141 lines
3.6 KiB
Go
141 lines
3.6 KiB
Go
package rrc
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import (
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"os"
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log "github.com/sirupsen/logrus"
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"gocv.io/x/gocv"
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)
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// Relative Radiation Correction
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// 采用在轨统计定标方法。利用卫星在轨后获取的常规影像数据，统计每个探元出现的灰度频次。
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const (
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PANCameraProbeNum = 9344 // 全色探元数
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MSSCameraProbeNum = 2336 // 多光谱探元数
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MaxGrayLevel = 65536
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)
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type RRC struct {
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PANDataSet []string
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MSSDataSet []string
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Histograms [5]BandHistogram
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}
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type BandHistogram struct {
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width int // 探元数
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N_i []int // N_i 探元像素总数 PAN 0-9343 MSS 0-2335
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n_ik [][]int // n_ik 第i探元灰度等级为k的像素数统计 PAN 9343 x 65536 MSS 2335 x 65536
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p_ik [][]float64 // p_ik = n_ik / N_i 探元灰度概率密度 PAN 9343 x 65536 MSS 2335 x 65536
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m_l []int64 // 具有灰度等级l的像素总数 l = 0-65535
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M int64 // 参与直方图统计的总像素数
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P_l []float64 // P_l = m_l/M 所有探元的期望直方图灰度等级为l的概率密度
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S_ik [][]float64 // S_ik = sum(p_ij),j=0..k 第i个探元直方图灰度等级k的累积概率密度
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V_l []float64 // V_l = sum(P_j),j=0..l // 期望直方图灰度级l对应的累积概率密度
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Tij [][]float64 // 第i个像元的j灰度等级对应的新的灰度值，用于修正图像
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}
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func (hist *BandHistogram) init(width int) {
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hist.width = width
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hist.M = 0
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hist.N_i = make([]int, width)
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hist.n_ik = make([][]int, width)
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hist.p_ik = make([][]float64, width)
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hist.m_l = make([]int64, MaxGrayLevel)
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hist.P_l = make([]float64, MaxGrayLevel)
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hist.S_ik = make([][]float64, width)
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hist.V_l = make([]float64, MaxGrayLevel)
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hist.Tij = make([][]float64, width)
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for i := 0; i < width; i++ {
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hist.n_ik[i] = make([]int, MaxGrayLevel)
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hist.p_ik[i] = make([]float64, MaxGrayLevel)
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hist.S_ik[i] = make([]float64, MaxGrayLevel)
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hist.Tij[i] = make([]float64, MaxGrayLevel)
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}
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}
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// 统计探元灰度的累积概率密度
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func (rrc *RRC) StatisticalPAN(l0 string) error {
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data, err := os.ReadFile(l0)
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if err != nil {
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return err
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}
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height := len(data) / (PANCameraProbeNum * 2)
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img, err := gocv.NewMatFromBytes(height, PANCameraProbeNum, gocv.MatTypeCV16U, data)
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if err != nil {
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log.Error("Error creating Mat from bytes: ", err)
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return err
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}
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hist := BandHistogram{}
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hist.init(PANCameraProbeNum)
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rrc.statistical(img, &hist)
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rrc.compute(&hist)
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rrc.output(&hist, "pan_gray_table.tif")
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return nil
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}
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func (rrc *RRC) statistical(img gocv.Mat, hist *BandHistogram) error {
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hist.M += int64(img.Rows() * img.Cols())
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for i := 0; i < hist.width; i++ {
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hist.N_i[i] += img.Rows()
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}
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for y := 0; y < img.Rows(); y++ {
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for x := 0; x < img.Cols(); x++ {
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gray := uint16(img.GetShortAt(x, y))
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hist.n_ik[x][gray]++
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}
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}
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for gray := 0; gray < MaxGrayLevel; gray++ {
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for i := 0; i < hist.width; i++ {
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hist.m_l[gray] += int64(hist.n_ik[i][gray])
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}
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}
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return nil
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}
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func (rrc *RRC) compute(hist *BandHistogram) error {
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width := len(hist.N_i)
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for i := 0; i < width; i++ {
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for k := 0; k < MaxGrayLevel; k++ {
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hist.p_ik[i][k] = float64(hist.n_ik[i][k]) / float64(hist.N_i[i])
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}
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}
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for gray := 0; gray < MaxGrayLevel; gray++ {
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hist.P_l[gray] = float64(hist.m_l[gray]) / float64(hist.M)
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}
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for i := 0; i < width; i++ {
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for k := 0; k < MaxGrayLevel; k++ {
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hist.S_ik[i][k] = 0
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for j := 0; j <= k; j++ {
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hist.S_ik[i][k] += hist.p_ik[i][j]
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}
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}
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}
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for gray := 0; gray < MaxGrayLevel; gray++ {
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hist.V_l[gray] = 0
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for j := 0; j <= gray; j++ {
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hist.V_l[gray] += hist.P_l[j]
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}
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}
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return nil
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}
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func (rrc *RRC) output(hist *BandHistogram, referenceTIF string) error {
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return nil
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}
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