin levels using eddy covariance data from ChinaFLUX stations

Spatial data mining discovers patterns and knowledge in spatial data. The geospatial data analysis plays a decisive role in framing essential policies related to the environment at the national as well as global level. It helps in the prediction of weather, studying the impact of climate change, the effect of global warming, forest fire, deforestation, and other causes of changes in nature. Clustering

Extraction of information about individual trees using remotely sensed data is essential to supporting ecological and commercial applications in forest environments. Data acquired by consumer-grade cameras onboard unmanned aerial vehicles (UAV) offer an affordable option of high-spatial resolution imagery that can be used to extract forest structural information at a tree level. The aim of this work

Urbanization in India has been rapid over the past few decades, which results in a substantial replacement of the natural surfaces into built-up lands. We present a performance evaluation of Sentinel-2B, Landsat-8 multispectral, and AVIRIS-NG hyperspectral imagery for extraction of road and roof surfaces using proposed spectral index-based and other conventional algorithms. The new road extraction

Building footprint segmentation from satellite and aerial images is an essential and challenging step for high-resolution building map generation. In urban management applications, such as building monitoring, infrastructure development, smart three-dimensional cities, and building change detection, building footprints are required to generate precise multiscale building maps. An efficient deep learning-based

Atmospheric correction can introduce errors in surface spectral reflectance, and hence induces errors in plant water estimation from remote sensing water indices. We intend to develop water indices that are less impacted by atmospheric effects for plant water content estimation based on the 970-nm water absorption feature. A simulation study using the PROSAIL and 6S models showed that uncertainty in

The present state of the art technologies for flood mapping are typically tested on small geographical regions due to limitation of resources, which hinders the implementation of real-time flood management activities. We proposed a unified framework (GEE4FLOOD) for rapid flood mapping in Google Earth Engine (GEE) cloud platform. With the unexpected spells of extreme rainfall in August 2018, many parts

We describe an algorithm based on a convolutional neural network that detects cloud formations and snow cover in satellite images using textures. Herein, multispectral satellite images, received from a multizone scanning instrument used for hydrometeorological support and installed on the Russian satellite Electro-L No. 2, are used as input data. The problem of snow and cloud classification in the

Organic matter (OM), iron (Fe), and zinc (Zn) in black soil are crucial to ensure high-quality production of agriculture, and hyperspectral technology is an effective approach to achieve a rapid estimation of these soil compositions. Eighty black soil samples were collected in Nehe city, Heilongjiang province, China. With indoor spectral data, the correlation between six spectral reflectance, which

In many areas of the globe, the installation of artificial drains on naturally poorly drained soils is a necessary part of farm management. Identifying the location of artificially drained areas is an important step in achieving environmentally sustainable agricultural production. However, in many regions, data on the presence or the distribution of artificial drainage systems are rare. We outline

Recently, many convolutional neural networks (CNN) have been successfully employed in bitemporal synthetic aperture radar (SAR) image change detection. However, most of the existing networks are too heavy and occupy a large volume of memory for storage and calculation. Motivated by this, we propose a lightweight neural network to reduce the computational and spatial complexity and facilitate the change

In conventional inverse synthetic aperture radar (ISAR) imaging of complex moving aircrafts, a micro-Doppler effect is generated because of the high-speed rotating parts such as propellers. The rotational parts introduce a sinusoidal frequency-modulated (SFM) signal in the radar echoes, which can significantly affect the quality of the ISAR imaging. To solve this problem, the Doppler frequency forms

Using satellite data to study small water bodies (SWB) and medium-sized water bodies (MSWB) is extremely useful for understanding their status, how to conserve them as water reservoirs, and their vulnerability to climate variability. The images studied in our work correspond to different-sized lagoons located in areas with high and low topography in a tropical region of Chiapas, Mexico. Our research

Following the objectives of the mission, Sentinel-2 (S2) makes a significant contribution to land monitoring, climate change, emergency management and security, and related problems. To increase the spatial resolution, researchers have been challenged to obtain a synthesized panchromatic band with a fine resolution for all S2 bands. The capacity to collect satellite imagery with a short revisit time

Spectral variability is one of the most limiting factors in hyperspectral unmixing, so it is important to further study the characteristics of spectral variability to improve the accuracy of unmixing. After conducting simulations under varying irradiation conditions, a linear mixed model combining endmember and band is proposed by introducing a band scaling factor to the endmember scaled spectrum.

The heavy-tailed properties of modulation transfer function (MTF) typically introduce noise and an aliasing effect in the MTF compensation (MTFC) effort to improve the spatial quality of optical satellite images. These degradative effects compromise the image’s signal-to-noise ratio (SNR). Consequently, users must evaluate the relative importance of image sharpness versus SNR for their applications

Dimensionality reduction is an important step in processing hyperspectral images (HSIs) to overcome the dimensionality problem. Linear dimensionality reduction methods such as independent component analysis (ICA) and linear discriminant analysis (LDA) are commonly employed to reduce the dimensionality of HSI. These methods fail to capture nonlinear dependency in the HSI data, as data lies in the nonlinear

The projection of objects on the earth’s surface caused by the sunlight produces shadows. They are inevitable in high-spatial-resolution satellite remote sensing images and reduce the accuracy of change detection, land cover classification, target recognition, and many more applications. Dark-colored land covers in these satellite images, such as water bodies, road, and soil, appear with similar spectral

Graph embedding (GE) frameworks are used for extracting the discriminative features of hyperspectral images (HSIs). However, it is difficult to select a proper neighborhood size for graph construction. To overcome this difficulty, a semisupervised feature extraction (FE) method, called semisupervised collaborative representation graph embedding (SCRGE), is proposed. The proposed algorithm utilizes

In order to achieve accurate locations of multiple targets on an airborne optoelectronic platform, we propose a multitarget location system based on the digital elevation model and a multitarget location model based on the target vector. Through the target detection algorithm, the pixel coordinates of each target in the field of view can be obtained and the visual axis vector of each target is constructed

The resolution of an imaging system can be quantified using the modulation transfer function (MTF) derived from an image with an edge feature. For Earth-viewing satellites, imaging an appropriate terrestrial edge feature is difficult when optical blur from the atmosphere contaminates the MTF. With a sharp edge and no atmosphere, our Moon has provided researchers with an alternative by deriving the

The large focal plane arrays used in the new generation of the Geostationary Operational Environmental Satellites (GOES) advanced baseline imager (ABI) introduce new calibration challenges compared with the heritage GOES imagers. The increased number of detectors allows for increased spatial, spectral, and temporal performance, but as a trade-off, it has an increased risk of image striping. We detail

This guest editorial introduces the Special Section on Representation Learning and Big Data Analytics for Remote Sensing.

Deep learning techniques have been leveraged in numerous applications and across different data modalities over the past few decades, more recently in the domain of remotely sensed imagery. Given the complexity and depth of convolutional neural network (CNN) architectures, it is difficult to fully evaluate performance, optimize the hyperparameters, and provide robust solutions to a specific machine

NOAA has both geostationary (GEO) and polar orbiting (LEO) satellites that are procured through different vendors. For long-term environmental studies, it is essential to establish consistent radiometric calibration among satellite instruments irrespective of the satellite platforms. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP)

Image navigation and registration (INR) processing for the Geostationary Lightning Mapper (GLM) assigns geographic coordinates to lightning events using orbit and attitude telemetry and a geometric calibration that matches coastline features from the GLM background scene against a digital map. Required performance is expressed as an optical angle at the aperture of the instrument of 112 μrad (3σ),

Image navigation and registration (INR) for the GOES-R series advanced baseline imager (ABI) is performed in ground processing of the raw image data. A Kalman filter processes ABI star measurements to compute time-varying optical misalignment parameters that allow real-time alignment of image samples to the GOES-R Fixed Grid. For the system to meet INR requirements, the filter measurements noise covariance

An image navigation (NAV) and registration (INR) performance assessment tool set (IPATS) was developed to assess the US Geostationary Operational Environmental Satellite R-series (GOES-R) Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance. IPATS produces five INR metrics for level 1B ABI images: navigation, channel-to-channel registration, frame-to-frame registration

The Geostationary Lightning Mapper (GLM) is a new geostationary lightning detection and location instrument, developed for the current generation of Geostationary Operational Environmental Satellite-R Series (GOES-R, S, T, U) satellites. Detection efficiency (DE) and false alarm rate (FAR) are evaluations of what an instrument detects and what it misses, compared with other well-characterized measurements

Satellite remote sensing has wide applications in many fields. However, the quality of the observed images captured from the satellite sensors exhibits significant variances and most images are low resolution. Therefore, they adversely affect the system performance in a variety of real-world applications such as object recognition and analysis. In order to enhance the resolution of remote sensing images

A large amount of remote sensing data can be easily acquired due to the increase in the advances in sensor’s technologies. The sensors can generate high-dimensional data in a lower time producing problems related to big data such as management and organization. Since the acquired data is characterized by a large dimension and lack of structure, the information analysis becomes harder. Therefore, an

Stacked denoising autoencoder (SDAE) model has a strong feature learning ability and has shown great success in the classification of remote sensing images. However, built-up area (BUA) information is easily interfered with by broken rocks, bare land, and other features with similar spectral features. SDAEs are vulnerable to broken and similar features in the image. We propose a multiscale SDAE model

Remote sensing data are often adversely affected by noises due to atmospheric water absorption, transmission errors, sensor sensitivity, and saturation. Therefore, the performance of the classical principal component analysis (PCA) is negatively affected when dealing with such noisy data. To overcome this problem, a robust deflated PCA via multiple instance factorings (RDPCA-MIF) for dimension reduction