Shadow Detection in Hyperspectral Images Acquired by Uav

Abstract

Commission III, WG III/4 KEY WORDS: Shadow detection, UAV, hyperspectral image, high spatial and spectral resolution image, spectral signatures, cloud shadow in agricultural fields. ABSTRACT:Shadows are common in any kind of remote sensing images. Unmanned Aerial Vehicle -UAV with a light camera attached can acquire images illuminated either by direct sunlight or by diffuse light under clouds. Indeed, areas with pixels shaded by clouds must be detected and labelled in order to use this additional information for image analysis. Classification of health and diseased plants in permanent culture as the orange plantation field can present some errors due to tree cast shadow. So, hyperspectral or multispectral image classification can be improved by previous shadow detection. Some FPI hyperspectral camera, designed for agricultural applications is limited in the spectral range between 500 to 900 nm. Wavelengths in the region of blue light and in the SWIR spectral region have physical properties that enable the enhancement of shaded regions in the images. In this work some combinations of different spectral bands were evaluated in order to specify those suitable to detect shadows in agricultural field images. In this sense, considering that vegetation and soil are the two main kind of coverage in an agricultural field, we hypothesized that wavelengths near blue light and the longest near infrared available in the camera range are good choices. In both spectral regions soil and vegetation targets have small spectral differences which contribute to enhance the differences between shaded and illuminated regions in the image. Hyperspectral images acquired with a FPI hyperspectral camera onboard a UAV over a plantation of oranges were used to evaluate these spectral bands. The results showed that the wavelengths of aproximatelly 510 nm and 840 nm available in the FPI camera are the best to detect any type of shadows in the agricultural fields.

Color-based shadow detection method in aerial images

“…There is limited labeled shadow data in aerial images as it is expensive to collect and label. However, many shadow detection methods using aerial images have been discussed recently [5], [6], [8]- [13]. Arévalo et al [5] used c1c2c3 color space to implement a region growing procedure on the c3 band to detect shadow in high-resolution satellite images.…”

A Radiative Transfer Model-Based Multi-Layered Regression Learning to Estimate Shadow Map in Hyperspectral Images

“…ρ d is the diffuse reflection constant, n determines the angular divergence of the lobe, and ρ s determines the peak value or "strength" of the lobe [23]. Figure 1 depicts BRDF distribution for different values of ρ d , ρ s , and n. Lambertian is a special case where ρ s = n = 0, leaving only the first term of Equation (7). In Figure 1, the second row shows BRDF of some real materials which approximate those in Figure 1.…”

“…Please note that this equation is only for the kernel region, which has the same reflectance ρ and has shadow and non-shadow parameters instead of global and local. Algorithm 1 Gradient-descent algorithm for global/kernel-based search 1: Let HSI Radiance Image be "L" with "s" samples and "b" bands, and its reflectance estimated by Run QUAC to find reflectance R 2: Assign outputs L w and L b as two zeros vector of "b" 3: Let stepSize be 0.01 with a decay of 0.995 4: while (∆L w ≤ 1 ×10 −10 and ∆L b ≤ 1 ×10 −10 ) do 5: Select 2 pixels at random 6: Estimate δL w and δL b for selected pixels by ELM equation 7:…”

“…Over the years, various methods of shadow detection proposed are object-based shadow detection methods which classify clouds, their shadows, and non-shadowed regions by applying image segmentation at different bandwidth images of HSI imagery, e.g., [3,4], and color invariance-based shadow detection methods create RGB in invariant color space and exploit it for classification as in [5]. Some algorithms use band indices to detect shadows in an HSI image [6,7]. Another class of algorithms require an a priori Digital Surface Model (DSM) [8] or Terrestrial Laser Scanning data together with the HSI image to find shadows cast from scene geometry [9].…”

“…Effect of filtering on a given pixel. Gradient-descent algorithm to estimate h(x, t) 1: For an n-dimensional input 't' and 'M' be the maximum number of iterations 2: Let the initial value of 'x' be mean(t)3: Let output h x be h(x, t)4: Let stepSize be 0.01 with a decay of 0.9955: for i = 1 to M do6: h d = h((x-stepSize), t)7:…”