吹爆！遥感高光谱分类(Python)

import matplotlib.pyplot as plt  
import numpy as np
from scipy.io import loadmat
import spectral
import cv2
import pandas as pd
from sklearn import preprocessing

print("OpenCV version:", cv2.__version__)
print("Spectral version:", spectral.__version__)

input_image = loadmat(r'C:\xxxxxxxxxxxxxxxxxxxxxxx/KSC.mat')['KSC']  #数据
output_image = loadmat(r'C:\xxxxxxxxxxxxxxxxxxxxxx/KSC_gt.mat')['KSC_gt']#标签

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()))


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)

cv2.imshow('1',output_image)  #没有实质性的作用，解决spectral.imshow闪退问题
cv2.waitKey(0)


# 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:
            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)  
data_D = preprocessing.StandardScaler().fit_transform(new_datawithlabel_array[:,:-1])
data_L = new_datawithlabel_array[:,-1]

new = np.column_stack((data_D,data_L))
new_ = pd.DataFrame(new)
new_.to_csv(r'C:xxxxxxxx/KSC.csv',header=False,index=False)# 将结果存档后续处理

四、模型训练

复制代码

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(r'C:xxxxxxxxxxxxx/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")

五、模型推理

复制代码

import matplotlib.pyplot as plt  
import numpy as np
from scipy.io import loadmat
import spectral
import joblib
from sklearn import metrics
import cv2

# KSC
input_image = loadmat(r'C:\xxxxxxxxxxx/KSC.mat')['KSC']
output_image = loadmat(r'C:\xxxxxxxxxx/KSC_gt.mat')['KSC_gt']


testdata = np.genfromtxt(r'C:\xxxxxxxx/KSC.csv',delimiter=',')
data_test = testdata[:,:-1]
label_test = testdata[:,-1]

clf = joblib.load("KSC_MODEL.m")

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

print (accuracy) # 97.1022836308


# 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 
            

# 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))

cv2.imshow('1',output_image)
cv2.waitKey(0)

六、踩坑记录

（1）问题描述：spectral.imshow(img)时，图像一闪而过 ，并且spectral好像没有类似CV2的waitKey方法。所以无法暂停。

C:\Users\admin\AppData\Roaming\Python\Python38\site-packages\spectral\graphics\spypylab.py:796: UserWarning: Failed to create RectangleSelector object. Interactive pixel class labeling will be unavailable.

warnings.warn(msg)

解决方法：借助CV2的waitKey

在ground_truth = spectral.imshow(classes = output_image.astype(int),figsize =(9,9),colors=ksc_color)下加入cv图像显示

cv2.imshow('1',output_image)

cv2.waitKey(0)

（2）问题描述：AttributeError: module 'spectral' has no attribute 'preprocessing'

解决方法:

导入该模块

from sklearn import preprocessing

（3）问题描述：AttributeError: 'DataFrame' object has no attribute 'as_matrix'

解决方法：as_matrix()属性已被淘汰，所以DataFrame对象没有as_matrix属性

解决方法：将 as_matrix() 改为 values

示例如下：

将：

data = data.as_matrix()

改为：

data = data.values