Illustration of the installation method of heterogeneous photovoltaic panels

How to extract PV panel area from crystalline silicon photovoltaic modules?

Both studies demonstrated that accurate PV panels area can be extracted using red, green, and blue band images. Therefore, we used RGB band information to extract PV panel information. The core part of crystalline silicon photovoltaic modules is the solar cell, which mostly appears in a deep blue color to enhance the absorption of sunlight [ 37 ].

Why are PV panel extraction results poor?

The implementation of existing methods often struggles with complex background interference and confusion between the background and the PV panels. As a result, the completeness and edge clarity of PV panel extraction results are compromised. Moreover, most previous studies have overlooked the unique color characteristics of PV panels.

Do PV panels have homogeneous texture?

Projecting the visual feature vector of every panel into a 3-dimension space, we found that the feature vectors are highly aggregated without any gap, as shown in Fig. 11 (a). The results conclude that PV panels show a high degree of texture consistency, i.e., they have homogeneous texture.

Can pkgpvn extract photovoltaic panels from high-resolution optical remote sensing images?

Moreover, most previous studies have overlooked the unique color characteristics of PV panels. To alleviate these deficiencies and limitations, a method for extracting photovoltaic panels from high-resolution optical remote sensing images guided by prior knowledge (PKGPVN) is proposed.

What are the different types of PV panels?

( a) Concentrated PV panels in terraced fields; ( b) discrete PV panels in grasslands; ( c) discrete PV panels in residential areas; ( d) concentrated PV panels in grasslands; ( e) discrete PV panels in terraced fields; ( f) concentrated PV panels in drylands; ( g) concentrated PV panels in farmlands; ( h) discrete PV panels in desert.

How to evaluate PV panel extraction ability of PVI?

In order to evaluate the PV panel extraction ability of PVI more objectively and clearly, first, we calculated the PVI of all the images in the PVP dataset. Then, we transformed the PVI images into binary images using the Otsu [ 50] method. The evaluation metrics show that the mean values of IoU and F1 are 57.64% and 68.49%.