In recent years, the use of unmanned aerial vehicles (UAVs) to obtain high-resolution multispectral images has received increasing attention. When using reflectance data for quantitative remote sensing, radiometric calibration is needed that converts digital number to reflectance. Empirical linear radiometric calibration models with large calibration sites are feasible and convenient for remote sensing satellite wide-band sensors, but problems may exist with using this method directly in a UAV high-resolution remote sensing application. In this paper, we proposed a high precision, low cost subband empirical line (SEL) radiometric calibration method with two targets composed of black and white nonwoven fabric. To verify the results of the calibration model, analyses were performed by examining the influence of function selection, calibration materials, and the number of calibration panels. Next, the method was applied to natural field targets. The results show that the black and white panels have good spectral, Lambertian, and contrast characteristics; the power equation was more suitable for the visible bands, whereas the linear equation was more suitable for red-edge and near-infrared bands. The average absolute errors between the predicted reflectance and the measured reflectance were at around 10%. The proposed method had smaller errors and higher accuracy than the other function method, and a high correlation between the predicted and measured reflectance of the maize samples was observed. The SEL method developed in this study provides a reference for studying the radiometric calibration of other multispectral sensors.

Polarimetric SAR (POLSAR) provides a rich set of information about objects on land surfaces. However, not all information works on land surface classification. This study proposes a new, integrated algorithm for optimal urban classification using POLSAR data. Both polarimetric decomposition and time-frequency (TF) decomposition were used to mine the hidden information of objects in POLSAR data, which was then applied in the C5.0 decision tree algorithm for optimal feature selection and classification. Using a NASA/JPL AIRSAR POLSAR scene as an example, the overall accuracy and kappa coefficient of the proposed method reached 91.17% and 0.90 in the L-band, much higher than those achieved by the commonly applied Wishart supervised classification that were 45.65% and 0.41. Meantime, the overall accuracy of the proposed method performed well in both C- and P-bands. Polarimetric decomposition and TF decomposition all proved useful in the process. TF information played a great role in delineation between urban/built-up areas and vegetation. Three polarimetric features (entropy, Shannon entropy, T11 Coherency Matrix element) and one TF feature (HH intensity of coherence) were found most helpful in urban areas classification. This study indicates that the integrated use of polarimetric decomposition and TF decomposition of POLSAR data may provide improved feature extraction in heterogeneous urban areas.

A novel approach is proposed for classifying the polarimetric SAR (PolSAR) data by integrating polarimetric decomposition, sub-aperture decomposition and decision tree algorithm. It is composed of three key steps: sub-aperture decomposition, feature extraction and combination, and decision tree classification. Feature extraction and combination is the main contribution to the innovation of the proposed method. Firstly, the full-resolution PolSAR image and its two sub-aperture images are decomposed to obtain the scattering entropy, average scattering angle and anisotropy, respectively. Then, the difference information between the two sub-aperture images are extracted, and combined with the target decomposition features from full-resolution images to form the classification feature set. Finally, C5.0 decision tree algorithm is used to classify the PolSAR image. A comparison between the proposed method and commonly-used Wishart supervised classification was made to verify the improvement of the proposed method on the classification. The overall accuracy using the proposed method was 88.39%, much higher than that using the Wishart supervised classification, which exhibited an overall accuracy of 69.82%. The Kappa Coefficient was 0.83, whereas that using the Wishart supervised classification was 0.56. The results indicate that the proposed method performed better than Wishart supervised classification for landscape classification in urban area using PolSAR data. Further investigation was carried out on the contribution of difference information to PolSAR classification. It was found that the sub-aperture decomposition improved the classification accuracy of forest, buildings and grassland effectively in high-density urban area. Compared with support vector machine (SVM) and QUEST classifier, C5.0 decision tree classifier performs more efficient in time consumption, feature selection and construction of decision rule.

The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. This paper develops an improved approach for building detection by utilizing Two-Dimensional Time-Frequency (2-D TF) decomposition. This method performs outstandingly in distinguishing between man-made and natural targets based on the isotropic behaviors, frequency-sensitive responses, and scattering mechanisms of objects. The proposed method can preserve the spatial resolution and exploit the advantages of TF decomposition; specifically, the exact outlines of buildings can be effectively located, and more types of features (e.g., flat roofs, roads, and walls that are oblique to the radar illumination) can be distinguished from forests in complex built-up areas by 2-D TF decomposition. The coarser-resolution subaperture images that are produced in the azimuth direction, which correspond to different looking angles, are beneficial for detecting man-made structures with main scattering centers oriented at oblique angles with respect to the radar illumination. In the range direction, the obtained subaperture images, which correspond to various observation frequencies, can be helpful in distinguishing flat roofs and roads from forests. This method was successfully implemented to analyze both NASA/JPL L-band AIRSAR and L-band EMISAR data sets. The building detection results of the proposed method exhibit a significant improvement over those of other methods and reach an overall accuracy over 80%, with approximately 20% higher than the accuracies of K-means clustering and the entropy/alpha-Wishart classifier and approximately 10% higher than the accuracy of the support vector machine method. Moreover, building details can be precisely detected, obliquely oriented buildings can be identified, and the distinction between buildings and forests is significantly improved, as both visually and statistically indicated. This method is highly adaptable and has substantial application value.

The Polarization Orientation Compensation (POC) method has been proposed to resolve overestimation of the volume component induced by topographical slopes and oblique building walls during model-based decompositions. The approach, however, can result in an underestimation of volume scattering within forests. This article proposes a novel method to improve the Yamaguchi 4-component (hereafter referred to as the Yamaguchi4) decomposition approach using POC. The scattering power of the decomposition components before and after POC is used to determine whether a pixel needs orientation compensation. The proposed method provides a number of advantages. First, the cross-polarized component, created by topographical slopes and man-made structures whose main scattering center is at an oblique direction with respect to radar illumination, is minimized. Oblique building blocks do not present the same volume as forests but, instead, result in double-bounce scattering. Second, this method resolves issues related to decreasing representations of vegetation according to the 4 scattering components when the volume-scattering power from forests is reduced. The new method was applied to currently available data, and positive results were achieved.