高光谱图像分类python代码

参考高光谱图像分类python代码

我建立了两个python文件:
1.data.py (负责加载.mat文件,将它转化为,csv文件)
2.train and classify.py (训练并观察分类效果)

下面分别是两段代码(对前面的引用代码做了轻微改动)
1.data.py

import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import spectral
from sklearn import preprocessing
from PIL import Image as image

# # 获取mat格式的数据,loadmat输出的是dict,所以需要进行定位
input_image = loadmat('D:\Desktop\Machine Learing\code\KSC\data\KSC.mat')['KSC']
output_image = loadmat('D:\Desktop\Machine Learing\code\KSC\data\KSC_gt.mat')['KSC_gt']

# # input_image.shape#:(610, 340, 103)
# # output_image.shape#:(610, 340)
# # np.unique(output_image)  # array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)
# 统计每类样本所含个数
dict_k = {
     }
for i in range(output_image.shape[0]):
    for j in range(output_image.shape[1]):
        # if output_image[i][j] in [m for m in range(1,17)]:
        if output_image[i][j] in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]:
            if output_image[i][j] not in dict_k:
                dict_k[output_image[i][j]] = 0
            dict_k[output_image[i][j]] += 1

print(dict_k)
print(reduce(lambda x, y: x + y, dict_k.values()))


# 展示地物
ground_truth = spectral.imshow(classes = output_image.astype(int),figsize =(9,9))

ksc_color =np.array([[255,255,255],
     [184,40,99],
     [74,77,145],
     [35,102,193],
     [238,110,105],
     [117,249,76],
     [114,251,253],
     [126,196,59],
     [234,65,247],
     [141,79,77],
     [183,40,99],
     [0,39,245],
     [90,196,111],
        ])

ground_truth = spectral.imshow(classes = output_image.astype(int),figsize =(9,9),colors=ksc_color)



# 除掉 0 这个非分类的类,把所有需要分类的元素提取出来
need_label = np.zeros([output_image.shape[0], output_image.shape[1]])
for i in range(output_image.shape[0]):
    for j in range(output_image.shape[1]):
        if output_image[i][j] != 0:
            # if output_image[i][j] in [1,2,3,4,5,6,7,8,9]:
            need_label[i][j] = output_image[i][j]

new_datawithlabel_list = []
for i in range(output_image.shape[0]):
    for j in range(output_image.shape[1]):
        if need_label[i][j] != 0:
            c2l = list(input_image[i][j])
            c2l.append(need_label[i][j])
            new_datawithlabel_list.append(c2l)

new_datawithlabel_array = np.array(
    new_datawithlabel_list)  # new_datawithlabel_array.shape (5211,177),包含了数据维度和标签维度,数据176维度,也就是176个波段,最后177列是标签维
data_D = preprocessing.StandardScaler().fit_transform(new_datawithlabel_array[:,:-1])
#data_D = preprocessing.MinMaxScaler().fit_transform(new_datawithlabel_array[:,:-1])
data_L = new_datawithlabel_array[:,-1]

# 将结果存档后续处理
import pandas as pd
new = np.column_stack((data_D,data_L))
new_ = pd.DataFrame(new)
new_.to_csv('D:\Desktop\Machine Learing\code\KSC\data\KSC.csv',header=False,index=False)

2.train and classify.py

import joblib
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.svm import SVC
from sklearn import metrics
from sklearn import preprocessing
import pandas as pd


# 导入数据集切割训练与测试数据

data = pd.read_csv('D:\Desktop\Machine Learing\code\KSC\data\KSC.csv',header=None)
data = data.values
data_D = data[:,:-1]
data_L = data[:,-1]
data_train, data_test, label_train, label_test = train_test_split(data_D,data_L,test_size=0.5)


# 模型训练与拟合
clf = SVC(kernel='rbf',gamma=0.125,C=16)
clf.fit(data_train,label_train)
pred = clf.predict(data_test)
accuracy = metrics.accuracy_score(label_test, pred)*100
print
accuracy


# 存储结果学习模型,方便之后的调用
joblib.dump(clf, "KSC_MODEL.m")

# mat文件的导入
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import spectral

# KSC
input_image = loadmat('D:\Desktop\Machine Learing\code\KSC\data\KSC.mat')['KSC']
output_image = loadmat('D:\Desktop\Machine Learing\code\KSC\data\KSC_gt.mat')['KSC_gt']

testdata = np.genfromtxt('D:\Desktop\Machine Learing\code\KSC\data\KSC.csv', delimiter=',')
data_test = testdata[:, :-1]
label_test = testdata[:, -1]

# /Users/mrlevo/Desktop/CBD_HC_MCLU_MODEL.m
clf = joblib.load("KSC_MODEL.m")

predict_label = clf.predict(data_test)
accuracy = metrics.accuracy_score(label_test, predict_label) * 100

print(accuracy) 

# 将预测的结果匹配到图像中
new_show = np.zeros((output_image.shape[0], output_image.shape[1]))
k = 0
for i in range(output_image.shape[0]):
    for j in range(output_image.shape[1]):
        if output_image[i][j] != 0:
            new_show[i][j] = predict_label[k]
            k += 1

        # print new_show.shape

# 展示地物
ground_truth = spectral.imshow(classes=output_image.astype(int), figsize=(9, 9))
ground_predict = spectral.imshow(classes=new_show.astype(int), figsize=(9, 9))

使用的是KSC数据集
附上下载地址高光谱数据集下载

结果展示
高光谱图像分类python代码_第1张图片
高光谱图像分类python代码_第2张图片上图为原始图片,下图是分类后图片,可以观察到分类效果比较好。

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