An Efficient Segmentation Method for Remote Sensing Images

Abstract

本論文提出一個基於類神經網路的彩色遙測之影像分割法。我們提出使用自組織映射圖（Kohonen self-organizing map）來萃取影像中主要的特徵，接著利用所得的特徵進行無監督式影像的分類（unsupervised image segmentation）。 在傳統的自組織映射圖，輸入層通常採用像素（pixel）本身的數值，並未考慮到影像周遭的像素，所以在森身遙測影像分割上並不能得到滿意的結果。事實上在自然影像中，像素本身與周遭像素都具有很大的相依性，於是在此論文中，我們提出一個修正的自組織映射圖演算法，我們在輸入層加入空間特徵（ spatial features ），包含平均值濾波器（mean-filter）、中值濾波器（medium-filter）與離散餘弦變換（discrete cosine transform）之係數等。此外我們並且給予每個神經元一個權重值，使其針對不同的輸入，產生對應的權重值，以達到針對不同類型的輸入，均可對應至正確的輸出位置。經過訓練過後的類神經網路，我們提出一有效的後處理的步驟來將具有相同類型的輸出神經元，結合成同一種輸出，並且利用簡單的濾波器來將孤立點移除。我們應用所提演算法來處理森林遙測影像的分割。在實驗中，我們把將不同的樹種視為不同的材質，針對材質的特型，給予不同的特徵，以達到分類的目的。實驗結果顯示所提的方法可以有效的對於彩色影像、遙測影像以及森林影像進行分類。

In this paper, an efficient segmentation method based on neural network is proposed for the color remote sensing images of forest. It is facilitated by Kohonen self-organizing map (SOM) network, and it performs the unsupervised segmentation.The images of different of tree species usually have the similar color distribution, and the differences between them are textures. The traditional SOM usually obtains a poor result in the segmentation of forest images, since it uses only the intensities of R, G, and B channels, it does not consider the relationship existed in the neighborhoods of pixels. However, in practice, the pixels in natural images usually have strong correlation with their neighborhoods. Therefore, we propose a modified self-organizing map network in this paper, it uses the additional spatial features in the input layer, such as the coefficients of discrete cosine transform. In this way, we consider both pixels themselves and the correlation information with their neighborhoods at the same time. We also add a new weighting function for each neuron, which can help each neuron to map to a suitable output neuron. Finally, we use the noise-filter to improve segmentation quality at the post-processing stage. Experimental results show that the proposed method can separate successfully the different color texture in the remote sensing images of forest.