HIS为:亮度(I )、色调(H)、饱和度(S);
强度表示光谱的整体亮度大小,对应于图像的空间分辨率;
传统的IHS图像融合方法基本思想是将IHS空间中的低分辨率亮度用高分辨率的图像的亮度成分所代替;
首先是正变换:
def IHS(data_low,data_high):
"""
基于IHS变换融合算法
输入:np.ndArray格式的三维数组
返回:可绘出图像的utf-8格式的三维数组
"""
A = [[1./3.,1./3.,1./3.],[-np.sqrt(2)/6.,-np.sqrt(2)/6.,2*np.sqrt(2)/6],[1./np.sqrt(2),-1./np.sqrt(2),0.]]
#RGB->IHS正变换矩阵
B = [[1.,-1./np.sqrt(2),1./np.sqrt(2)],[1.,-1./np.sqrt(2),-1./np.sqrt(2)],[1.,np.sqrt(2),0.]]
#IHS->RGB逆变换矩阵
A = np.matrix(A)
B = np.matrix(B)
band , w , h = data_high.shape
pixels = w * h
data_low = data_low.reshape(3,pixels)
data_high = data_high.reshape(3,pixels)
a1 = np.dot(A , np.matrix(data_high))#高分影像正变换
a2 = np.dot(A , np.matrix(data_low))#低分影像正变换
a2[0,:] = a1[0,:]#用高分影像第一波段替换低分影像第一波段
RGB = np.array(np.dot(B , a2))#融合影像逆变换
RGB = RGB.reshape((3,w,h))
min_val = np.min(RGB.ravel())
max_val = np.max(RGB.ravel())
RGB = np.uint8((RGB.astype(np.float) - min_val) / (max_val - min_val) * 255)
RGB = Image.fromarray(cv2.merge([RGB[0],RGB[1],RGB[2]]))
return RGB
def imresize(data_low,data_high):
"""
图像缩放函数
输入:np.ndArray格式的三维数组
返回:np.ndArray格式的三维数组
"""
band , col , row = data_high.shape
data = np.zeros(((band,col,row)))
for i in range(0,band):
data[i] = smi.imresize(data_low[i],(col,row))
return data
def gdal_open(path):
"""
读取图像函数
输入:图像路径
返回:np.ndArray格式的三维数组
"""
data = gdal.Open(path)
col = data.RasterXSize#读取图像长度
row = data.RasterYSize#读取图像宽度
data_array_r = data.GetRasterBand(1).ReadAsArray(0,0,col,row).astype(np.float)#读取图像第一波段并转换为数组
data_array_g = data.GetRasterBand(2).ReadAsArray(0,0,col,row).astype(np.float)#读取图像第二波段并转换为数组
data_array_b = data.GetRasterBand(3).ReadAsArray(0,0,col,row).astype(np.float)#读取图像第三波段并转换为数组
data_array = np.array((data_array_r,data_array_g,data_array_b))
return data_array
执行代码:
def main(path_low,path_high):
data_low = gdal_open(path_low)
data_high = gdal_open(path_high)
data_low = imresize(data_low,data_high)
RGB = IHS(data_low,data_high)
RGB.save("IHS.png",'png')
if __name__ == "__main__":
path_low = 'RGB.tif'
path_high = 'Band8.tif'
main(path_low,path_high)
高分全色波段:
RGB影像:
IHS融合结果: