Assessment of Coarse-Resolution Land Cover Products Using CASI Hyperspectral Data in an Arid Zone in Northwestern China

Abstract

Abstract:The accuracy of different coarse-resolution land cover products is an important consideration for product users at the regional or global scale, and different evaluation methods inevitably result in discrepancies in accuracy for the same land cover product. The remote sensing community has responded to this increased interest by improving methodologies for more accurately evaluating the correctness of land cover information. In this study, a pixel-based hierarchical classification strategy followed by an object-based classification method was applied to compact airborne spectrographic imager (CASI) hyperspectral data in order to produce highly accurate, high spatial resolution classification reference data. Some aspects of the fuzzy/conventional evaluation of MODIS land cover (MODISLC) (500 m) and GlobCover (300 m) data based on sub-pixel class fractions derived from high spatial resolution reference data at different thematic resolutions are also discussed. Relationships between homogeneity and fuzzy accuracy for two land cover products were obtained at different thematic resolutions. Additionally, the influences on the relationship resulting from the thematic resolution were also studied, and these are reported in this paper. Attempts were made to establish fuzzy/conventional evaluation rules for fuzzy classes, and the different performances of the fuzzy and conventional evaluations for hard/fuzzy labels were compared. The adjusted GlobCover accuracy after theoretical removal of the effect caused by spatial resolution was calculated based on the relationship OPEN ACCESSRemote Sens. 2014, 6 2865 between homogeneity and accuracy; the result was a higher accuracy than for MODISLC at the same thematic resolution. In addition, the different performance characteristics of the relationships between homogeneity and adjusted GlobCover accuracy/MODISLC accuracy at different thematic resolutions were compared and analyzed over the area where the CASI transects were obtained.

Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

“…Rule (ii) helped to reduce 500 m pixel edge effects in situations where there were possible changes in the underlying land cover. Rule (iii) was introduced because the homogeneous pixels had a higher classification accuracy [34]. Rule (iv) helped to reduce the GEE cloud-based platform provides massive amounts of multi-source satellite data and high-performance computation service, making the computation between different satellite imagery a relatively fast and flexible process.…”

The Application of Hyperspectral Sensing Data for Seabed Classification in the Coastal Area of Korea

“…A CASI-1500 hyperspectral sensor consisting of an Instrument Control Unit (ICU), Sensor Head Unit (SHU), and monitor was employed for the hyperspectral surveying over study area for the seabed classification. The CASI-1500 system was integrated with a POSAV 410 Inertial Measurement Unit (IMU) that records aircraft motion (roll, pitch, and heading) using gyroscopes and accelerometers and that also records aircraft position using differential GPS [9].…”

“…The data from this flight was used to determine the linear and angular offsets between the CASI-1500 and the co-mounted sensors (POSAV 410 and GPS) using ITRES bundle adjustment software [8,10]. This allowed for measurements made by the IMU and GPS to be referenced to the co-mounted CASI SHU during post-processing, thereby increasing geometric accuracy in the final georeferenced imagery [9]. The installation and internal sensor offsets determined from this calibration site were applied in the geocorrection process to all imagery collected during the installation of the CASI system.…”

“…The first is to apply radiometric calibrations to convert the raw digital numbers of the imagery into radiance values. The second step involves processing the attitude and positional measurements from the airborne inertial system and GPS to create a navigation file, which is then used in the third and final general processing step, where the CASI data is georeferenced using UTM coordinates in the WGS84 datum [9,10]. During the surveying, extensive ground survey data collections in support of the hyperspectral data takes were performed.…”

Development of a High Resolution BRDF/Albedo Product by Fusing Airborne CASI Reflectance with MODIS Daily Reflectance in the Oasis Area of the Heihe River Basin, China

“…Preliminary validation of the corrected CASI reflectance showed that the absolute differences were 1.5% (relative difference 5.93%) in the visible range of the spectrum (400 nm-700 nm) and 2.5% (relative difference 7.89%) in the near infrared (700 nm-1055 nm), compared with the reflectance of a homogenous concrete surface measured synchronically in the ground campaign. Land cover types were mapped using the CASI BRF; this classification technique, detailed in the work of Wang et al [27], is based on the ratio vegetation index (RVI). The FEA contains four land cover types: cropland (mainly corn), manmade features, bare soil, and bushes, as shown in Figure 1.…”