We investigated a method for noise removal on Landsat-8 OLI timeseries using CBERS-4 MUX data to improve crop classification. An algorithm was built to look to the nearest MUX image for each Landsat image, based on user defined time span. The algorithm checks for cloud contaminated pixels on the Landsat time series using Fmask and replaces them with CBERS-4 MUX to build the integrated time seri...

The Landsat 8 spacecraft was launched on 11 February 2013 carrying two imaging payloads: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The TIRS instrument employs a refractive telescope design that is opaque to visible wavelengths making prelaunch geometric characterization challenging. TIRS geometric calibration thus relied heavily on on-orbit measurements. Since th...

Thin cirrus clouds frequently contaminate images acquired with either Landsat 7 ETM+ or the earlier generation of Landsat series satellite instruments. The situation has changed since the launch of the Landsat 8 Operational Land Imager (OLI) into space in 2013. OLI implemented a cirrus detecting channel (Band 9) centered within a strong atmospheric water vapor absorption band near 1.375 μm with...

An image dataset from the Landsat OLI spaceborne sensor is compared with the Landsat TM in order to evaluate the excellence of the new imagery in urban landcover classification. Widely known pixel-based and object-based image analysis methods have been implemented in this work like Maximum Likelihood, Support Vector Machine, k-Nearest Neighbor, Feature Analyst and Sub-pixel. Classification resu...

The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic radiometric correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates...

Conventional methods for Aerosol Optical Depth (AOD) retrieval are limited to areas with low reflectance such as water or vegetated areas because the satellite signals from the aerosols in these areas are more obvious than those in areas with higher reflectance such as urban and sandy areas. Land Surface Reflectance (LSR) is the key parameter that must be estimated accurately. Most current meth...

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data,...

Land Surface Temperature (LST) can be calculated through a thermal infrared remote sensing data. It can record the apparent temperature of the Earth’s surface by measuring the radiant energy exiting its surface. With the advent of satellite images and digital image processing software, now it is possible to calculate LST. In this study, LST for Devikulam taluk was derived using Single Window al...