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

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。